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BYOC"},"19":{"id":"/compute/bring-your-own-compute-3","page_id":"/compute/bring-your-own-compute","type":"heading","tags":[],"url":"/compute/bring-your-own-compute#next-steps","content":"Next Steps"},"20":{"id":"/compute/bring-your-own-compute-4","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute","content":"DGX Cloud Lepton allows you to seamlessly integrate your own compute infrastructure, providing enterprise-grade workload management capabilities."},"21":{"id":"/compute/bring-your-own-compute-5","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#what-is-bring-your-own-compute","content":"Bring Your Own Compute (BYOC) is a feature that enables you to use your existing hardware infrastructure with Lepton's platform. This approach offers several advantages:"},"22":{"id":"/compute/bring-your-own-compute-6","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#what-is-bring-your-own-compute","content":"Leverage your existing hardware investments"},"23":{"id":"/compute/bring-your-own-compute-7","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#what-is-bring-your-own-compute","content":"Maintain control over your physical infrastructure"},"24":{"id":"/compute/bring-your-own-compute-8","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#what-is-bring-your-own-compute","content":"Unify your compute resources under Lepton's management interface"},"25":{"id":"/compute/bring-your-own-compute-9","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#what-is-bring-your-own-compute","content":"Access Lepton's enterprise-grade orchestration capabilities"},"26":{"id":"/compute/bring-your-own-compute-10","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#getting-started-with-byoc","content":"To start using your own compute with Lepton, follow these steps:"},"27":{"id":"/compute/bring-your-own-compute-11","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#getting-started-with-byoc","content":"Review Requirements: Ensure your machines meet the BYOC Requirements for hardware, software, network, and storage configurations."},"28":{"id":"/compute/bring-your-own-compute-12","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#getting-started-with-byoc","content":"Create a Node Group: Before importing machines, create a node group to organize your compute resources."},"29":{"id":"/compute/bring-your-own-compute-13","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#getting-started-with-byoc","content":"Add Your Machines: Use either SSH or GPUd to add machines to your node group."},"30":{"id":"/compute/bring-your-own-compute-14","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#getting-started-with-byoc","content":"Manage Your Nodes: After importing, manage your BYOC nodes through the node group management interface."},"31":{"id":"/compute/bring-your-own-compute-15","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#next-steps","content":"Review BYOC Requirements"},"32":{"id":"/compute/bring-your-own-compute-16","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#next-steps","content":"Create BYOC Node Group"},"33":{"id":"/compute/bring-your-own-compute-17","page_id":"/compute/bring-your-own-compute","tags":[],"type":"text","url":"/compute/bring-your-own-compute#next-steps","content":"Import Your Machines"},"34":{"id":"/get-started/batch-job","page_id":"/get-started/batch-job","type":"page","content":"Batch Job","tags":[],"url":"/get-started/batch-job"},"35":{"id":"/get-started/batch-job-0","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job","content":"Learn about Batch Jobs on DGX Cloud Lepton."},"36":{"id":"/get-started/batch-job-1","page_id":"/get-started/batch-job","type":"heading","tags":[],"url":"/get-started/batch-job#create-batch-job","content":"Create Batch Job"},"37":{"id":"/get-started/batch-job-2","page_id":"/get-started/batch-job","type":"heading","tags":[],"url":"/get-started/batch-job#view-the-batch-job","content":"View the Batch Job"},"38":{"id":"/get-started/batch-job-3","page_id":"/get-started/batch-job","type":"heading","tags":[],"url":"/get-started/batch-job#next-steps","content":"Next Steps"},"39":{"id":"/get-started/batch-job-4","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job","content":"A Batch Job is a one-off task that runs to completion, such as training a model or processing data.\nDGX Cloud Lepton provides a simple way to run and manage batch jobs in the cloud."},"40":{"id":"/get-started/batch-job-5","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"Navigate to the create job page."},"41":{"id":"/get-started/batch-job-6","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"In the Resource section, select the node group and your desired resource shape.\nFor example, select H100-80GB-HBM3 x 1 from node group h100sxm-0, and leave the worker and priority settings at their default values."},"42":{"id":"/get-started/batch-job-7","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"In the Container section, use the default image. In the Run Command field, specify the following command to run in the container:"},"43":{"id":"/get-started/batch-job-8","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"By configuring the above settings, you can create a batch job that:"},"44":{"id":"/get-started/batch-job-9","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"Uses one H100 GPU from node group h100sxm-0"},"45":{"id":"/get-started/batch-job-10","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"Uses the default image and runs a simple counter that processes 10 items with a 2-second delay between each"},"46":{"id":"/get-started/batch-job-11","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"Completes after approximately 20 seconds and is archived after 3 days"},"47":{"id":"/get-started/batch-job-12","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#create-batch-job","content":"You need to have a node group with available nodes in your workspace first."},"48":{"id":"/get-started/batch-job-13","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#view-the-batch-job","content":"After the job is created, you can check the status and results on the details page."},"49":{"id":"/get-started/batch-job-14","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#view-the-batch-job","content":"Click Logs under the Replicas tab to see the logs for each replica. Because the worker count is one, there will be only one replica, and the logs should display the following:"},"50":{"id":"/get-started/batch-job-15","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#next-steps","content":"For more information about batch jobs, refer to the following:"},"51":{"id":"/get-started/batch-job-16","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#next-steps","content":"Configure your Batch Job"},"52":{"id":"/get-started/batch-job-17","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#next-steps","content":"Job templates"},"53":{"id":"/get-started/batch-job-18","page_id":"/get-started/batch-job","tags":[],"type":"text","url":"/get-started/batch-job#next-steps","content":"Predefined environment variables"},"54":{"id":"/get-started/dev-pod","page_id":"/get-started/dev-pod","type":"page","content":"Dev Pod","tags":[],"url":"/get-started/dev-pod"},"55":{"id":"/get-started/dev-pod-0","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod","content":"Learn how to create and use a development pod on DGX Cloud Lepton."},"56":{"id":"/get-started/dev-pod-1","page_id":"/get-started/dev-pod","type":"heading","tags":[],"url":"/get-started/dev-pod#create-a-dev-pod-from-a-container-image","content":"Create a Dev Pod from a Container Image"},"57":{"id":"/get-started/dev-pod-2","page_id":"/get-started/dev-pod","type":"heading","tags":[],"url":"/get-started/dev-pod#create-from-notebooks","content":"Create from Notebooks"},"58":{"id":"/get-started/dev-pod-3","page_id":"/get-started/dev-pod","type":"heading","tags":[],"url":"/get-started/dev-pod#access-via-web-terminal","content":"Access via Web Terminal"},"59":{"id":"/get-started/dev-pod-4","page_id":"/get-started/dev-pod","type":"heading","tags":[],"url":"/get-started/dev-pod#next-steps","content":"Next Steps"},"60":{"id":"/get-started/dev-pod-5","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod","content":"For AI application developers, having a convenient development environment is essential for building and testing applications."},"61":{"id":"/get-started/dev-pod-6","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod","content":"DGX Cloud Lepton provides a solution called Dev Pods—lightweight, container-based AI development environments."},"62":{"id":"/get-started/dev-pod-7","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod","content":"There are two ways to launch a Dev Pod: start from a blank container image or launch a Starter Kit, which contains a predefined notebook following various AI tasks and workflows."},"63":{"id":"/get-started/dev-pod-8","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#create-a-dev-pod-from-a-container-image","content":"Navigate to the create pod page on the dashboard, where you can configure and launch a Dev Pod using a custom container image."},"64":{"id":"/get-started/dev-pod-9","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#create-a-dev-pod-from-a-container-image","content":"Enter a name, select a node group and machine type in the Resource section, then click Create. The Dev Pod starts shortly."},"65":{"id":"/get-started/dev-pod-10","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#create-a-dev-pod-from-a-container-image","content":"You need to have a node group with available nodes in the workspace first."},"66":{"id":"/get-started/dev-pod-11","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#create-from-notebooks","content":"To launch a Dev Pod with a connected Jupyter session, go to the Starter Kits page to view curated notebooks. Select a notebook to open a read-only preview. To run it, click Create pod from this notebook at the top of the page and complete the Dev Pod settings."},"67":{"id":"/get-started/dev-pod-12","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#create-from-notebooks","content":"For more information on using Starter Kits, refer to the Starter Kits on Dev Pods guide."},"68":{"id":"/get-started/dev-pod-13","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#access-via-web-terminal","content":"After creation, visit the details page to check the Dev Pod's status, connection information, metrics, and more."},"69":{"id":"/get-started/dev-pod-14","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#access-via-web-terminal","content":"Switch to the Terminal tab. DGX Cloud Lepton automatically establishes a connection to the Dev Pod, allowing you to control it through a full web terminal."},"70":{"id":"/get-started/dev-pod-15","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"With these steps, you can launch a Dev Pod and start developing."},"71":{"id":"/get-started/dev-pod-16","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"Refer to the following guides to learn more about Dev Pods:"},"72":{"id":"/get-started/dev-pod-17","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"Configure your Dev Pod"},"73":{"id":"/get-started/dev-pod-18","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"Use SSH to access your Dev Pod"},"74":{"id":"/get-started/dev-pod-19","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"Enable JupyterLab in a Dev Pod"},"75":{"id":"/get-started/dev-pod-20","page_id":"/get-started/dev-pod","tags":[],"type":"text","url":"/get-started/dev-pod#next-steps","content":"Run NCCL test"},"76":{"id":"/get-started/endpoint","page_id":"/get-started/endpoint","type":"page","content":"Endpoint","tags":[],"url":"/get-started/endpoint"},"77":{"id":"/get-started/endpoint-0","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint","content":"Learn how to create and use endpoints on DGX Cloud Lepton for AI model deployment."},"78":{"id":"/get-started/endpoint-1","page_id":"/get-started/endpoint","type":"heading","tags":[],"url":"/get-started/endpoint#create-an-endpoint","content":"Create an Endpoint"},"79":{"id":"/get-started/endpoint-2","page_id":"/get-started/endpoint","type":"heading","tags":[],"url":"/get-started/endpoint#use-the-endpoint","content":"Use the Endpoint"},"80":{"id":"/get-started/endpoint-3","page_id":"/get-started/endpoint","type":"heading","tags":[],"url":"/get-started/endpoint#playground","content":"Playground"},"81":{"id":"/get-started/endpoint-4","page_id":"/get-started/endpoint","type":"heading","tags":[],"url":"/get-started/endpoint#api-request","content":"API Request"},"82":{"id":"/get-started/endpoint-5","page_id":"/get-started/endpoint","type":"heading","tags":[],"url":"/get-started/endpoint#next-steps","content":"Next Steps"},"83":{"id":"/get-started/endpoint-6","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint","content":"An Endpoint is a running instance of an AI model that exposes an HTTP server."},"84":{"id":"/get-started/endpoint-7","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint","content":"DGX Cloud Lepton lets you deploy AI models as endpoints, making them accessible via high-performance, scalable REST APIs."},"85":{"id":"/get-started/endpoint-8","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Navigate to the create LLM endpoint page."},"86":{"id":"/get-started/endpoint-9","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Select vLLM as the LLM engine, and load a model from Hugging Face in the Model section. In this case, we will use the nvidia/Nemotron-Research-Reasoning-Qwen-1.5B model."},"87":{"id":"/get-started/endpoint-10","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Then, in the Resource section, select the node group and your desired resource shape. In this case, use H100-80GB-HBM3 x 1 from node group h100."},"88":{"id":"/get-started/endpoint-11","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Click Create to deploy an endpoint that:"},"89":{"id":"/get-started/endpoint-12","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Uses one H100 GPU from node group h100"},"90":{"id":"/get-started/endpoint-13","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"Deploys the nvidia/Nemotron-Research-Reasoning-Qwen-1.5B model with vLLM"},"91":{"id":"/get-started/endpoint-14","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#create-an-endpoint","content":"You need to have a node group with available nodes in your workspace first."},"92":{"id":"/get-started/endpoint-15","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#use-the-endpoint","content":"By default, the endpoint is public and can be accessed by anyone with the URL. Refer to the endpoint configurations for managing endpoint access control."},"93":{"id":"/get-started/endpoint-16","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#playground","content":"After the endpoint is created, the endpoint details page shows a chat playground where you can interact with the deployed model."},"94":{"id":"/get-started/endpoint-17","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#api-request","content":"You can also use the endpoint URL to make API requests. Go to the API tab on the endpoint details page for details."},"95":{"id":"/get-started/endpoint-18","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#api-request","content":"For example, you can use the following command to list the available models in the endpoint."},"96":{"id":"/get-started/endpoint-19","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#next-steps","content":"For more information about endpoints, refer to the following:"},"97":{"id":"/get-started/endpoint-20","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#next-steps","content":"Endpoint configurations"},"98":{"id":"/get-started/endpoint-21","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#next-steps","content":"Create endpoints from NVIDIA NIM"},"99":{"id":"/get-started/endpoint-22","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#next-steps","content":"Create LLM endpoints"},"100":{"id":"/get-started/endpoint-23","page_id":"/get-started/endpoint","tags":[],"type":"text","url":"/get-started/endpoint#next-steps","content":"Create endpoints from container image"},"101":{"id":"/get-started/node-group","page_id":"/get-started/node-group","type":"page","content":"Node Group","tags":[],"url":"/get-started/node-group"},"102":{"id":"/get-started/node-group-0","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group","content":"Learn how to create and manage node groups on DGX Cloud Lepton."},"103":{"id":"/get-started/node-group-1","page_id":"/get-started/node-group","type":"heading","tags":[],"url":"/get-started/node-group#create-a-byoc-node-group","content":"Create a BYOC Node Group"},"104":{"id":"/get-started/node-group-2","page_id":"/get-started/node-group","type":"heading","tags":[],"url":"/get-started/node-group#use-a-node-group","content":"Use a Node Group"},"105":{"id":"/get-started/node-group-3","page_id":"/get-started/node-group","type":"heading","tags":[],"url":"/get-started/node-group#create-workloads","content":"Create Workloads"},"106":{"id":"/get-started/node-group-4","page_id":"/get-started/node-group","type":"heading","tags":[],"url":"/get-started/node-group#next-steps","content":"Next Steps"},"107":{"id":"/get-started/node-group-5","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group","content":"A node group is a dedicated set of nodes of a specific GPU type. Node groups let you group multiple nodes for a given workload. There are two types of node groups:"},"108":{"id":"/get-started/node-group-6","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group","content":"BYOC Node Group: Uses your own compute resources."},"109":{"id":"/get-started/node-group-7","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group","content":"Lepton Managed Node Group: All resources are managed by DGX Cloud Lepton."},"110":{"id":"/get-started/node-group-8","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group","content":"Lepton Managed Node Groups will be created by your Technical Account Manager(TAM) for you to use."},"111":{"id":"/get-started/node-group-9","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group#create-a-byoc-node-group","content":"Bring Your Own Compute (BYOC) allows you to bring your own compute resources to DGX Cloud Lepton. This is useful if you have existing resources you want to use with DGX Cloud Lepton.\nFollow the steps on the Create BYOC Node Group page to get started."},"112":{"id":"/get-started/node-group-10","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group#use-a-node-group","content":"Once the node group is available, you can view its details on the node group list page."},"113":{"id":"/get-started/node-group-11","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group#use-a-node-group","content":"By default, a new node group is empty. To add capacity, bring your own compute by clicking Add Machines under the Nodes tab."},"114":{"id":"/get-started/node-group-12","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group#create-workloads","content":"After adding machines to the node group, you can create workloads from the node group using available nodes.\nWhen creating a workload, select the node group in the Resource pane to view the GPU types available in that group."},"115":{"id":"/get-started/node-group-13","page_id":"/get-started/node-group","tags":[],"type":"text","url":"/get-started/node-group#next-steps","content":"Bring Your Own Compute"},"116":{"id":"/get-started/workspace","page_id":"/get-started/workspace","type":"page","content":"Workspace","tags":[],"url":"/get-started/workspace"},"117":{"id":"/get-started/workspace-0","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace","content":"Learn what a workspace is and how to create and manage a workspace on DGX Cloud Lepton."},"118":{"id":"/get-started/workspace-1","page_id":"/get-started/workspace","type":"heading","tags":[],"url":"/get-started/workspace#create-workspace","content":"Create Workspace"},"119":{"id":"/get-started/workspace-2","page_id":"/get-started/workspace","type":"heading","tags":[],"url":"/get-started/workspace#nvidia-cloud-account","content":"NVIDIA Cloud Account"},"120":{"id":"/get-started/workspace-3","page_id":"/get-started/workspace","type":"heading","tags":[],"url":"/get-started/workspace#activate-workspace","content":"Activate Workspace"},"121":{"id":"/get-started/workspace-4","page_id":"/get-started/workspace","type":"heading","tags":[],"url":"/get-started/workspace#external-sso-integration","content":"External SSO Integration"},"122":{"id":"/get-started/workspace-5","page_id":"/get-started/workspace","type":"heading","tags":[],"url":"/get-started/workspace#next-steps","content":"Next Steps"},"123":{"id":"/get-started/workspace-6","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace","content":"A workspace is a virtual environment that provides a set of tools and resources for managing your infrastructure and workloads."},"124":{"id":"/get-started/workspace-7","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#nvidia-cloud-account","content":"Before creating a workspace on DGX Cloud Lepton, you need a valid NVIDIA Cloud Account (NCA) and access to the DGX Cloud Lepton platform.\nAfter logging into DGX Cloud Lepton with your NCA, a workspace is generated automatically."},"125":{"id":"/get-started/workspace-8","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#activate-workspace","content":"Given that DGX Cloud Lepton is in early access, your workspace must be activated after it is created.\nA banner will appear indicating your workspace is not yet activated. Follow the instructions to apply for access on the NVIDIA Developer website."},"126":{"id":"/get-started/workspace-9","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#activate-workspace","content":"After you apply, NVIDIA will review your application and contact you."},"127":{"id":"/get-started/workspace-10","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#external-sso-integration","content":"DGX Cloud Lepton can integrate with an external SSO/IdP to centralize user authentication and manage access to NVIDIA cloud services. To get started, refer to NGC External SSO. Contact your NVIDIA TAM if you have questions."},"128":{"id":"/get-started/workspace-11","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"DGX Cloud Lepton provides many features to help you manage your workspace. Learn more:"},"129":{"id":"/get-started/workspace-12","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"Members"},"130":{"id":"/get-started/workspace-13","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"Token"},"131":{"id":"/get-started/workspace-14","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"Secrets"},"132":{"id":"/get-started/workspace-15","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"Registry"},"133":{"id":"/get-started/workspace-16","page_id":"/get-started/workspace","tags":[],"type":"text","url":"/get-started/workspace#next-steps","content":"Templates"},"134":{"id":"/reference/api","page_id":"/reference/api","type":"page","content":"Python SDK Reference","tags":[],"url":"/reference/api"},"135":{"id":"/reference/api-0","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api","content":"Tutorial on using the Python SDK to interact with DGX Cloud Lepton"},"136":{"id":"/reference/api-1","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#installation-and-authentication","content":"Installation and authentication"},"137":{"id":"/reference/api-2","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#validate-installation","content":"Validate installation"},"138":{"id":"/reference/api-3","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#basic-python-sdk-flow","content":"Basic Python SDK flow"},"139":{"id":"/reference/api-4","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#initialize-a-client","content":"Initialize a client"},"140":{"id":"/reference/api-5","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#define-the-task-to-perform","content":"Define the task to perform"},"141":{"id":"/reference/api-6","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#mounting-shared-storage","content":"Mounting shared storage"},"142":{"id":"/reference/api-7","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#authenticating-with-private-container-registries","content":"Authenticating with private container registries"},"143":{"id":"/reference/api-8","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#running-in-a-node-reservation","content":"Running in a node reservation"},"144":{"id":"/reference/api-9","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#execute-the-task","content":"Execute the task"},"145":{"id":"/reference/api-10","page_id":"/reference/api","type":"heading","tags":[],"url":"/reference/api#example-job-submission-via-sdk","content":"Example job submission via SDK"},"146":{"id":"/reference/api-11","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api","content":"DGX Cloud Lepton supports the REST API protocol and includes a Python SDK for interacting with workspaces. Common tasks include monitoring and launching batch jobs and endpoints. This document provides an overview of how to interact with the Python SDK for DGX Cloud Lepton."},"147":{"id":"/reference/api-12","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#installation-and-authentication","content":"First, install the Python SDK and authenticate with your workspace. Install the SDK with:"},"148":{"id":"/reference/api-13","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#installation-and-authentication","content":"Next, authenticate with your workspace:"},"149":{"id":"/reference/api-14","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#installation-and-authentication","content":"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."},"150":{"id":"/reference/api-15","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#installation-and-authentication","content":"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back into your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."},"151":{"id":"/reference/api-16","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#validate-installation","content":"After authentication, validate the installation by running:"},"152":{"id":"/reference/api-17","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#validate-installation","content":"This lists your available workspaces and should look similar to the following if authentication was successful:"},"153":{"id":"/reference/api-18","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#basic-python-sdk-flow","content":"Nearly all workflows using the Python SDK follow the same basic flow:"},"154":{"id":"/reference/api-19","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#basic-python-sdk-flow","content":"Initialize a client"},"155":{"id":"/reference/api-20","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#basic-python-sdk-flow","content":"Define the task to perform"},"156":{"id":"/reference/api-21","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#basic-python-sdk-flow","content":"Execute the task"},"157":{"id":"/reference/api-22","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#basic-python-sdk-flow","content":"The following sections break down these steps and provide a complete example."},"158":{"id":"/reference/api-23","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#initialize-a-client","content":"Initializing the client is straightforward—simply import the Lepton API module and instantiate the client:"},"159":{"id":"/reference/api-24","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#initialize-a-client","content":"The client variable can be reused for multiple tasks."},"160":{"id":"/reference/api-25","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Most tasks available to users on DGX Cloud Lepton are supported via the SDK. The following API resources are accessible:"},"161":{"id":"/reference/api-26","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Batch Jobs"},"162":{"id":"/reference/api-27","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Dev Pods"},"163":{"id":"/reference/api-28","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Endpoints"},"164":{"id":"/reference/api-29","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Events"},"165":{"id":"/reference/api-30","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Health Checks"},"166":{"id":"/reference/api-31","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Ingress"},"167":{"id":"/reference/api-32","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Logs"},"168":{"id":"/reference/api-33","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Monitoring"},"169":{"id":"/reference/api-34","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Node Groups"},"170":{"id":"/reference/api-35","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Queue"},"171":{"id":"/reference/api-36","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Readiness"},"172":{"id":"/reference/api-37","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Replicas"},"173":{"id":"/reference/api-38","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Secrets"},"174":{"id":"/reference/api-39","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Templates"},"175":{"id":"/reference/api-40","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Workspaces"},"176":{"id":"/reference/api-41","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Each of these resources has a specific template they expect for the API request. For example, the Batch Jobs API expects a job to have a leptonai.api.v1.types.job.LeptonJob type for submission. Similarly, Endpoints (also known as \"Deployments\" in the SDK), expect a leptonai.api.v1.types.deployment.LeptonDeployment object for submission, as do Dev Pods which are handled similarly to Endpoints by the backend. The list of API specs can be found here. Open the file for the specific task you need and review its specification."},"177":{"id":"/reference/api-42","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"All jobs require a Resource Shape to be specified. This tells the platform what resources should be allocated in the container, such as number and type of GPUs, CPU cores and memory, and local storage. To view available shapes, run lep node resource-shape in CLI version 0.26.4 or later. This returns a table of all available resource shapes in your node groups. The Shapes column shows the name of the shape. Use the desired name for the resource_shape field in the job specifications."},"178":{"id":"/reference/api-43","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"For a batch job, you need a LeptonJob object with a LeptonJobUserSpec. Review the LeptonJobUserSpec in the Python script for the list of settings which are required for launching a job. The following is a quick example of defining a batch job spec (this expands upon the previous code which instantiated the client):"},"179":{"id":"/reference/api-44","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"The example above does the following:"},"180":{"id":"/reference/api-45","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Imports all required modules"},"181":{"id":"/reference/api-46","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Finds the ID of the specified node group - Update the listed node group for your specific needs"},"182":{"id":"/reference/api-47","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Get the list of the node IDs for all nodes in your node group - this specifies which nodes the job can be scheduled on"},"183":{"id":"/reference/api-48","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Specify the job spec - this includes defining the resource shape, container, command, and number of workers"},"184":{"id":"/reference/api-49","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#define-the-task-to-perform","content":"Define the job by passing the job spec and giving it a name"},"185":{"id":"/reference/api-50","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"If shared storage is available in your node group, you can mount it in a job by pointing to the storage address and indicating which directory to mount from storage and where to mount it in the container."},"186":{"id":"/reference/api-51","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"To find your shared storage name, navigate to Nodes in the UI and select your desired node group. Next, click Actions > Config > Storage in the top-right corner of the page to list the available storage in the node group. If you have shared storage available, you should see something similar to the following image:"},"187":{"id":"/reference/api-52","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"As indicated in the image, lepton-shared-fs is the name of the shared storage which is mountable in containers."},"188":{"id":"/reference/api-53","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"With the storage name captured, we can define the mount specifications. To mount the volume, you will need to create a Python dictionary as follows:"},"189":{"id":"/reference/api-54","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"More information on each value:"},"190":{"id":"/reference/api-55","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"path: This is the directory from your shared storage to be mounted. Putting / will mount the root directory of the shared storage inside your container."},"191":{"id":"/reference/api-56","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"mount_path: This is the directory the path directory from storage will be mounted inside the container. Putting /my-mount will mount the path directory from storage at /my-mount inside the container. Note that this value cannot be /."},"192":{"id":"/reference/api-57","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"from: This is the name of the storage to mount. When using shared storage, you must first specify the storage type followed by a colon and your storage name. The storage type is very commonly node-nfs. Following the example above, this should be node-nfs:lepton-shared-fs."},"193":{"id":"/reference/api-58","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"You can also specify multiple mounts by making the dictionary above a list of dictionaries, similar to the following:"},"194":{"id":"/reference/api-59","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"After defining your mount points, add them to a job template with the following syntax:"},"195":{"id":"/reference/api-60","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#mounting-shared-storage","content":"If you have a single dictionary and not a list, this would be:"},"196":{"id":"/reference/api-61","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#authenticating-with-private-container-registries","content":"If you are using a private container registry that requires authentication, you need to specify the private container secret in the job definition."},"197":{"id":"/reference/api-62","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#authenticating-with-private-container-registries","content":"To create a new secret, navigate to the Settings > Registries page in the UI and click the green + New registry auth button in the top-right corner of the page. Follow the prompts to add your private image token."},"198":{"id":"/reference/api-63","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#authenticating-with-private-container-registries","content":"To add your secret to the job specification, enter it in the image_pull_secrets field like below:"},"199":{"id":"/reference/api-64","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#authenticating-with-private-container-registries","content":"Note that image_pull_secrets expects a list of strings, allowing multiple secrets to be added."},"200":{"id":"/reference/api-65","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#running-in-a-node-reservation","content":"If you have an existing node reservation for scheduling jobs on dedicated resources, you can add that to your request using the node_reservation field as follows:"},"201":{"id":"/reference/api-66","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#execute-the-task","content":"After the job has been defined in the previous step, it can be launched using the client. Since we are launching a job, we would use:"},"202":{"id":"/reference/api-67","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#execute-the-task","content":"This adds the job to the queue and schedules it when resources become available. The job should appear in the UI after the create function runs."},"203":{"id":"/reference/api-68","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#example-job-submission-via-sdk","content":"The following is a self-contained example of launching a batch job using the Python SDK following the flow outlined earlier."},"204":{"id":"/reference/api-69","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#example-job-submission-via-sdk","content":"Save the script above to a file such as run.py and launch it with:"},"205":{"id":"/reference/api-70","page_id":"/reference/api","tags":[],"type":"text","url":"/reference/api#example-job-submission-via-sdk","content":"Once the script completes, the launched job should be viewable in the UI."},"206":{"id":"/reference/support","page_id":"/reference/support","type":"page","content":"Support","tags":[],"url":"/reference/support"},"207":{"id":"/reference/support-0","page_id":"/reference/support","tags":[],"type":"text","url":"/reference/support","content":"How to request support for DGX Cloud Lepton"},"208":{"id":"/reference/support-1","page_id":"/reference/support","tags":[],"type":"text","url":"/reference/support","content":"As part of DGX Cloud Lepton, you will receive an entitlement for the NVIDIA Enterprise Support Portal (ESP) from your NVIDIA Technical Account Manager (TAM). You will need to register for ESP access following the instructions provided by your TAM."},"209":{"id":"/reference/support-2","page_id":"/reference/support","tags":[],"type":"text","url":"/reference/support","content":"Once registered, you can file support cases through ESP by clicking the Support Portal link in the Support dropdown menu in the top right of the DGX Cloud Lepton dashboard."},"210":{"id":"/reference/support-3","page_id":"/reference/support","tags":[],"type":"text","url":"/reference/support","content":"You can also send feedback for non-urgent issues or general comments by clicking the Send Feedback link. Note that a response is not guaranteed when using this feedback option."},"211":{"id":"/compute/bring-your-own-compute/add-machines","page_id":"/compute/bring-your-own-compute/add-machines","type":"page","content":"Add Machines","tags":[],"url":"/compute/bring-your-own-compute/add-machines"},"212":{"id":"/compute/bring-your-own-compute/add-machines-0","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines","content":"Learn how to add your machines to DGX Cloud Lepton with BYOC."},"213":{"id":"/compute/bring-your-own-compute/add-machines-1","page_id":"/compute/bring-your-own-compute/add-machines","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/add-machines#prerequisites","content":"Prerequisites"},"214":{"id":"/compute/bring-your-own-compute/add-machines-2","page_id":"/compute/bring-your-own-compute/add-machines","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Add Machines via SSH Access"},"215":{"id":"/compute/bring-your-own-compute/add-machines-3","page_id":"/compute/bring-your-own-compute/add-machines","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"Add Machines via Local Command"},"216":{"id":"/compute/bring-your-own-compute/add-machines-4","page_id":"/compute/bring-your-own-compute/add-machines","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/add-machines#validate-the-nodes","content":"Validate the Nodes"},"217":{"id":"/compute/bring-your-own-compute/add-machines-5","page_id":"/compute/bring-your-own-compute/add-machines","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/add-machines#release-machines","content":"Release Machines"},"218":{"id":"/compute/bring-your-own-compute/add-machines-6","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines","content":"DGX Cloud Lepton provides a simple way to manage all your BYOC nodes in the node group page."},"219":{"id":"/compute/bring-your-own-compute/add-machines-7","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines","content":"There are two ways to add your machines to DGX Cloud Lepton:"},"220":{"id":"/compute/bring-your-own-compute/add-machines-8","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines","content":"Add machines via SSH access"},"221":{"id":"/compute/bring-your-own-compute/add-machines-9","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines","content":"Add machines via local command"},"222":{"id":"/compute/bring-your-own-compute/add-machines-10","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#prerequisites","content":"To bring your own compute to DGX Cloud Lepton, you need to have a node group created first. If you don't have one, refer to the BYOC node group documentation to create a node group."},"223":{"id":"/compute/bring-your-own-compute/add-machines-11","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#prerequisites","content":"Only workspace admins can add machines to the node group."},"224":{"id":"/compute/bring-your-own-compute/add-machines-12","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#prerequisites","content":"The machines should meet the requirements of BYOC."},"225":{"id":"/compute/bring-your-own-compute/add-machines-13","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"The first way is to add your machines via SSH access. Follow the steps below:"},"226":{"id":"/compute/bring-your-own-compute/add-machines-14","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Navigate to the detailed page of the node group you want to add machines to, click the Add Machines button in the Nodes tab, and select Add via SSH Access."},"227":{"id":"/compute/bring-your-own-compute/add-machines-15","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Obtain the IP address and username of the machine you want to add."},"228":{"id":"/compute/bring-your-own-compute/add-machines-16","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Input the IP address and username in the following format:"},"229":{"id":"/compute/bring-your-own-compute/add-machines-17","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"You can add up to 10 machines at once by adding each machine on a new line."},"230":{"id":"/compute/bring-your-own-compute/add-machines-18","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Click the Import button to start importing."},"231":{"id":"/compute/bring-your-own-compute/add-machines-19","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"After importing the IP address and username, DGX Cloud Lepton will generate a public key for each machine and return a command to add the machines to the node group."},"232":{"id":"/compute/bring-your-own-compute/add-machines-20","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-ssh-access","content":"Copy the command and run it on the machine to register the public key. DGX Cloud Lepton will use this key to handle the rest of the process."},"233":{"id":"/compute/bring-your-own-compute/add-machines-21","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"You can also add your machines via local command, powered by GPUd:"},"234":{"id":"/compute/bring-your-own-compute/add-machines-22","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"Navigate to the detailed page of the node group you want to add machines to, click the Add Machines button in the Nodes tab, and select Add via Local Command."},"235":{"id":"/compute/bring-your-own-compute/add-machines-23","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"Click the Generate Command button in the pop-up window."},"236":{"id":"/compute/bring-your-own-compute/add-machines-24","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"You will see the generated command like the following script and the expiration time:"},"237":{"id":"/compute/bring-your-own-compute/add-machines-25","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#add-machines-via-local-command","content":"Copy the command and run it on the machine you want to add. This command will install GPUd and use GPUd to join the node group."},"238":{"id":"/compute/bring-your-own-compute/add-machines-26","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#validate-the-nodes","content":"DGX Cloud Lepton will automatically validate the nodes after adding them and display their status in the node list."},"239":{"id":"/compute/bring-your-own-compute/add-machines-27","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#validate-the-nodes","content":"If the machine is added successfully, the node status will show as Healthy."},"240":{"id":"/compute/bring-your-own-compute/add-machines-28","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#release-machines","content":"You can also release machines from the node group, which are represented as Nodes in the node group page."},"241":{"id":"/compute/bring-your-own-compute/add-machines-29","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#release-machines","content":"Click the menu button for the node and select the Release option."},"242":{"id":"/compute/bring-your-own-compute/add-machines-30","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#release-machines","content":"Select Graceful Release or Release, enter the reason, and submit the request."},"243":{"id":"/compute/bring-your-own-compute/add-machines-31","page_id":"/compute/bring-your-own-compute/add-machines","tags":[],"type":"text","url":"/compute/bring-your-own-compute/add-machines#release-machines","content":"The machines will be released from the node group, which will remove the dependencies installed during the BYOC process."},"244":{"id":"/compute/bring-your-own-compute/create-byoc","page_id":"/compute/bring-your-own-compute/create-byoc","type":"page","content":"Create BYOC Node Group","tags":[],"url":"/compute/bring-your-own-compute/create-byoc"},"245":{"id":"/compute/bring-your-own-compute/create-byoc-0","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc","content":"Learn how to create BYOC node groups in DGX Cloud Lepton."},"246":{"id":"/compute/bring-your-own-compute/create-byoc-1","page_id":"/compute/bring-your-own-compute/create-byoc","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/create-byoc#background","content":"Background"},"247":{"id":"/compute/bring-your-own-compute/create-byoc-2","page_id":"/compute/bring-your-own-compute/create-byoc","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Create a BYOC Node Group"},"248":{"id":"/compute/bring-your-own-compute/create-byoc-3","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#background","content":"Bring Your Own Compute (BYOC) allows you to bring your own compute resources to DGX Cloud Lepton. This is useful if you have existing resources you want to use with DGX Cloud Lepton."},"249":{"id":"/compute/bring-your-own-compute/create-byoc-4","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#background","content":"Node groups let you group multiple nodes for a given workload. There are two types of node groups:"},"250":{"id":"/compute/bring-your-own-compute/create-byoc-5","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#background","content":"BYOC Node Group: Uses your own compute resources."},"251":{"id":"/compute/bring-your-own-compute/create-byoc-6","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#background","content":"Lepton Managed Node Group: All resources are managed by DGX Cloud Lepton."},"252":{"id":"/compute/bring-your-own-compute/create-byoc-7","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#background","content":"This document focuses on how to create and manage BYOC node groups."},"253":{"id":"/compute/bring-your-own-compute/create-byoc-8","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Navigate to the Node Groups page, click Create Node Group, and select BYOC."},"254":{"id":"/compute/bring-your-own-compute/create-byoc-9","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Node Group Name: The name of the node group."},"255":{"id":"/compute/bring-your-own-compute/create-byoc-10","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Used By: List of workspaces that can allocate resources from this node group. The current workspace is pre-selected and cannot be removed."},"256":{"id":"/compute/bring-your-own-compute/create-byoc-11","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Visible To: List of workspaces that can view the node group."},"257":{"id":"/compute/bring-your-own-compute/create-byoc-12","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Accelerator Type: Defaults to the automatically detected accelerator type. You can also manually select the accelerator type from the preset options. The accelerator type name should identically match the GPU name shown when nvidia-smi is run on the GPU node."},"258":{"id":"/compute/bring-your-own-compute/create-byoc-13","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"InfiniBand: Whether to enable InfiniBand. Defaults to disabled."},"259":{"id":"/compute/bring-your-own-compute/create-byoc-14","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Advanced Configuration:"},"260":{"id":"/compute/bring-your-own-compute/create-byoc-15","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Job Default Envs: Environment variables to be set for all jobs in the node group. You can add multiple environment variables, including variable env and secret env."},"261":{"id":"/compute/bring-your-own-compute/create-byoc-16","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Private Net Interface: A list of network interfaces that are used for host LAN network traffic."},"262":{"id":"/compute/bring-your-own-compute/create-byoc-17","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Host Network: Whether to enable host network for the node group. Defaults to disabled. Enable host network to allow sharing the node's network namespace, which is useful for workloads requiring direct access to the host's network interfaces."},"263":{"id":"/compute/bring-your-own-compute/create-byoc-18","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Privileged Mode: Whether to enable privileged mode for the node group. Defaults to disabled. Privileged mode allows the node group to run privileged pods. This is useful for workloads that require access to the host's resources, such as the host's filesystem."},"264":{"id":"/compute/bring-your-own-compute/create-byoc-19","page_id":"/compute/bring-your-own-compute/create-byoc","tags":[],"type":"text","url":"/compute/bring-your-own-compute/create-byoc#create-a-byoc-node-group","content":"Expected GPUd Check: Configurations for the expected GPUd check. You can set expected values so that GPUd can help check if the node group is healthy, including InfiniBand Count, Minimum InfiniBand Bandwidth, and GPU Count."},"265":{"id":"/compute/bring-your-own-compute/requirements","page_id":"/compute/bring-your-own-compute/requirements","type":"page","content":"BYOC Requirements","tags":[],"url":"/compute/bring-your-own-compute/requirements"},"266":{"id":"/compute/bring-your-own-compute/requirements-0","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements","content":"Learn about the requirements for bringing your own compute to DGX Cloud Lepton."},"267":{"id":"/compute/bring-your-own-compute/requirements-1","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#software-requirements","content":"Software Requirements"},"268":{"id":"/compute/bring-your-own-compute/requirements-2","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#operating-system","content":"Operating System"},"269":{"id":"/compute/bring-your-own-compute/requirements-3","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#cuda-toolkit","content":"CUDA Toolkit"},"270":{"id":"/compute/bring-your-own-compute/requirements-4","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#nvidia-driver-and-nvidia-fabric-manager","content":"NVIDIA Driver and NVIDIA Fabric Manager"},"271":{"id":"/compute/bring-your-own-compute/requirements-5","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#standard-boot-option","content":"Standard Boot Option"},"272":{"id":"/compute/bring-your-own-compute/requirements-6","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#trusted-launch-boot-option","content":"Trusted Launch Boot Option"},"273":{"id":"/compute/bring-your-own-compute/requirements-7","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#system-libraries","content":"System Libraries"},"274":{"id":"/compute/bring-your-own-compute/requirements-8","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#software-packages","content":"Software Packages"},"275":{"id":"/compute/bring-your-own-compute/requirements-9","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#hardware-requirements","content":"Hardware Requirements"},"276":{"id":"/compute/bring-your-own-compute/requirements-10","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Storage Configuration"},"277":{"id":"/compute/bring-your-own-compute/requirements-11","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#cpu-requirements","content":"CPU Requirements"},"278":{"id":"/compute/bring-your-own-compute/requirements-12","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#memory","content":"Memory"},"279":{"id":"/compute/bring-your-own-compute/requirements-13","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#network-configuration","content":"Network Configuration"},"280":{"id":"/compute/bring-your-own-compute/requirements-14","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#network-requirements","content":"Network Requirements"},"281":{"id":"/compute/bring-your-own-compute/requirements-15","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#rdma-configuration","content":"RDMA Configuration"},"282":{"id":"/compute/bring-your-own-compute/requirements-16","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#ip-address-restrictions","content":"IP Address Restrictions"},"283":{"id":"/compute/bring-your-own-compute/requirements-17","page_id":"/compute/bring-your-own-compute/requirements","type":"heading","tags":[],"url":"/compute/bring-your-own-compute/requirements#performance-verification","content":"Performance Verification"},"284":{"id":"/compute/bring-your-own-compute/requirements-18","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements","content":"To bring your own compute to DGX Cloud Lepton, ensure your machines meet the following requirements."},"285":{"id":"/compute/bring-your-own-compute/requirements-19","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#software-requirements","content":"Since BYOC in DGX Cloud Lepton follows the standards of NVIDIA DGX OS 6 and NVIDIA DGX OS 7, your machines must meet the following requirements."},"286":{"id":"/compute/bring-your-own-compute/requirements-20","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#operating-system","content":"Your machine must run Ubuntu 22.04 LTS or a newer LTS version with a corresponding kernel version. For example, you can use Ubuntu 22.04 LTS with either kernel 5.15 or 6.8 since they are both compatible with 22.04 LTS.\nRefer to the official Ubuntu kernel release cycle for more details."},"287":{"id":"/compute/bring-your-own-compute/requirements-21","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#operating-system","content":"You must have root access to your machine."},"288":{"id":"/compute/bring-your-own-compute/requirements-22","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#operating-system","content":"Swap memory must be disabled."},"289":{"id":"/compute/bring-your-own-compute/requirements-23","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#operating-system","content":"Package auto-updates must be disabled."},"290":{"id":"/compute/bring-your-own-compute/requirements-24","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#cuda-toolkit","content":"For CUDA toolkit, we recommend version 12.4.1 or later with NVCC installed."},"291":{"id":"/compute/bring-your-own-compute/requirements-25","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#standard-boot-option","content":"If you select the Standard boot option for your machine (on Azure, this is called Security Option), DGX Cloud Lepton can automatically install the 550.144.03 NVIDIA driver with NVIDIA Fabric Manager for you."},"292":{"id":"/compute/bring-your-own-compute/requirements-26","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#standard-boot-option","content":"If NVIDIA driver and NVIDIA Fabric Manager are already installed on your machine, DGX Cloud Lepton will not install them again."},"293":{"id":"/compute/bring-your-own-compute/requirements-27","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#trusted-launch-boot-option","content":"If you want to use the Trusted Launch boot option, you need to install the NVIDIA driver and NVIDIA Fabric Manager manually."},"294":{"id":"/compute/bring-your-own-compute/requirements-28","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#trusted-launch-boot-option","content":"Release 550 family is recommended, especially version 550.144.03 or newer."},"295":{"id":"/compute/bring-your-own-compute/requirements-29","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#trusted-launch-boot-option","content":"You can also use release 535 family, with version ≥ 535.230.02."},"296":{"id":"/compute/bring-your-own-compute/requirements-30","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#trusted-launch-boot-option","content":"If you are using a system with NVSwitch, you must have NVIDIA Fabric Manager installed with the same version as the driver."},"297":{"id":"/compute/bring-your-own-compute/requirements-31","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#system-libraries","content":"glibc version ≥ GLIBC_2.32"},"298":{"id":"/compute/bring-your-own-compute/requirements-32","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#software-packages","content":"DGX Cloud Lepton will install GPUd, containerd, kubelet, and tailscale packages during the BYOC installation process."},"299":{"id":"/compute/bring-your-own-compute/requirements-33","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#software-packages","content":"If you have your own version of these packages already installed on the system, the BYOC installation process can fail."},"300":{"id":"/compute/bring-your-own-compute/requirements-34","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#software-packages","content":"NVIDIA recommends removing these packages from the system prior to adding the system to DGX Cloud Lepton."},"301":{"id":"/compute/bring-your-own-compute/requirements-35","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"For best performance and stability, we recommend the following storage configuration (local to each GPU node):"},"302":{"id":"/compute/bring-your-own-compute/requirements-36","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Root: NVMe SSD in RAID-1 configuration (post-RAID capacity of at least 1 TB)."},"303":{"id":"/compute/bring-your-own-compute/requirements-37","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Data: At least 20 TB of NVMe SSD (can be multiple disks)."},"304":{"id":"/compute/bring-your-own-compute/requirements-38","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Additionally, the following are minimum storage requirements with three configuration options:"},"305":{"id":"/compute/bring-your-own-compute/requirements-39","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Single OS Disk Setup"},"306":{"id":"/compute/bring-your-own-compute/requirements-40","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Minimum 640GB storage per GPU on the OS disk"},"307":{"id":"/compute/bring-your-own-compute/requirements-41","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"OS Disk + Data Volume Setup"},"308":{"id":"/compute/bring-your-own-compute/requirements-42","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"OS disk: Minimum 128GB"},"309":{"id":"/compute/bring-your-own-compute/requirements-43","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Data volume: Minimum 640GB per GPU (e.g., using LVM)"},"310":{"id":"/compute/bring-your-own-compute/requirements-44","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"OS Disk + Multiple Data Disks Setup"},"311":{"id":"/compute/bring-your-own-compute/requirements-45","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"OS disk: Minimum 128GB"},"312":{"id":"/compute/bring-your-own-compute/requirements-46","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Total data disks: Minimum 640GB per GPU"},"313":{"id":"/compute/bring-your-own-compute/requirements-47","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Note: DGX Cloud Lepton will automatically combine all data disks into a single volume."},"314":{"id":"/compute/bring-your-own-compute/requirements-48","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#storage-configuration","content":"Larger storage space is recommended for training clusters."},"315":{"id":"/compute/bring-your-own-compute/requirements-49","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#cpu-requirements","content":"We recommend having at least eight physical CPU cores per GPU, for example, AMD EPYC 9004 Series or Intel 4th or 5th Gen Xeon CPUs."},"316":{"id":"/compute/bring-your-own-compute/requirements-50","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#memory","content":"Minimum 256GB RAM per GPU"},"317":{"id":"/compute/bring-your-own-compute/requirements-51","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#memory","content":"ECC (Error-Correcting Code) support is recommended."},"318":{"id":"/compute/bring-your-own-compute/requirements-52","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#network-requirements","content":"All outbound traffic must be allowed"},"319":{"id":"/compute/bring-your-own-compute/requirements-53","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#network-requirements","content":"DNS server must be properly configured"},"320":{"id":"/compute/bring-your-own-compute/requirements-54","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#network-requirements","content":"A dedicated public IP address per machine is preferred"},"321":{"id":"/compute/bring-your-own-compute/requirements-55","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#network-requirements","content":"If you want to use Dev Pod on DGX Cloud Lepton, you need to open ports 40000 to 65535 for your machines."},"322":{"id":"/compute/bring-your-own-compute/requirements-56","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#rdma-configuration","content":"For multi-node training workloads, follow your cloud provider's guidelines to set up RDMA for east/west traffic."},"323":{"id":"/compute/bring-your-own-compute/requirements-57","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#ip-address-restrictions","content":"Avoid using or relying on IP addresses from the following CIDR ranges:"},"324":{"id":"/compute/bring-your-own-compute/requirements-58","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#ip-address-restrictions","content":"10.50.0.0/16"},"325":{"id":"/compute/bring-your-own-compute/requirements-59","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#ip-address-restrictions","content":"172.20.0.0/16"},"326":{"id":"/compute/bring-your-own-compute/requirements-60","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#ip-address-restrictions","content":"100.64.0.0/10"},"327":{"id":"/compute/bring-your-own-compute/requirements-61","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#performance-verification","content":"Your machines must pass the NCCL test provided by NVIDIA."},"328":{"id":"/compute/bring-your-own-compute/requirements-62","page_id":"/compute/bring-your-own-compute/requirements","tags":[],"type":"text","url":"/compute/bring-your-own-compute/requirements#performance-verification","content":"After ensuring your machines meet these requirements, refer to this guide to add them to DGX Cloud Lepton."},"329":{"id":"/examples/batch-job/distributed-training-with-mpi","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"page","content":"Distributed Training with MPI","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi"},"330":{"id":"/examples/batch-job/distributed-training-with-mpi-0","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi","content":"Learn how to run distributed training with MPI in DGX Cloud Lepton."},"331":{"id":"/examples/batch-job/distributed-training-with-mpi-1","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#prepare-the-python-script-for-distributed-training","content":"Prepare the Python script for distributed training"},"332":{"id":"/examples/batch-job/distributed-training-with-mpi-2","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#create-job-through-dashboard","content":"Create Job through Dashboard"},"333":{"id":"/examples/batch-job/distributed-training-with-mpi-3","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#set-up-the-job","content":"Set up the job"},"334":{"id":"/examples/batch-job/distributed-training-with-mpi-4","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#select-job-type","content":"Select Job Type"},"335":{"id":"/examples/batch-job/distributed-training-with-mpi-5","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#resource","content":"Resource"},"336":{"id":"/examples/batch-job/distributed-training-with-mpi-6","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#container","content":"Container"},"337":{"id":"/examples/batch-job/distributed-training-with-mpi-7","page_id":"/examples/batch-job/distributed-training-with-mpi","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-mpi#create-and-monitor","content":"Create and Monitor"},"338":{"id":"/examples/batch-job/distributed-training-with-mpi-8","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi","content":"MPI (Message Passing Interface) is a standard for parallel computing. It is a message-passing library that allows for the communication and coordination of processes in a distributed environment."},"339":{"id":"/examples/batch-job/distributed-training-with-mpi-9","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi","content":"DGX Cloud Lepton supports MPI for distributed training. Here is an example of running a distributed MPI job with two workers on DGX Cloud Lepton."},"340":{"id":"/examples/batch-job/distributed-training-with-mpi-10","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#prepare-the-python-script-for-distributed-training","content":"As an example, this script implements distributed training of a convolutional neural network (CNN) on the MNIST dataset using PyTorch's DistributedDataParallel (DDP) to leverage multiple GPUs in parallel."},"341":{"id":"/examples/batch-job/distributed-training-with-mpi-11","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#prepare-the-python-script-for-distributed-training","content":"The file has been saved in the GitHub repository here."},"342":{"id":"/examples/batch-job/distributed-training-with-mpi-12","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#create-job-through-dashboard","content":"Head over to the Batch Jobs page, and follow the steps below to create a job."},"343":{"id":"/examples/batch-job/distributed-training-with-mpi-13","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#select-job-type","content":"Select MPI on the top of the create job page."},"344":{"id":"/examples/batch-job/distributed-training-with-mpi-14","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#resource","content":"In the resource section, first select which node group you want to use."},"345":{"id":"/examples/batch-job/distributed-training-with-mpi-15","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#resource","content":"Select the resource type you want to use, for example, gpu.8xh100-sxm, and set the number of workers to the desired number.\nIn this guide, we will use two replicas, so we set the number of workers to 2."},"346":{"id":"/examples/batch-job/distributed-training-with-mpi-16","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#container","content":"In the container section, use the image (nvcr.io/nvidia/pytorch:25.08-py3) and paste the following command as the MPI Command to run the job:"},"347":{"id":"/examples/batch-job/distributed-training-with-mpi-17","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#create-and-monitor","content":"Now you can click the Create button to create and run the job. After that, you can check the job logs or details to monitor the job."},"348":{"id":"/examples/batch-job/distributed-training-with-mpi-18","page_id":"/examples/batch-job/distributed-training-with-mpi","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-mpi#create-and-monitor","content":"Within the job details page, you can see the status of each worker and the logs for each worker.\nYou can also use the Web Terminal to connect to the worker node and check the worker's status.\nOnce the job is finished, you will see the job with a Completed state."},"349":{"id":"/examples/batch-job/distributed-training-with-pytorch","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"page","content":"Distributed Training with PyTorch","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch"},"350":{"id":"/examples/batch-job/distributed-training-with-pytorch-0","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch","content":"Learn how to run distributed training with PyTorch in DGX Cloud Lepton."},"351":{"id":"/examples/batch-job/distributed-training-with-pytorch-1","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#prepare-the-python-script-for-distributed-training","content":"Prepare the Python script for distributed training"},"352":{"id":"/examples/batch-job/distributed-training-with-pytorch-2","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#create-job-through-dashboard","content":"Create Job through Dashboard"},"353":{"id":"/examples/batch-job/distributed-training-with-pytorch-3","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#set-up-the-job","content":"Set up the job"},"354":{"id":"/examples/batch-job/distributed-training-with-pytorch-4","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#select-job-type","content":"Select Job Type"},"355":{"id":"/examples/batch-job/distributed-training-with-pytorch-5","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#resource","content":"Resource"},"356":{"id":"/examples/batch-job/distributed-training-with-pytorch-6","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#container","content":"Container"},"357":{"id":"/examples/batch-job/distributed-training-with-pytorch-7","page_id":"/examples/batch-job/distributed-training-with-pytorch","type":"heading","tags":[],"url":"/examples/batch-job/distributed-training-with-pytorch#create-and-monitor","content":"Create and Monitor"},"358":{"id":"/examples/batch-job/distributed-training-with-pytorch-8","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch","content":"For batch jobs, some frameworks like PyTorch, TensorFlow, and Horovod support distributed training. DGX Cloud Lepton also supports distributed training for these frameworks."},"359":{"id":"/examples/batch-job/distributed-training-with-pytorch-9","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch","content":"Here is an example of running a distributed PyTorch job with two workers on DGX Cloud Lepton."},"360":{"id":"/examples/batch-job/distributed-training-with-pytorch-10","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#prepare-the-python-script-for-distributed-training","content":"As an example, this script implements distributed training of a convolutional neural network (CNN) on the MNIST dataset using PyTorch's DistributedDataParallel (DDP) to leverage multiple GPUs in parallel."},"361":{"id":"/examples/batch-job/distributed-training-with-pytorch-11","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#prepare-the-python-script-for-distributed-training","content":"The file has been saved in the GitHub repository here."},"362":{"id":"/examples/batch-job/distributed-training-with-pytorch-12","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#create-job-through-dashboard","content":"Head over to the Batch Jobs page, and follow the steps below to create a job."},"363":{"id":"/examples/batch-job/distributed-training-with-pytorch-13","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#select-job-type","content":"Select Torchrun on the top of the create job page."},"364":{"id":"/examples/batch-job/distributed-training-with-pytorch-14","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#resource","content":"In the resource section, first select which node group you want to use."},"365":{"id":"/examples/batch-job/distributed-training-with-pytorch-15","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#resource","content":"Select the resource type you want to use, for example, gpu.8xh100-sxm, and set the number of workers to the desired number.\nIn this guide, we want to use 2 replicas, so we set the number of workers to 2."},"366":{"id":"/examples/batch-job/distributed-training-with-pytorch-16","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#container","content":"In the container section, use the image (nvcr.io/nvidia/pytorch:25.08-py3) and paste the following command as the Torchrun Command to run the job:"},"367":{"id":"/examples/batch-job/distributed-training-with-pytorch-17","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#create-and-monitor","content":"Now you can click the Create button to create and run the job. After that, you can check the job logs or details to monitor the job."},"368":{"id":"/examples/batch-job/distributed-training-with-pytorch-18","page_id":"/examples/batch-job/distributed-training-with-pytorch","tags":[],"type":"text","url":"/examples/batch-job/distributed-training-with-pytorch#create-and-monitor","content":"Within the job details page, you can see the status of each worker and the logs for each worker.\nYou can also use the Web Terminal to connect to the worker node and check the worker's status.\nOnce the job is finished, you will see the job with a Completed state."},"369":{"id":"/examples/batch-job/job-failure-diagnose","page_id":"/examples/batch-job/job-failure-diagnose","type":"page","content":"Job Failure Diagnosis","tags":[],"url":"/examples/batch-job/job-failure-diagnose"},"370":{"id":"/examples/batch-job/job-failure-diagnose-0","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose","content":"Learn how to diagnose job failures in DGX Cloud Lepton."},"371":{"id":"/examples/batch-job/job-failure-diagnose-1","page_id":"/examples/batch-job/job-failure-diagnose","type":"heading","tags":[],"url":"/examples/batch-job/job-failure-diagnose#create-the-job","content":"Create the Job"},"372":{"id":"/examples/batch-job/job-failure-diagnose-2","page_id":"/examples/batch-job/job-failure-diagnose","type":"heading","tags":[],"url":"/examples/batch-job/job-failure-diagnose#resource","content":"Resource"},"373":{"id":"/examples/batch-job/job-failure-diagnose-3","page_id":"/examples/batch-job/job-failure-diagnose","type":"heading","tags":[],"url":"/examples/batch-job/job-failure-diagnose#container","content":"Container"},"374":{"id":"/examples/batch-job/job-failure-diagnose-4","page_id":"/examples/batch-job/job-failure-diagnose","type":"heading","tags":[],"url":"/examples/batch-job/job-failure-diagnose#create","content":"Create"},"375":{"id":"/examples/batch-job/job-failure-diagnose-5","page_id":"/examples/batch-job/job-failure-diagnose","type":"heading","tags":[],"url":"/examples/batch-job/job-failure-diagnose#diagnose-job-failure","content":"Diagnose Job Failure"},"376":{"id":"/examples/batch-job/job-failure-diagnose-6","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose","content":"DGX Cloud Lepton provides a built-in feature to automatically diagnose job failures.\nThis feature is designed to help users quickly identify and resolve issues in batch jobs, ensuring optimal performance and reliability."},"377":{"id":"/examples/batch-job/job-failure-diagnose-7","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose","content":"Let's walk through an example of how to use this feature to diagnose a job failure."},"378":{"id":"/examples/batch-job/job-failure-diagnose-8","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#create-the-job","content":"Navigate to the Batch Jobs page to create the job with the following configuration:"},"379":{"id":"/examples/batch-job/job-failure-diagnose-9","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#resource","content":"As we just want to test the job failure diagnosis feature, we can create a job with any desired GPU card and corresponding node group."},"380":{"id":"/examples/batch-job/job-failure-diagnose-10","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#container","content":"In the container section, use the default image and paste the following command to run the job:"},"381":{"id":"/examples/batch-job/job-failure-diagnose-11","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#container","content":"The above script will create a memory leak on a GPU by continuously allocating tensors using Pytorch and preventing them from being garbage collected, until it reaches a target memory usage (defaulting to 10GB but configurable via command line)."},"382":{"id":"/examples/batch-job/job-failure-diagnose-12","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#create","content":"Click Create to create the job.\nOnce the job is created, you can view the job in the Lepton Dashboard."},"383":{"id":"/examples/batch-job/job-failure-diagnose-13","page_id":"/examples/batch-job/job-failure-diagnose","tags":[],"type":"text","url":"/examples/batch-job/job-failure-diagnose#diagnose-job-failure","content":"The job will fail after reaching the target memory usage. You will then see a failure message on the job details page.\nFor this example, you will see an error tag showing the job failed due to ERR_GPU_OUT_OF_MEMORY.\nHover over the error tag to see the detailed error message."},"384":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"page","content":"Migrate Slurm Scripts to DGX Cloud Lepton Jobs","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job"},"385":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-0","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","content":"Migrate Slurm scripts to DGX Cloud Lepton jobs, including job creation, resource allocation, and environment configuration."},"386":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-1","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Slurm to DGX Cloud Lepton Terminology"},"387":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-2","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Example"},"388":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-3","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Create the Job on DGX Cloud Lepton"},"389":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-4","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"Create the job using the DGX Cloud Lepton CLI"},"390":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-5","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#list-jobs","content":"List Jobs"},"391":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-6","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#stop-a-job","content":"Stop a Job"},"392":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-7","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-10-jobs","content":"Create 10 jobs"},"393":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-8","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","type":"heading","tags":[],"url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#references","content":"References"},"394":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-9","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"The following table outlines the key terminology mapping between Slurm and DGX Cloud Lepton for job migration."},"395":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-10","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Slurm"},"396":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-11","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"DGX Cloud Lepton"},"397":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-12","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--job-name"},"398":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-13","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Job Name"},"399":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-14","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--partition, --cluster, --qos"},"400":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-15","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Node Group and Queue Priority"},"401":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-16","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--nodes (-N), --ntasks (-n)"},"402":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-17","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Workers"},"403":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-18","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--gres=gpu:X, --cpus-per-task, --mem"},"404":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-19","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Resource Shape"},"405":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-20","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--time"},"406":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-21","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Job Timeout"},"407":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-22","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--constraint, --nodelist"},"408":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-23","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Node Group / Node ID"},"409":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-24","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"srun, mpirun command"},"410":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-25","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Run Command (DGX Cloud Lepton will auto wrap with /bin/bash -c)"},"411":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-26","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"--export, environment"},"412":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-27","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Environment Variables and Secrets"},"413":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-28","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Singularity  / Docker Image"},"414":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-29","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Container Image"},"415":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-30","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"squeue, scancel"},"416":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-31","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Jobs List and Stop Job"},"417":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-32","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Job array, dependencies"},"418":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-33","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#slurm-to-dgx-cloud-lepton-terminology","content":"Not Supported Yet"},"419":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-34","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"This example demonstrates how to migrate a Slurm script to a DGX Cloud Lepton job."},"420":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-35","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"The following Slurm script launches an interactive job:"},"421":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-36","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Requests 1 node with 8 GPUs"},"422":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-37","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Uses a specific partition"},"423":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-38","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Runs a container from a Singularity image file"},"424":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-39","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Exports all current environment variables to the container"},"425":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-40","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Opens an interactive bash shell inside the container"},"426":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-41","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#example","content":"Now, convert this to a batch job on DGX Cloud Lepton."},"427":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-42","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Navigate to the Create a Job page, and configure the following fields:"},"428":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-43","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Name: test-job as the job name configured in the Slurm script."},"429":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-44","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Resource:"},"430":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-45","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Select a node group with available GPUs. If you don't have one, refer to this guide to request a node group. For this example, we request 1 node with 8 GPUs, so we can select a GPU type like H100-80GB-HBM3 and specify x8 for the GPU count."},"431":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-46","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"To specify the priority, click on the dropdown in the Resource section and select one of the priorities. Refer to this guide for more details."},"432":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-47","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Set workers to 1. You can also specify a higher number of workers to accelerate the training process."},"433":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-48","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Container Image: Specify the private container image in the Container section. For this example, use the default image."},"434":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-49","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Advanced Settings: Configure advanced settings in the Advanced Settings section."},"435":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-50","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-on-dgx-cloud-lepton","content":"Add environment variables and secrets under Environment Variables. For example, you can add HUGGING_FACE_HUB_TOKEN as a securely stored secret on DGX Cloud Lepton, which you can then reference as an environment variable within the container."},"436":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-51","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"Jobs can also be submitted using the Lepton CLI. Install the latest version of the CLI with the following command:"},"437":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-52","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"Once installed, the CLI can be invoked with lep. To see the list of available commands, run:"},"438":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-53","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"Taking the example srun command above, this can be invoked with the CLI as follows:"},"439":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-54","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"Breaking down each of the flags:"},"440":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-55","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--name: This is the name of the job and is analogous to the --job-name command."},"441":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-56","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--resource-shape: This describes what resources should be allocated in the container. For multi-GPU workloads, this comes in the format gpu.Nx<gpu-type> where N is the number of GPUs to allocate in the job and <gpu-type> is the name of the GPU to run on, such as h100-80gb. For single-GPU workloads, this would be gpu.h100-80gb. The list of available resource shapes can be viewed with lep job create -h and viewing the --resource-shape option. This is analogous to the --gpus-per-node flag for Slurm."},"442":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-57","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--node-group: This specifies which node group to run the job in. The list of available node groups can be found in the Nodes section of the DGX Cloud Lepton UI. This most closely resembles partitions in Slurm, but it goes further as different node groups could span different NVIDIA Cloud Providers (NCPs)."},"443":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-58","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--container-image: This is the container image to use for the job from a container registry, such as nvcr.io/nvidia/pytorch:YY.MM-py3. This is analogous to the --container-image command in Slurm."},"444":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-59","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--env: This is a key:value pair to add as an environment variable. Multiple environment variables can be set with multiple --env flags."},"445":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-60","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--secret: You can specify a secret that is added to DGX Cloud Lepton to be usable in the job. Set the NAME to the name that the secret will be set to inside the container, and SECRET_NAME to the name of the secret in DGX Cloud Lepton."},"446":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-61","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#create-the-job-using-the-dgx-cloud-lepton-cli","content":"--command: This is the actual command to run inside the job. This can either be something like sleep infinity to let the job run indefinitely in the background and allow users to connect remotely to the container, or one or multiple commands strung together to be run on container start."},"447":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-62","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#list-jobs","content":"To list all jobs, run:"},"448":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-63","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#list-jobs","content":"To list all jobs in some specific states, run:"},"449":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-64","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#stop-a-job","content":"To stop a job, run:"},"450":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-65","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#references","content":"Create a Job on DGX Cloud Lepton"},"451":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-66","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#references","content":"Slurm Documentation"},"452":{"id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job-67","page_id":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job","tags":[],"type":"text","url":"/examples/batch-job/migrate-slurm-scripts-to-lepton-job#references","content":"Lepton CLI Reference"},"453":{"id":"/examples/batch-job/moving-data-using-s5cmd","page_id":"/examples/batch-job/moving-data-using-s5cmd","type":"page","content":"Moving Data from S3 Using s5cmd","tags":[],"url":"/examples/batch-job/moving-data-using-s5cmd"},"454":{"id":"/examples/batch-job/moving-data-using-s5cmd-0","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd","content":"Learn how to download data from S3 using s5cmd in a DGX Cloud Lepton batch job."},"455":{"id":"/examples/batch-job/moving-data-using-s5cmd-1","page_id":"/examples/batch-job/moving-data-using-s5cmd","type":"heading","tags":[],"url":"/examples/batch-job/moving-data-using-s5cmd#prerequisites","content":"Prerequisites"},"456":{"id":"/examples/batch-job/moving-data-using-s5cmd-2","page_id":"/examples/batch-job/moving-data-using-s5cmd","type":"heading","tags":[],"url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Create AWS Credentials"},"457":{"id":"/examples/batch-job/moving-data-using-s5cmd-3","page_id":"/examples/batch-job/moving-data-using-s5cmd","type":"heading","tags":[],"url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"Create the Batch Job"},"458":{"id":"/examples/batch-job/moving-data-using-s5cmd-4","page_id":"/examples/batch-job/moving-data-using-s5cmd","type":"heading","tags":[],"url":"/examples/batch-job/moving-data-using-s5cmd#monitor-progress","content":"Monitor Progress"},"459":{"id":"/examples/batch-job/moving-data-using-s5cmd-5","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd","content":"This example demonstrates how to create a batch job to download data from a private S3 bucket. A batch job workload is used due to the non-interactive nature of this task. The workload is configured with private information to access the S3 bucket and execute the commands necessary to install and run the s5cmd tool to download the data from that bucket to a mounted filestore."},"460":{"id":"/examples/batch-job/moving-data-using-s5cmd-6","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#prerequisites","content":"The following are required before running the data download workload:"},"461":{"id":"/examples/batch-job/moving-data-using-s5cmd-7","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#prerequisites","content":"Storage associated with your node group"},"462":{"id":"/examples/batch-job/moving-data-using-s5cmd-8","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#prerequisites","content":"A private S3 bucket with data you want to download"},"463":{"id":"/examples/batch-job/moving-data-using-s5cmd-9","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#prerequisites","content":"AWS access credentials (access key ID and secret access key) with permissions to read from the S3 bucket"},"464":{"id":"/examples/batch-job/moving-data-using-s5cmd-10","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"To securely provide AWS credentials to your workload, you'll need to create secrets in DGX Cloud Lepton."},"465":{"id":"/examples/batch-job/moving-data-using-s5cmd-11","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Navigate to the Settings page in your workspace and click Secrets on the left side."},"466":{"id":"/examples/batch-job/moving-data-using-s5cmd-12","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Click + New Secret and select Custom Secret."},"467":{"id":"/examples/batch-job/moving-data-using-s5cmd-13","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Configure the secret:"},"468":{"id":"/examples/batch-job/moving-data-using-s5cmd-14","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Give it a descriptive name like AWS_SECRET_ACCESS_KEY."},"469":{"id":"/examples/batch-job/moving-data-using-s5cmd-15","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Set the value to your AWS secret access key."},"470":{"id":"/examples/batch-job/moving-data-using-s5cmd-16","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Set the visibility to Private if you do not want the secret to be used by other users in the workspace."},"471":{"id":"/examples/batch-job/moving-data-using-s5cmd-17","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Click Add to save."},"472":{"id":"/examples/batch-job/moving-data-using-s5cmd-18","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-aws-credentials","content":"Repeat steps 2-4 for your AWS access key ID."},"473":{"id":"/examples/batch-job/moving-data-using-s5cmd-19","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"Navigate to the create job page to configure your data download job."},"474":{"id":"/examples/batch-job/moving-data-using-s5cmd-20","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"Job Name: Set a descriptive name like s3-data-download.\nResource: Select the desired node group and resource shape. You can use a CPU-only resource for this workload.\nImage: Use the built-in image.\nRun Command: Copy the following script and replace your-s3-bucket-name-here with your actual S3 bucket name:"},"475":{"id":"/examples/batch-job/moving-data-using-s5cmd-21","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"Select Advanced Configuration, go to Environment Variables, and click Add Secret. Add your AWS Access Key ID and secret access key as secrets. This should look like the image below."},"476":{"id":"/examples/batch-job/moving-data-using-s5cmd-22","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"In the Storage section, click + Mount Storage. Select the storage volume where you want to download the data. Input your desired from path and /dataset as the mount path."},"477":{"id":"/examples/batch-job/moving-data-using-s5cmd-23","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#create-the-batch-job","content":"Click CREATE to submit your job to the queue. The job will automatically start when cluster resources become available."},"478":{"id":"/examples/batch-job/moving-data-using-s5cmd-24","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#monitor-progress","content":"After creating the job, you will be redirected to the job overview page. Monitor the job status until the status shows \"Completed\"."},"479":{"id":"/examples/batch-job/moving-data-using-s5cmd-25","page_id":"/examples/batch-job/moving-data-using-s5cmd","tags":[],"type":"text","url":"/examples/batch-job/moving-data-using-s5cmd#monitor-progress","content":"Once the status shows \"Completed\", click on the job name and go to the LOGS tab to verify the data transfer was successful. The logs should show the s5cmd output indicating successful data transfer from your S3 bucket to the mounted storage."},"480":{"id":"/examples/batch-job/nanogpt-training","page_id":"/examples/batch-job/nanogpt-training","type":"page","content":"NanoGPT Training","tags":[],"url":"/examples/batch-job/nanogpt-training"},"481":{"id":"/examples/batch-job/nanogpt-training-0","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training","content":"Learn how to train a NanoGPT model with distributed training on DGX Cloud Lepton."},"482":{"id":"/examples/batch-job/nanogpt-training-1","page_id":"/examples/batch-job/nanogpt-training","type":"heading","tags":[],"url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Create Training Job"},"483":{"id":"/examples/batch-job/nanogpt-training-2","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training","content":"This is a step-by-step guide for training a NanoGPT model with distributed training on DGX Cloud Lepton."},"484":{"id":"/examples/batch-job/nanogpt-training-3","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Navigate to the create job page where you can see the job configuration form."},"485":{"id":"/examples/batch-job/nanogpt-training-4","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Job name: Set it to nanogpt-training."},"486":{"id":"/examples/batch-job/nanogpt-training-5","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Resource: Select H100 x8 GPUs and set the worker count to 1. You can use multiple workers to speed up the training process."},"487":{"id":"/examples/batch-job/nanogpt-training-6","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Image: Choose the custom image and enter nvcr.io/nvidia/pytorch:24.11-py3"},"488":{"id":"/examples/batch-job/nanogpt-training-7","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"Run command: Copy the following code to the run command field."},"489":{"id":"/examples/batch-job/nanogpt-training-8","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"After completing the configuration, click Create to submit the job. You can then view the job status on the job detail page."},"490":{"id":"/examples/batch-job/nanogpt-training-9","page_id":"/examples/batch-job/nanogpt-training","tags":[],"type":"text","url":"/examples/batch-job/nanogpt-training#create-training-job","content":"The duration of the training job depends on the number of parameters you set and the number of GPUs you use. When the job is running, you can check the real-time logs and metrics."},"491":{"id":"/examples/batch-job/nccl-benchmark","page_id":"/examples/batch-job/nccl-benchmark","type":"page","content":"NCCL Performance Benchmark Job","tags":[],"url":"/examples/batch-job/nccl-benchmark"},"492":{"id":"/examples/batch-job/nccl-benchmark-0","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark","content":"This example shows how to run an NCCL performance benchmark job."},"493":{"id":"/examples/batch-job/nccl-benchmark-1","page_id":"/examples/batch-job/nccl-benchmark","type":"heading","tags":[],"url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"Step 1: Create a New Job"},"494":{"id":"/examples/batch-job/nccl-benchmark-2","page_id":"/examples/batch-job/nccl-benchmark","type":"heading","tags":[],"url":"/examples/batch-job/nccl-benchmark#step-2-run-the-job","content":"Step 2: Run the Job"},"495":{"id":"/examples/batch-job/nccl-benchmark-3","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark","content":"This example shows you how to run an NCCL performance benchmark job on DGX Cloud Lepton step by step."},"496":{"id":"/examples/batch-job/nccl-benchmark-4","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"First, you need to create a job in our platform.\nHead over to the Create Job page."},"497":{"id":"/examples/batch-job/nccl-benchmark-5","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"As you can see, there are many configurations you can fill in, like name, resource, image, etc. You can find a more detailed guide in documentation for creating a job."},"498":{"id":"/examples/batch-job/nccl-benchmark-6","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"In this example, we will use the following configurations:"},"499":{"id":"/examples/batch-job/nccl-benchmark-7","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"Name: nccl-benchmark or any name you want."},"500":{"id":"/examples/batch-job/nccl-benchmark-8","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"Resource: For the performance benchmark, choose 8xH100 to use the entire node for the NCCL performance benchmark, and set the number of workers to 2 to use both nodes. You will need to select a node group matching the resource shape requirements. It's recommended to use your own dedicated node group for this job."},"501":{"id":"/examples/batch-job/nccl-benchmark-9","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"Image: We will use nvcr.io/nvidia/pytorch:24.11-py3 as the image for the job. This image is built with the latest NVIDIA container toolkit and PyTorch 24.11. Choose custom image and enter the image name."},"502":{"id":"/examples/batch-job/nccl-benchmark-10","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"Run command: We will load code from a remote GitHub repository and run the NCCL performance benchmark. Enter the command as follows:"},"503":{"id":"/examples/batch-job/nccl-benchmark-11","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-1-create-a-new-job","content":"You need to fill in the YOUR_SSH_PUBLIC_KEY and YOUR_SSH_PRIVATE_KEY with your own credentials."},"504":{"id":"/examples/batch-job/nccl-benchmark-12","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-2-run-the-job","content":"Click on the Create button, then you can see the job status in the detail page of the job.\nThe job will proceed once two replicas are in the \"Ready\" state, which will take a few minutes."},"505":{"id":"/examples/batch-job/nccl-benchmark-13","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-2-run-the-job","content":"You can see the logs of the job by clicking on the Logs button to check the test result."},"506":{"id":"/examples/batch-job/nccl-benchmark-14","page_id":"/examples/batch-job/nccl-benchmark","tags":[],"type":"text","url":"/examples/batch-job/nccl-benchmark#step-2-run-the-job","content":"After the job is finished, the two replicas will be terminated automatically with Completed state."},"507":{"id":"/examples/batch-job/nemo-fw-e2e-guide","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"page","content":"NeMo Framework End-to-End Workflow Example","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide"},"508":{"id":"/examples/batch-job/nemo-fw-e2e-guide-0","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide","content":"A self-contained example on how to train a Large Language Model from scratch using NeMo Framework"},"509":{"id":"/examples/batch-job/nemo-fw-e2e-guide-1","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"Requirements"},"510":{"id":"/examples/batch-job/nemo-fw-e2e-guide-2","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#initial-setup","content":"Initial Setup"},"511":{"id":"/examples/batch-job/nemo-fw-e2e-guide-3","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-hugging-face-account","content":"Create a Hugging Face Account"},"512":{"id":"/examples/batch-job/nemo-fw-e2e-guide-4","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#optional-accept-the-model-specific-licenses","content":"(Optional) Accept the Model-Specific Licenses"},"513":{"id":"/examples/batch-job/nemo-fw-e2e-guide-5","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-weights--biases-account","content":"Create a Weights & Biases Account"},"514":{"id":"/examples/batch-job/nemo-fw-e2e-guide-6","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-vs-code-locally","content":"Set Up VS Code Locally"},"515":{"id":"/examples/batch-job/nemo-fw-e2e-guide-7","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#authenticate-with-dgx-cloud-lepton","content":"Authenticate with DGX Cloud Lepton"},"516":{"id":"/examples/batch-job/nemo-fw-e2e-guide-8","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#prepare-the-data","content":"Prepare the Data"},"517":{"id":"/examples/batch-job/nemo-fw-e2e-guide-9","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"Set Up the Scripts"},"518":{"id":"/examples/batch-job/nemo-fw-e2e-guide-10","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"Launch Data Preparation"},"519":{"id":"/examples/batch-job/nemo-fw-e2e-guide-11","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#pre-train-the-model","content":"Pre-Train the Model"},"520":{"id":"/examples/batch-job/nemo-fw-e2e-guide-12","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Set Up the Environment"},"521":{"id":"/examples/batch-job/nemo-fw-e2e-guide-13","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#launch-the-pre-training-job","content":"Launch the Pre-Training Job"},"522":{"id":"/examples/batch-job/nemo-fw-e2e-guide-14","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"View the Project Dashboard on Weights & Biases"},"523":{"id":"/examples/batch-job/nemo-fw-e2e-guide-15","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Deploy the Model for Inference"},"524":{"id":"/examples/batch-job/nemo-fw-e2e-guide-16","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-playground","content":"Send Requests to the Deployed Model using the Playground"},"525":{"id":"/examples/batch-job/nemo-fw-e2e-guide-17","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"Send Requests to the Deployed Model using the API"},"526":{"id":"/examples/batch-job/nemo-fw-e2e-guide-18","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#clean-up","content":"Clean Up"},"527":{"id":"/examples/batch-job/nemo-fw-e2e-guide-19","page_id":"/examples/batch-job/nemo-fw-e2e-guide","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-e2e-guide#citations","content":"Citations"},"528":{"id":"/examples/batch-job/nemo-fw-e2e-guide-20","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide","content":"This workflow provides a full end-to-end example of preparing a dataset and training a hybrid SSM foundation model based on Nemotron-H using the redesigned NeMo 2.0 for NeMo Framework. This guide is split into sub-sections to describe each part in detail."},"529":{"id":"/examples/batch-job/nemo-fw-e2e-guide-21","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide","content":"NeMo 2.0 now uses a Pythonic API that allows it to be integrated with IDEs such as Visual Studio Code (VS Code) and supports type checking."},"530":{"id":"/examples/batch-job/nemo-fw-e2e-guide-22","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide","content":"While this guide demonstrates pre-training a Nemotron-H 8B model from scratch, you can modify it to train any supported model with NeMo 2.0. For more information about NeMo 2.0, including the latest list of supported models, see the NVIDIA NeMo Framework User Guide."},"531":{"id":"/examples/batch-job/nemo-fw-e2e-guide-23","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide","content":"Nemotron-H is a hybrid SSM that leverages the Mamba architecture for faster training and inference speeds. Recent studies have proven this hybrid to be very robust, with models yielding slightly higher accuracy and much quicker training times and faster inference throughput compared to pure transformer-based equivalents, as found by Blakeman et al. (2025). With the rise of reasoning models, there is an increasing shift toward inference performance to meet the large output token demand. The hybrid SSM architecture of Nemotron-H is an excellent candidate for reasoning models given the high inference throughput."},"532":{"id":"/examples/batch-job/nemo-fw-e2e-guide-24","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"The following is a list of requirements to follow this complete workflow:"},"533":{"id":"/examples/batch-job/nemo-fw-e2e-guide-25","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes with eight GPUs each."},"534":{"id":"/examples/batch-job/nemo-fw-e2e-guide-26","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"VS Code installed on a local machine. Download instructions are available here."},"535":{"id":"/examples/batch-job/nemo-fw-e2e-guide-27","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"Python 3.10 or newer with PIP installed on a local machine."},"536":{"id":"/examples/batch-job/nemo-fw-e2e-guide-28","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"A shared filesystem with read/write access that is mountable in jobs."},"537":{"id":"/examples/batch-job/nemo-fw-e2e-guide-29","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"A Hugging Face account with an API token (setup steps are provided in the following section)."},"538":{"id":"/examples/batch-job/nemo-fw-e2e-guide-30","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#requirements","content":"A Weights & Biases account with an API token (setup steps are provided in the following section)."},"539":{"id":"/examples/batch-job/nemo-fw-e2e-guide-31","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#initial-setup","content":"This guide uses two external services to simplify the LLM development process: Hugging Face and Weights & Biases."},"540":{"id":"/examples/batch-job/nemo-fw-e2e-guide-32","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#initial-setup","content":"Hugging Face contains resources for many of the most popular language models and datasets in the community. You can leverage these resources while training the model to minimize deployment steps and maintain consistency with community model assumptions."},"541":{"id":"/examples/batch-job/nemo-fw-e2e-guide-33","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#initial-setup","content":"This workflow walks through training a Nemotron-H 8B model from scratch. The dataset you use needs to be tokenized using a custom tokenizer. NVIDIA, who produced the Nemotron-H models, published the tokenizer for the Nemotron-H models on Hugging Face. To use the tokenizer, you need to create a Hugging Face account and get an API token. The following steps guide you through that process."},"542":{"id":"/examples/batch-job/nemo-fw-e2e-guide-34","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-hugging-face-account","content":"If you don't have a Hugging Face account already, create one by going to https://huggingface.co/join and signing up with your corporate email account."},"543":{"id":"/examples/batch-job/nemo-fw-e2e-guide-35","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-hugging-face-account","content":"Once your account is set up, go to https://huggingface.co/settings/tokens while logged in to create a personal access token. Create a new token with Read access and give it a memorable name. Save the generated token in a safe place, as it won't be viewable again for security reasons."},"544":{"id":"/examples/batch-job/nemo-fw-e2e-guide-36","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#optional-accept-the-model-specific-licenses","content":"If you're using gated models that require users to accept their license, such as the official Llama-3.1-8B model, navigate to their specific model card on Hugging Face, read the privacy policy, and click the Agree and access repository button while logged in to Hugging Face. Now you can download resources from the gated repository using your personal access token."},"545":{"id":"/examples/batch-job/nemo-fw-e2e-guide-37","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#optional-accept-the-model-specific-licenses","content":"Since the Nemotron-H model isn't gated, this step is not necessary for following this guide."},"546":{"id":"/examples/batch-job/nemo-fw-e2e-guide-38","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-weights--biases-account","content":"Weights & Biases is a tool that allows developers to easily track experiments for AI applications. NeMo Framework natively supports logging many values such as training loss, learning rate, and gradient norm as well as resource utilization with Weights & Biases. Weights & Biases is highly recommended for tracking NeMo Framework jobs."},"547":{"id":"/examples/batch-job/nemo-fw-e2e-guide-39","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#create-a-weights--biases-account","content":"To get started with Weights & Biases, navigate to https://wandb.ai in a web browser and click the Sign Up button in the top right to create a free account. Once logged in, go to https://wandb.ai/settings and go to the bottom to create a new API key. This API key will be used while launching workflows to automatically log to Weights & Biases."},"548":{"id":"/examples/batch-job/nemo-fw-e2e-guide-40","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-vs-code-locally","content":"With VS Code installed on your local machine, run the application and open a new directory to save the scripts you'll use for launching jobs on the DGX Cloud cluster."},"549":{"id":"/examples/batch-job/nemo-fw-e2e-guide-41","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-vs-code-locally","content":"In VS Code, open a terminal window by clicking the Terminal > New Terminal button in the menu. Next, create a Python virtual environment and install the dependencies required for running NeMo 2.0 and Lepton using the following commands in the new terminal:"},"550":{"id":"/examples/batch-job/nemo-fw-e2e-guide-42","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-vs-code-locally","content":"Once dependencies are installed, you can define the data preparation and training scripts using VS Code."},"551":{"id":"/examples/batch-job/nemo-fw-e2e-guide-43","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-vs-code-locally","content":"The source env/bin/activate command above activates a Python virtual environment with the dependencies installed. If you need to leave the virtual environment, you can run deactivate. To activate it again, navigate back to the directory where the virtual environment named env was saved and run source env/bin/activate again. If you run into ModuleNotFound errors, it is likely the environment needs to be re-activated."},"552":{"id":"/examples/batch-job/nemo-fw-e2e-guide-44","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#authenticate-with-dgx-cloud-lepton","content":"NeMo Framework on DGX Cloud Lepton leverages the Lepton Python SDK to upload data to the cluster and schedule jobs. To use the Python SDK, you need to authenticate with the cluster using the Lepton CLI tool installed in the previous step. In your active virtual environment, run:"},"553":{"id":"/examples/batch-job/nemo-fw-e2e-guide-45","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#authenticate-with-dgx-cloud-lepton","content":"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."},"554":{"id":"/examples/batch-job/nemo-fw-e2e-guide-46","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#authenticate-with-dgx-cloud-lepton","content":"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."},"555":{"id":"/examples/batch-job/nemo-fw-e2e-guide-47","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#prepare-the-data","content":"NeMo Framework supports processing custom text-based datasets for pre-training new models. The data preprocessor requires datasets to be cleansed, excluding any sensitive or improperly formatted data that is unsuitable for use during pre-training. Each file in the dataset must be in .json or, ideally, .jsonl format. Datasets can be downloaded from external sources or uploaded directly to the remote filesystem."},"556":{"id":"/examples/batch-job/nemo-fw-e2e-guide-48","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#prepare-the-data","content":"The following example walks through downloading, extracting, concatenating, and preprocessing the Nemotron-CC dataset, which includes a large corpus of curated text from several domains and has been deduplicated and cleaned to make it an excellent candidate for pre-training LLMs. While the remainder of the document will be based on the Nemotron-CC dataset, this general process can be followed for most custom datasets and will provide guidance on how to adapt as needed."},"557":{"id":"/examples/batch-job/nemo-fw-e2e-guide-49","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"You will use four different scripts to prepare the Nemotron-CC dataset for pre-training a Nemotron-H-based SSM. These scripts will be automatically copied to the remote filesystem once launched. First, create a new sub-directory locally to save all of the files using this command:"},"558":{"id":"/examples/batch-job/nemo-fw-e2e-guide-50","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The four scripts that need to be created are as follows:"},"559":{"id":"/examples/batch-job/nemo-fw-e2e-guide-51","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The first script downloads the medium, medium-high, and high quality subsets of the Nemotron-CC training dataset from Common Crawl to the remote filesystem. By default, Nemotron-CC includes data subsets ranging from low quality to high quality, but you only want to filter out the lower quality data as studies have shown this degrades model performance (Blakeman et al., 2025)."},"560":{"id":"/examples/batch-job/nemo-fw-e2e-guide-52","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The script retrieves the list of pages to download from Common Crawl, then removes all of the low and low-medium quality pages before downloading the remaining links using the cc-downloader tool. This can run on a single node for more efficient resource utilization. Using the local VS Code session created previously, save the following file in the local directory at data_prep/download.sh."},"561":{"id":"/examples/batch-job/nemo-fw-e2e-guide-53","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The individual dataset shards are compressed in the Zstandard or .zstd format and must be decompressed. The following script distributes the downloaded files across all ranks, decompresses the shards, and then removes the compressed downloads to keep the filesystem clean. Using the local VS Code session, save the script in the local directory as data_prep/extract.py."},"562":{"id":"/examples/batch-job/nemo-fw-e2e-guide-54","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"Given that the Nemotron-CC dataset contains several thousands of files, it is helpful to concatenate them into fewer, larger files. Processing a smaller number of large files is much faster than handling a large number of small files, which eliminates potential data bottlenecks during the pre-training stage."},"563":{"id":"/examples/batch-job/nemo-fw-e2e-guide-55","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The following script takes 50 individual shards at a time and combines them into one large file, repeating for the entire dataset. Each rank concatenates a unique subsection of the dataset and deletes the individual shards in the end. Using the local VS Code session, save the script in the local directory as data_prep/concat.sh."},"564":{"id":"/examples/batch-job/nemo-fw-e2e-guide-56","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"The script combines 50 individual shards by default into a single file. For the complete dataset, this will yield 465 larger combined files representing the data, each being approximately 42 GB in size. To change how many shards are used in each file, increase or decrease the shards_per_file variable below. A larger number will result in fewer files that are larger in size. A smaller number will result in more files that are smaller in size."},"565":{"id":"/examples/batch-job/nemo-fw-e2e-guide-57","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"Once all of the files have been concatenated, it is time to preprocess the dataset. The preprocessing phase tokenizes each dataset file using the Nemotron-H 8B Base tokenizer, which is downloaded from Hugging Face, and creates .bin and .idx files for each concatenated file. As with the other scripts, this one divides the work amongst all available workers to speed up preprocessing. Using the local VS Code session, save the following script in the local directory as data_prep/preprocess.py."},"566":{"id":"/examples/batch-job/nemo-fw-e2e-guide-58","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"As mentioned, this script uses the Nemotron-H tokenizer because the intent is to use this data for pre-training a Nemotron-H model. However, you can swap out the tokenizer for a different one available on Hugging Face if pre-training a different model is desired.\nFor example, you can use the Llama-3.1-8B tokenizer instead. To do this, replace the reference to nvidia/Nemotron-H-8B-Base-8K in the script with the repo ID of the Llama-3.1-8B model, meta-llama/Meta-Llama-3.1-8B.\nBe sure to accept any applicable licenses on the model repository page if you haven't already done so."},"567":{"id":"/examples/batch-job/nemo-fw-e2e-guide-59","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"A final script needs to be written to launch all of the data preparation jobs on the cluster. This uses NeMo-Run to authenticate with the DGX Cloud Lepton cluster and run distributed PyTorch jobs directly on the cluster. The jobs will be launched sequentially in the order they are called. Using the local VS Code session, save the following script locally as data-prep.py."},"568":{"id":"/examples/batch-job/nemo-fw-e2e-guide-60","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"Several lines in the script below will need to be modified to reflect your cluster. The lines are as follows:"},"569":{"id":"/examples/batch-job/nemo-fw-e2e-guide-61","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"resource_shape=\"gpu.h100-80gb\": Replace gpu.h100-80gb with the desired resource shape. This is the GPU type and configuration to use for the job, such as gpu.8xh100-80gb might refer to a pod with 8x H100 GPUs available in it."},"570":{"id":"/examples/batch-job/nemo-fw-e2e-guide-62","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."},"571":{"id":"/examples/batch-job/nemo-fw-e2e-guide-63","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"\"HF_TOKEN\": \"xxxxxxxxxxxxxxxxxx\": Add your Hugging Face authentication token between the quotation marks."},"572":{"id":"/examples/batch-job/nemo-fw-e2e-guide-64","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"executor = lepton_executor(nodes=8, devices=1): The example runs on eight pods with one process per node. If more nodes/processes are required, specify the amount here."},"573":{"id":"/examples/batch-job/nemo-fw-e2e-guide-65","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-scripts","content":"\"from\": \"local:nfs\": If using remote shared storage, enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."},"574":{"id":"/examples/batch-job/nemo-fw-e2e-guide-66","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"Once all the scripts are saved in the specified location, it is time to launch the preprocessing job. NeMo-Run will launch the job automatically on the cluster, so starting data preparation is as simple as running a Python command. Launch data preparation with the following command in the terminal of your local VS Code session:"},"575":{"id":"/examples/batch-job/nemo-fw-e2e-guide-67","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"After creating the data preparation job, a pod for each worker and primary will be scheduled and started once resources become available on the cluster. You can monitor the process by viewing the logs in the DGX Cloud Lepton UI. The /nemo-workspace/data directory will evolve throughout the process with the following changes at the end of each stage:"},"576":{"id":"/examples/batch-job/nemo-fw-e2e-guide-68","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"After downloading, there will be 23,240 compressed data shards named CC-MAIN-*.zstd where the * identifies the individual files. These files are nested within directories to indicate the shard's quality, type, whether it is human or synthetic, etc. Each file is approximately 150 MB in size."},"577":{"id":"/examples/batch-job/nemo-fw-e2e-guide-69","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"After extraction, there will be 23,240 unzipped data shards named CC-MAIN-*.jsonl and all of the compressed .zstd files will be removed. Each file is approximately 350 MB in size."},"578":{"id":"/examples/batch-job/nemo-fw-e2e-guide-70","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"After concatenation, there will be 465 large, combined files named nemotron-cc_N.jsonl where N indicates the subset of the data. Each file will be approximately 46 GB in size. All of the individual CC-MAIN*.jsonl files will be removed."},"579":{"id":"/examples/batch-job/nemo-fw-e2e-guide-71","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"After preprocessing, there will be 465 .bin files and 400 .idx files named nemotron-cc-N_text_document, where N corresponds to the combined data file number. Each .bin file should be approximately 40 GB in size and .idx files should be around 350 MB (sizes will vary)."},"580":{"id":"/examples/batch-job/nemo-fw-e2e-guide-72","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-data-preparation","content":"Once all files have been preprocessed, it is time to begin pre-training the model."},"581":{"id":"/examples/batch-job/nemo-fw-e2e-guide-73","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#pre-train-the-model","content":"NeMo Framework contains many predefined configurations for various models, including the Nemotron-H models. This section will demonstrate how to initiate training a Nemotron-H 8B model on DGX Cloud Lepton using the preprocessed Nemotron-CC dataset."},"582":{"id":"/examples/batch-job/nemo-fw-e2e-guide-74","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#pre-train-the-model","content":"Pre-training is the most compute-intensive phase of the LLM training process as the model is typically trained for hundreds of billions to several trillions of tokens while it learns the vocabulary and word pairings of the underlying dataset. Depending on the size of the dataset and model, as well as the amount of compute resources available to train the model, this process can take anywhere from several days to a few months to finish. Therefore, it is strongly recommended to leverage as much of your available compute power as possible for pre-training the model."},"583":{"id":"/examples/batch-job/nemo-fw-e2e-guide-75","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Now the training job can be defined. The following script is used to launch pre-training of a Nemotron-H 8B model for one trillion tokens using the Nemotron-CC dataset that was prepared. Save the script to nemotronh-pretrain.py locally using your VS Code session. Note that, as with data preparation earlier, several lines will need to be modified to reflect your cluster. These lines are as follows:"},"584":{"id":"/examples/batch-job/nemo-fw-e2e-guide-76","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"resource_shape=\"gpu.8xh100-80gb\": Replace gpu.8xh100-80gb with the desired resource shape. This is the GPU type and configuration to use for the job, such as gpu.8xh100-80gb might refer to a pod with 8x H100 GPUs available in it."},"585":{"id":"/examples/batch-job/nemo-fw-e2e-guide-77","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."},"586":{"id":"/examples/batch-job/nemo-fw-e2e-guide-78","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"\"HF_TOKEN\": \"xxxxxxxxxxxxxxxxxx\": Add your Hugging Face authentication token between the quotation marks."},"587":{"id":"/examples/batch-job/nemo-fw-e2e-guide-79","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"\"WANDB_API_KEY\": \"xxxxxxxxxxxxxxxxxx\": Add your Weights & Biases authentication token between the quotation marks."},"588":{"id":"/examples/batch-job/nemo-fw-e2e-guide-80","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"\"from\": \"local:nfs\": If using remote shared storage, enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."},"589":{"id":"/examples/batch-job/nemo-fw-e2e-guide-81","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Depending on how many resources you have available, you can also change the number of nodes used for pre-training by modifying this line:"},"590":{"id":"/examples/batch-job/nemo-fw-e2e-guide-82","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Update the nodes=8 line to the desired number of nodes to train with. Keep gpus_per_node at eight as this allows optimal multi-node communication over NCCL."},"591":{"id":"/examples/batch-job/nemo-fw-e2e-guide-83","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Additionally, a Python script needs to be created that converts the model to Hugging Face format once training finishes. Create a new directory named scripts using:"},"592":{"id":"/examples/batch-job/nemo-fw-e2e-guide-84","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"Copy and save the following Python script to scripts/convert.py:"},"593":{"id":"/examples/batch-job/nemo-fw-e2e-guide-85","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#set-up-the-environment","content":"This script will run after the model completes pre-training and find the final checkpoint in the training directory and convert it to Hugging Face format where it can be used for downstream tasks."},"594":{"id":"/examples/batch-job/nemo-fw-e2e-guide-86","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-the-pre-training-job","content":"After modifying and saving the nemotronh-pretrain.py script locally and saving the conversion script, launch the pre-training job from the terminal in your local VS Code session using the following command:"},"595":{"id":"/examples/batch-job/nemo-fw-e2e-guide-87","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-the-pre-training-job","content":"Make sure your Python virtual environment is activated before running this command."},"596":{"id":"/examples/batch-job/nemo-fw-e2e-guide-88","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-the-pre-training-job","content":"The job will be scheduled with DGX Cloud Lepton and will launch once resources become available. After submission, the job will appear in the DGX Cloud Lepton Batch Jobs page."},"597":{"id":"/examples/batch-job/nemo-fw-e2e-guide-89","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#launch-the-pre-training-job","content":"NeMo Framework is fully integrated with Weights & Biases and logs multiple metrics that can be viewable on their website. If the W&B key was provided in the command, a new W&B project will automatically be created and metrics will be uploaded there. Viewing logs on W&B is recommended as the best path to monitor training progress."},"598":{"id":"/examples/batch-job/nemo-fw-e2e-guide-90","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"To view your charts, navigate to https://wandb.ai. You should see a link to the newly created project on your home page. Clicking the link will take you to your project dashboard, which should look similar to the following. Note that the figure below includes training results for two different runs where the second run is a continuation of the first."},"599":{"id":"/examples/batch-job/nemo-fw-e2e-guide-91","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"Two of the most important charts to monitor during pre-training are the reduced_train_loss and val_loss charts, which show how the model is learning over time. In general, these charts should have an exponential decay shape."},"600":{"id":"/examples/batch-job/nemo-fw-e2e-guide-92","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"The job will take around three weeks to complete on eight nodes. Since NeMo Framework pre-training scales linearly, doubling the number of nodes should halve the amount of time required to pre-train the model."},"601":{"id":"/examples/batch-job/nemo-fw-e2e-guide-93","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"While the model trains, a checkpoint will be periodically saved in the background automatically. Per the command above, the checkpoints will be saved in the /nemo-workspace/nemotronh_8b/nemotronh_8b/<date>/checkpoints directory where <date> is a timestamp of when the job was launched. Only the 10 checkpoints with the best val_loss values as well as the latest checkpoint will be saved. These checkpoints will be used for future fine-tuning runs. If the pre-training process gets interrupted for any reason, it can be re-launched by either cloning the job in the UI or running the python3 nemotronh-pretrain.py command again. Training will automatically resume from the latest checkpoint."},"602":{"id":"/examples/batch-job/nemo-fw-e2e-guide-94","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#view-the-project-dashboard-on-weights--biases","content":"After pre-training finishes, another task will begin to convert the final pre-trained model checkpoint to the Hugging Face format. This spins up another pod with a single GPU, which is required for conversion. The final Hugging Face model will be saved at /nemo-workspace/nemotronh_8b/huggingface. The converted Hugging Face model can be deployed with vLLM for inference."},"603":{"id":"/examples/batch-job/nemo-fw-e2e-guide-95","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Now that you have finished pre-training a base model and converted it to the Hugging Face format, you can deploy it for inference as a NIM and send requests to the deployed model to do quick human evaluations."},"604":{"id":"/examples/batch-job/nemo-fw-e2e-guide-96","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"This section is NOT intended for production inference deployments. The purpose of this section is to provide a quick way for engineers, QA teams, and other internal stakeholders to evaluate the model with user-generated prompts and inform decisions on the model's readiness. A production deployment would include user tracking, performance optimizations, and more."},"605":{"id":"/examples/batch-job/nemo-fw-e2e-guide-97","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"To deploy the model for inference, navigate to the Endpoints page, click the Create Endpoint button, and select the Create LLM Endpoint box."},"606":{"id":"/examples/batch-job/nemo-fw-e2e-guide-98","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Follow these steps in the new form that opens:"},"607":{"id":"/examples/batch-job/nemo-fw-e2e-guide-99","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Enter a name for the endpoint, such as nemotronh-8b-base."},"608":{"id":"/examples/batch-job/nemo-fw-e2e-guide-100","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Select Load from storage for the model and select the Volume where the checkpoint was saved and enter /nemo-workspace/nemotronh_8b/huggingface as the Model file path. This will load the Hugging Face model directly from the shared storage."},"609":{"id":"/examples/batch-job/nemo-fw-e2e-guide-101","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Select the node group to run the deployment in."},"610":{"id":"/examples/batch-job/nemo-fw-e2e-guide-102","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Select the GPU resource shape to deploy the model in. The 8B model can fit in a single GPU with at least 24 GB of GPU memory."},"611":{"id":"/examples/batch-job/nemo-fw-e2e-guide-103","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"In the Run Command section, add --trust-remote-code to the end of the command. By default, Hugging Face throws an error when models that aren't hosted on its servers are run so the user is aware that the model could be malicious. Since you built this model and trust it, you can authorize its usage for vLLM. The command will end up looking like vllm serve /nemo-workspace/nemotronh_8b/huggingface --port 8080 --trust-remote-code."},"612":{"id":"/examples/batch-job/nemo-fw-e2e-guide-104","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"(Optional) Set the Autoscaling option to increase the number of replicas and/or disable autoscaling to keep the instance persistent."},"613":{"id":"/examples/batch-job/nemo-fw-e2e-guide-105","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"(Optional) Set the Access tokens to Enable public access to disable authentication for requests. This will make the endpoint available for anyone that has the URL without authentication. This should only be for temporary testing purposes."},"614":{"id":"/examples/batch-job/nemo-fw-e2e-guide-106","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"In the Storage section, verify the correct volume and mount path is selected."},"615":{"id":"/examples/batch-job/nemo-fw-e2e-guide-107","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Click Create to deploy the model."},"616":{"id":"/examples/batch-job/nemo-fw-e2e-guide-108","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#deploy-the-model-for-inference","content":"Back in the Endpoints page you will see the newly created inference endpoint. It will take some time for the model to be loaded from storage before it transitions to the Ready state. Once the deployment is ready, it can handle inference requests."},"617":{"id":"/examples/batch-job/nemo-fw-e2e-guide-109","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-playground","content":"The easiest way to send a request to the deployed model is via the Playground in the UI. Open the endpoint that was created for the model and select the Playground tab. This opens a chat interface for the model using the exposed API. Note that this is an easy way to test the model, but shouldn't be considered a full chat interface and can't handle very large input and output token sizes."},"618":{"id":"/examples/batch-job/nemo-fw-e2e-guide-110","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-playground","content":"Using the sliders on the right of the page, set the Temperature to 0.7, Max Tokens to 512, and Top P to 0.6."},"619":{"id":"/examples/batch-job/nemo-fw-e2e-guide-111","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-playground","content":"Next, enter a prompt, such as Write me a short story about a baby dragon that learns to fly and hit the Send button to initiate the request. The request will be sent to the deployed model and will stream the response once ready. The form will stream the generated response from the model."},"620":{"id":"/examples/batch-job/nemo-fw-e2e-guide-112","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"The endpoint also exposes a REST API that follows the OpenAI API standard for interacting with requests. This can be used to integrate with downstream applications like chat UIs, AI agents, or other backend services. The general format for sending a request from the terminal is as follows:"},"621":{"id":"/examples/batch-job/nemo-fw-e2e-guide-113","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"The endpoint URL can be found in the UI by selecting the nemotronh-8b-base deployment in the Endpoints menu and copying the Endpoint URL at the top of the page under the model name. This URL typically has the format https://<workspace ID>-<endpoint name>.xenon.lepton.run. For example, if a workspace has an ID of abcdefg, the URL would be https://abcdefg-nemotronh-8b-base.xenon.lepton.run."},"622":{"id":"/examples/batch-job/nemo-fw-e2e-guide-114","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"To send a request from the terminal, replace X.X.X.X with the endpoint URL in the curl command above. Additionally, replace Write me a short story about a baby dragon that learns to fly with your prompt of choice. This command will generate 2048 tokens but this can be changed as needed depending on the prompt."},"623":{"id":"/examples/batch-job/nemo-fw-e2e-guide-115","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"After submitting the command, it will be passed to the deployed model, which will generate a response to the prompt. Streaming is disabled for this particular request to make the results easier to read in the terminal."},"624":{"id":"/examples/batch-job/nemo-fw-e2e-guide-116","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"The response should look similar to the following (response truncated - actual responses will vary):"},"625":{"id":"/examples/batch-job/nemo-fw-e2e-guide-117","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"The model's response will be in the choices list, specifically in the assistant role shown. For example, the exact response begins \"Sure, here's a short story: once upon a time...\""},"626":{"id":"/examples/batch-job/nemo-fw-e2e-guide-118","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#send-requests-to-the-deployed-model-using-the-api","content":"For more information on the OpenAI API, reference the official documentation."},"627":{"id":"/examples/batch-job/nemo-fw-e2e-guide-119","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#clean-up","content":"When the deployment is no longer needed, it can be stopped to free up additional compute resources."},"628":{"id":"/examples/batch-job/nemo-fw-e2e-guide-120","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#clean-up","content":"To stop the job, go to the Endpoints page in the UI and click the Delete button next to the nemotronh-8b-base endpoint shown in the list."},"629":{"id":"/examples/batch-job/nemo-fw-e2e-guide-121","page_id":"/examples/batch-job/nemo-fw-e2e-guide","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-e2e-guide#citations","content":"Blakeman, A., Basant, A., Khattar, A., Renduchintala, A., Bercovich, A., Ficek, A., Bjorlin, A., Taghibakhshi, A., Deshmukh, A. S., Mahabaleshwarkar, A. S., Tao, A., Shors, A., Aithal, A., Poojary, A., Dattagupta, A., Buddharaju, B., Chen, B., Ginsburg, B., Wang, B., ... Chen, Z. (2025). Nemotron-H: A family of accurate and efficient hybrid Mamba-Transformer models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.03624"},"630":{"id":"/examples/batch-job/nemo-fw-quickstart","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"page","content":"NeMo Framework Quick Start Guide","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart"},"631":{"id":"/examples/batch-job/nemo-fw-quickstart-0","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart","content":"A minimal example to launch a NeMo Framework pre-training job on DGX Cloud Lepton"},"632":{"id":"/examples/batch-job/nemo-fw-quickstart-1","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#requirements","content":"Requirements"},"633":{"id":"/examples/batch-job/nemo-fw-quickstart-2","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#initial-setup","content":"Initial Setup"},"634":{"id":"/examples/batch-job/nemo-fw-quickstart-3","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#authenticate-with-dgx-cloud-lepton","content":"Authenticate with DGX Cloud Lepton"},"635":{"id":"/examples/batch-job/nemo-fw-quickstart-4","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#launch-pre-training-script","content":"Launch Pre-training Script"},"636":{"id":"/examples/batch-job/nemo-fw-quickstart-5","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"Example Script Template"},"637":{"id":"/examples/batch-job/nemo-fw-quickstart-6","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-a-single-gpu","content":"Pre-train Using a Single GPU"},"638":{"id":"/examples/batch-job/nemo-fw-quickstart-7","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-eight-gpus","content":"Pre-train Using Eight GPUs"},"639":{"id":"/examples/batch-job/nemo-fw-quickstart-8","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#pre-train-on-two-nodes","content":"Pre-train on Two Nodes"},"640":{"id":"/examples/batch-job/nemo-fw-quickstart-9","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#clean-up","content":"Clean Up"},"641":{"id":"/examples/batch-job/nemo-fw-quickstart-10","page_id":"/examples/batch-job/nemo-fw-quickstart","type":"heading","tags":[],"url":"/examples/batch-job/nemo-fw-quickstart#next-steps","content":"Next Steps"},"642":{"id":"/examples/batch-job/nemo-fw-quickstart-11","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart","content":"This guide demonstrates how to launch an LLM pre-training job on DGX Cloud Lepton using NeMo Framework with minimal effort. You can use this guide to validate your cluster's setup and its readiness for larger-scale distributed jobs. For a complete end-to-end tutorial on training an LLM from scratch, refer to the full guide."},"643":{"id":"/examples/batch-job/nemo-fw-quickstart-12","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#requirements","content":"To validate NeMo Framework on DGX Cloud Lepton, you need:"},"644":{"id":"/examples/batch-job/nemo-fw-quickstart-13","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#requirements","content":"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes with eight GPUs each."},"645":{"id":"/examples/batch-job/nemo-fw-quickstart-14","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#requirements","content":"Python 3.10 or newer with PIP installed on a local machine."},"646":{"id":"/examples/batch-job/nemo-fw-quickstart-15","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#requirements","content":"A shared filesystem with read/write access that is mountable in jobs."},"647":{"id":"/examples/batch-job/nemo-fw-quickstart-16","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#initial-setup","content":"First, install the required Python dependencies to launch the script. Open a terminal on your computer and navigate to your preferred directory to save the launching scripts. Then, run the following commands:"},"648":{"id":"/examples/batch-job/nemo-fw-quickstart-17","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#initial-setup","content":"The source env/bin/activate command above activates a Python virtual environment with the dependencies installed. If you need to leave the virtual environment, you can run deactivate. To activate it again, navigate back to the directory where the virtual environment named env was saved and run source env/bin/activate again. If you run into ModuleNotFound errors, it is likely the environment needs to be re-activated."},"649":{"id":"/examples/batch-job/nemo-fw-quickstart-18","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#authenticate-with-dgx-cloud-lepton","content":"The Lepton Python SDK needs to authenticate with the DGX Cloud Lepton workspace to schedule the jobs. To authenticate with the CLI, run the following in your virtual environment:"},"650":{"id":"/examples/batch-job/nemo-fw-quickstart-19","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#authenticate-with-dgx-cloud-lepton","content":"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."},"651":{"id":"/examples/batch-job/nemo-fw-quickstart-20","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#authenticate-with-dgx-cloud-lepton","content":"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."},"652":{"id":"/examples/batch-job/nemo-fw-quickstart-21","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#launch-pre-training-script","content":"Now you can launch a pre-training job with NeMo Framework using synthetic data. The system will generate fake data during pre-training. While the resulting trained model won't be meaningful, this process helps validate the entire software stack and assess performance."},"653":{"id":"/examples/batch-job/nemo-fw-quickstart-22","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#launch-pre-training-script","content":"We'll start with a template script for single-GPU pre-training, then modify it to run on eight GPUs on a single node, and finally scale to 16 GPUs across two nodes."},"654":{"id":"/examples/batch-job/nemo-fw-quickstart-23","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"This is the template for launching pre-training. Copy this locally as train.py:"},"655":{"id":"/examples/batch-job/nemo-fw-quickstart-24","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"The script template will need to be modified based on your cluster. These settings are as follows:"},"656":{"id":"/examples/batch-job/nemo-fw-quickstart-25","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"resource_shape = f\"gpu.{devices}xa100-80gb\" and resource_shape = \"gpu.a100-80gb\": Replace the a100-80gb piece in both lines with the desired resource shape. This is the GPU type and configuration to use for the job. If your cluster has H100 GPUs, this would likely be h100-80gb. Be sure to update this in both sections as this will automatically handle the different formats for resource shapes."},"657":{"id":"/examples/batch-job/nemo-fw-quickstart-26","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."},"658":{"id":"/examples/batch-job/nemo-fw-quickstart-27","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#example-script-template","content":"\"from\": \"node-nfs:my-nfs\": Enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."},"659":{"id":"/examples/batch-job/nemo-fw-quickstart-28","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-a-single-gpu","content":"After modifying the template script for your environment, activate the virtual environment if not already done with:"},"660":{"id":"/examples/batch-job/nemo-fw-quickstart-29","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-a-single-gpu","content":"Next, launch the pre-training job with:"},"661":{"id":"/examples/batch-job/nemo-fw-quickstart-30","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-a-single-gpu","content":"This will copy the training script to the cluster storage and spin up a Batch Job using the NeMo:25.04 container on a single GPU. You can view the job in the Batch Jobs page in the UI. After the job starts, it will take a couple of minutes for all of the libraries to load and training progress will be shown in the logs. You'll know training has started when you see a line similar to this:"},"662":{"id":"/examples/batch-job/nemo-fw-quickstart-31","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-a-single-gpu","content":"The train_step_timing in s line shows the overall throughput."},"663":{"id":"/examples/batch-job/nemo-fw-quickstart-32","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-eight-gpus","content":"Moving on, scale the job up to eight GPUs for a single node. Edit the train.py script and change this line:"},"664":{"id":"/examples/batch-job/nemo-fw-quickstart-33","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-eight-gpus","content":"to this:"},"665":{"id":"/examples/batch-job/nemo-fw-quickstart-34","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-eight-gpus","content":"Save the file and run the script again with:"},"666":{"id":"/examples/batch-job/nemo-fw-quickstart-35","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-using-eight-gpus","content":"This will launch the same job as before, but now on eight GPUs. Check the job in the UI and view the performance. Ideally, the train_step_timing in s should be approximately 8x faster than the 1 GPU numbers from earlier."},"667":{"id":"/examples/batch-job/nemo-fw-quickstart-36","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-on-two-nodes","content":"Finally, update the script to run on 2 nodes with eight GPUs per node. Edit the train.py script and change this line:"},"668":{"id":"/examples/batch-job/nemo-fw-quickstart-37","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-on-two-nodes","content":"to this:"},"669":{"id":"/examples/batch-job/nemo-fw-quickstart-38","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-on-two-nodes","content":"Save the file and run the script again with:"},"670":{"id":"/examples/batch-job/nemo-fw-quickstart-39","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#pre-train-on-two-nodes","content":"This will launch a third job on 16 total GPUs on 2 nodes. Check the latest job in the UI and compare the train_step_timing in s again. This should ideally be approximately 2x faster than the eight GPU job run earlier."},"671":{"id":"/examples/batch-job/nemo-fw-quickstart-40","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#clean-up","content":"After completing all jobs, you can remove them in the UI by clicking the Delete button next to each job in the Batch Jobs page."},"672":{"id":"/examples/batch-job/nemo-fw-quickstart-41","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#next-steps","content":"If you made it this far, congratulations! Your DGX Cloud Lepton workspace should now be ready for distributed training jobs."},"673":{"id":"/examples/batch-job/nemo-fw-quickstart-42","page_id":"/examples/batch-job/nemo-fw-quickstart","tags":[],"type":"text","url":"/examples/batch-job/nemo-fw-quickstart#next-steps","content":"For an in-depth guide on training and fine-tuning LLMs with NeMo Framework, refer to our comprehensive guide."},"674":{"id":"/examples/batch-job/nemo-rl","page_id":"/examples/batch-job/nemo-rl","type":"page","content":"NeMo-RL Quick Start","tags":[],"url":"/examples/batch-job/nemo-rl"},"675":{"id":"/examples/batch-job/nemo-rl-0","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl","content":"An introduction to running NeMo-RL jobs for post-training LLMs on DGX Cloud Lepton"},"676":{"id":"/examples/batch-job/nemo-rl-1","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#requirements","content":"Requirements"},"677":{"id":"/examples/batch-job/nemo-rl-2","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#container-building","content":"Container Building"},"678":{"id":"/examples/batch-job/nemo-rl-3","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#job-setup","content":"Job Setup"},"679":{"id":"/examples/batch-job/nemo-rl-4","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#raycluster","content":"RayCluster"},"680":{"id":"/examples/batch-job/nemo-rl-5","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#running-jobs-in-the-raycluster","content":"Running jobs in the RayCluster"},"681":{"id":"/examples/batch-job/nemo-rl-6","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#rayjob","content":"RayJob"},"682":{"id":"/examples/batch-job/nemo-rl-7","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#monitoring","content":"Monitoring"},"683":{"id":"/examples/batch-job/nemo-rl-8","page_id":"/examples/batch-job/nemo-rl","type":"heading","tags":[],"url":"/examples/batch-job/nemo-rl#next-steps","content":"Next Steps"},"684":{"id":"/examples/batch-job/nemo-rl-9","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl","content":"NVIDIA NeMo-RL is a scalable library for post-training models and supports various techniques such as GRPO, DPO, and SFT. NeMo-RL leverages Ray for scheduling and management of resources. This guide showcases how to create a lightweight RayCluster on DGX Cloud Lepton and launch jobs with NeMo-RL."},"685":{"id":"/examples/batch-job/nemo-rl-10","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#requirements","content":"The following is a list of requirements to follow this guide:"},"686":{"id":"/examples/batch-job/nemo-rl-11","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#requirements","content":"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes."},"687":{"id":"/examples/batch-job/nemo-rl-12","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#requirements","content":"Privileged mode enabled on the DGX Cloud Lepton workspace for container building."},"688":{"id":"/examples/batch-job/nemo-rl-13","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#requirements","content":"Access to a container registry to publish images."},"689":{"id":"/examples/batch-job/nemo-rl-14","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#requirements","content":"A shared filesystem with read/write access that is mountable in jobs."},"690":{"id":"/examples/batch-job/nemo-rl-15","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"The NeMo-RL container must be built before launching jobs on DGX Cloud Lepton. Containers can be built directly on the platform using privileged dev pods. This process is documented here. Before building, ensure privileged mode is enabled for your node group as detailed in the linked guide."},"691":{"id":"/examples/batch-job/nemo-rl-16","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"To build the container, create a new dev pod with the following options:"},"692":{"id":"/examples/batch-job/nemo-rl-17","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Enter a pod name, such as nemo-rl-builder."},"693":{"id":"/examples/batch-job/nemo-rl-18","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Select a single GPU in your node group for the resource shape."},"694":{"id":"/examples/batch-job/nemo-rl-19","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Check the Enable privileged mode box in the Resource form."},"695":{"id":"/examples/batch-job/nemo-rl-20","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Select a Custom image and specify nvcr.io/nvidia/nemo:25.07 for the image."},"696":{"id":"/examples/batch-job/nemo-rl-21","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Select Custom entrypoint for the container Entrypoint."},"697":{"id":"/examples/batch-job/nemo-rl-22","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Enter the following as the entrypoint command. Replace docker tag nemo_rl <registry tag> with the location of your registry to push to. For example, if you will push the container to a private registry on nvcr.io named my-private-registry, the command will be docker tag nemo_rl nvcr.io/my-private-registry/nemo-rl. Copy the same tag for the docker push command beneath it as well, such as docker push nvcr.io/my-private-registry/nemo-rl."},"698":{"id":"/examples/batch-job/nemo-rl-23","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Click the Create button at the bottom of the page to run the dev pod and build the image. This process can take 30 minutes or longer depending on connection speed and processing power. Progress can be monitored in the job logs in the UI."},"699":{"id":"/examples/batch-job/nemo-rl-24","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#container-building","content":"Once the dev pod completes, the image should be available at the container registry you specified at the end of the entrypoint command."},"700":{"id":"/examples/batch-job/nemo-rl-25","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#job-setup","content":"There are two primary methods for launching jobs with Ray, which NeMo-RL relies on: via a RayCluster and directly as a RayJob. The two are compared here. As a summary, choose RayCluster when you want resources to persist and plan on running several jobs, or choose RayJob when you want to schedule a job on just the resources you need, run a single task to completion, and immediately free up the resources once complete. This guide demonstrates both methods—follow whichever path best suits your needs."},"701":{"id":"/examples/batch-job/nemo-rl-26","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"A RayCluster will spin up several workers and a head node, allowing jobs to be submitted to these resources. The resources will remain allocated until this job on DGX Cloud Lepton is explicitly stopped by a user, making the resources available again. This option is best for users who plan on running multiple jobs concurrently or sequentially and do not want resources to get torn down and re-allocated after each job."},"702":{"id":"/examples/batch-job/nemo-rl-27","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"To launch a simple RayCluster on DGX Cloud Lepton, create a new Batch Job in the UI and enter the following fields:"},"703":{"id":"/examples/batch-job/nemo-rl-28","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Enter a job name like nemo-rl-cluster."},"704":{"id":"/examples/batch-job/nemo-rl-29","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Select the node group for the job to run on."},"705":{"id":"/examples/batch-job/nemo-rl-30","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Enter the number of GPUs for each node in the Resource section. For example, x8 will use eight GPUs per worker."},"706":{"id":"/examples/batch-job/nemo-rl-31","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Enter the number of workers for the job. By default, this example will allow jobs to be scheduled on the head node in addition to the workers. If the head node should not run jobs, add an extra worker (that is, if 4 workers for running jobs and a standalone head node are desired, enter 5 for the worker count in the UI)."},"707":{"id":"/examples/batch-job/nemo-rl-32","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Enter the custom image that was pushed in the previous section, for example nvcr.io/my-private-registry/nemo-rl."},"708":{"id":"/examples/batch-job/nemo-rl-33","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Add your authentication key for the container registry in the Private Image Registry Auth field."},"709":{"id":"/examples/batch-job/nemo-rl-34","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Copy the following into the Run Command field. Note, if a discrete head node is desired, change the ray start --head --port=6379 --block line to be ray start --head --port=6379 --block --num-cpus 0 --num-gpus 0. This will ensure jobs don't get scheduled on the head node."},"710":{"id":"/examples/batch-job/nemo-rl-35","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"In the Advanced Configuration section, add a Hugging Face token by clicking Add secret in the Environment Variables field. Either select your Hugging Face token if already added, or add one by clicking CREATE NEW SECRET and select the Hugging Face option and fill out the requested information."},"711":{"id":"/examples/batch-job/nemo-rl-36","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"In the Advanced Configuration section, add storage with the + Mount Storage button and select the volume for your node group's storage and enter the storage path as well as the mount path inside containers."},"712":{"id":"/examples/batch-job/nemo-rl-37","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#raycluster","content":"Click the Create button at the bottom of the field to schedule the job. Once resources are available, DGX Cloud Lepton will pull the custom container and create the simple RayCluster."},"713":{"id":"/examples/batch-job/nemo-rl-38","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#running-jobs-in-the-raycluster","content":"Once the RayCluster is running and all pods are ready, select the job in the Batch Jobs queue. In the Replicas tab at the bottom of the screen, select the Terminal button for the first replica in the list. This will open a terminal session directly in the container for the head node where you can launch commands."},"714":{"id":"/examples/batch-job/nemo-rl-39","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#running-jobs-in-the-raycluster","content":"To verify the RayCluster, run:"},"715":{"id":"/examples/batch-job/nemo-rl-40","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#running-jobs-in-the-raycluster","content":"The status should match the number of nodes and GPUs specified when the job was created."},"716":{"id":"/examples/batch-job/nemo-rl-41","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#running-jobs-in-the-raycluster","content":"When the cluster is ready, you can launch a NeMo-RL job in the terminal session. For example, to run GRPO against a specific model on two workers with 8 GPUs each, run the following:"},"717":{"id":"/examples/batch-job/nemo-rl-42","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"A RayJob will allocate a specific number of resources and create a one-off RayCluster where a single job is run and resources are freed up at conclusion. The job runs in the background and is not interactive. This option is best for users who plan on running a single job and want resources to be available for any job types and not limited to just Ray."},"718":{"id":"/examples/batch-job/nemo-rl-43","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"To launch a simple RayJob on DGX Cloud Lepton, create a new Batch Job in the UI and enter the following fields:"},"719":{"id":"/examples/batch-job/nemo-rl-44","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Enter a job name like nemo-rl-job."},"720":{"id":"/examples/batch-job/nemo-rl-45","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Select the node group for the job to run on."},"721":{"id":"/examples/batch-job/nemo-rl-46","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Enter the number of GPUs for each node in the Resource section. For example, x8 will use eight GPUs per worker."},"722":{"id":"/examples/batch-job/nemo-rl-47","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Enter the number of workers for the job. By default, this example will allow jobs to be scheduled on the head node in addition to the workers. If the head node should not run jobs, add an extra worker (that is, if 4 workers for running jobs and a standalone head node are desired, enter 5 for the worker count in the UI)."},"723":{"id":"/examples/batch-job/nemo-rl-48","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Enter the custom image that was pushed in the previous section, for example nvcr.io/my-private-registry/nemo-rl."},"724":{"id":"/examples/batch-job/nemo-rl-49","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Add your authentication key for the container registry in the Private Image Registry Auth field."},"725":{"id":"/examples/batch-job/nemo-rl-50","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Copy the following into the Run Command field. Note, if a discrete head node is desired, change the ray start --head --port=6379 line to be ray start --head --port=6379 --num-cpus 0 --num-gpus 0. This will ensure the job doesn't run on the head node."},"726":{"id":"/examples/batch-job/nemo-rl-51","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"In the script, change the uv run python... command to the specific task you want to run with NeMo-RL including any modifications to the parameters. The task specified here will be run directly on the RayCluster that is created as part of this Batch Job."},"727":{"id":"/examples/batch-job/nemo-rl-52","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"In the Advanced Configuration section, add a Hugging Face token by clicking Add secret in the Environment Variables field. Either select your Hugging Face token if already added, or add one by clicking CREATE NEW SECRET and select the Hugging Face option and fill out the requested information."},"728":{"id":"/examples/batch-job/nemo-rl-53","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"In the Advanced Configuration section, add storage with the + Mount Storage button and select the volume for your node group's storage and enter the storage path as well as the mount path inside containers."},"729":{"id":"/examples/batch-job/nemo-rl-54","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#rayjob","content":"Click the Create button at the bottom of the field to schedule the job. Once resources are available, DGX Cloud Lepton will pull the custom container and create the simple RayCluster and run the specified job directly."},"730":{"id":"/examples/batch-job/nemo-rl-55","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#monitoring","content":"Regardless of the method used for launching the job, you can view the logs from the training process in the Logs tab on the job page in the UI. Additionally, you can see resource utilization in the Metrics tab for the job."},"731":{"id":"/examples/batch-job/nemo-rl-56","page_id":"/examples/batch-job/nemo-rl","tags":[],"type":"text","url":"/examples/batch-job/nemo-rl#next-steps","content":"For more information on using NeMo-RL, refer to their GitHub page and associated documentation."},"732":{"id":"/examples/batch-job/run-jobs-with-conda-environment","page_id":"/examples/batch-job/run-jobs-with-conda-environment","type":"page","content":"Running Jobs with Conda Environment","tags":[],"url":"/examples/batch-job/run-jobs-with-conda-environment"},"733":{"id":"/examples/batch-job/run-jobs-with-conda-environment-0","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment","content":"Learn how to run jobs with Conda environment in DGX Cloud Lepton."},"734":{"id":"/examples/batch-job/run-jobs-with-conda-environment-1","page_id":"/examples/batch-job/run-jobs-with-conda-environment","type":"heading","tags":[],"url":"/examples/batch-job/run-jobs-with-conda-environment#prepare-the-conda-environment","content":"Prepare the Conda environment"},"735":{"id":"/examples/batch-job/run-jobs-with-conda-environment-2","page_id":"/examples/batch-job/run-jobs-with-conda-environment","type":"heading","tags":[],"url":"/examples/batch-job/run-jobs-with-conda-environment#create-a-job-with-conda-environment","content":"Create a Job with Conda environment"},"736":{"id":"/examples/batch-job/run-jobs-with-conda-environment-3","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment","content":"Conda is known for its ability to create isolated environments for different projects.\nWhen running jobs on DGX Cloud Lepton, you can use Conda to ensure your code runs with the exact dependencies needed, making your workloads more reliable and reproducible."},"737":{"id":"/examples/batch-job/run-jobs-with-conda-environment-4","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment","content":"Here is an example of running a job with Conda environment management on DGX Cloud Lepton."},"738":{"id":"/examples/batch-job/run-jobs-with-conda-environment-5","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment#prepare-the-conda-environment","content":"Assume you have already set up a Dev Pod running with a Conda-installed image and filesystem mounted at /mnt.\nYou can create a Conda environment with PyTorch and pack it to the filesystem."},"739":{"id":"/examples/batch-job/run-jobs-with-conda-environment-6","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment#prepare-the-conda-environment","content":"The foo.tar.gz file contains the Conda environment."},"740":{"id":"/examples/batch-job/run-jobs-with-conda-environment-7","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment#create-a-job-with-conda-environment","content":"You can then load the Conda environment in the job by adding the following command in the Run Command field during job creation.\nMake sure Conda is installed in the container image."},"741":{"id":"/examples/batch-job/run-jobs-with-conda-environment-8","page_id":"/examples/batch-job/run-jobs-with-conda-environment","tags":[],"type":"text","url":"/examples/batch-job/run-jobs-with-conda-environment#create-a-job-with-conda-environment","content":"Once the job is created, you can view the job in the dashboard."},"742":{"id":"/examples/dev-pod/access-cursor","page_id":"/examples/dev-pod/access-cursor","type":"page","content":"(Experimental) Access Dev Pod with Cursor","tags":[],"url":"/examples/dev-pod/access-cursor"},"743":{"id":"/examples/dev-pod/access-cursor-0","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor","content":"Learn how to connect Cursor to a dev pod."},"744":{"id":"/examples/dev-pod/access-cursor-1","page_id":"/examples/dev-pod/access-cursor","type":"heading","tags":[],"url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"1. Create a Dev Pod and Launch a Cursor Dev Tunnel"},"745":{"id":"/examples/dev-pod/access-cursor-2","page_id":"/examples/dev-pod/access-cursor","type":"heading","tags":[],"url":"/examples/dev-pod/access-cursor#2-grant-access-to-the-vs-code-server","content":"2. Grant Access to the VS Code Server"},"746":{"id":"/examples/dev-pod/access-cursor-3","page_id":"/examples/dev-pod/access-cursor","type":"heading","tags":[],"url":"/examples/dev-pod/access-cursor#3-connect-to-the-dev-pod-with-cursor","content":"3. Connect to the Dev Pod with Cursor"},"747":{"id":"/examples/dev-pod/access-cursor-4","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor","content":"This is provided as an example that was tested with version 1.0 of Cursor and may not work with different versions. Remote access for version 1.0 could be removed by the Cursor team at any time as well. For any issues encountered while following this document, please reach out to Cursor support. NVIDIA is unable to provide assistance with remote access to Cursor tools."},"748":{"id":"/examples/dev-pod/access-cursor-5","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor","content":"Cursor is a powerful AI-assisted code editor built on VS Code that can connect to a Dev Pod for remote development using DGX Cloud Lepton resources. In this guide, we will show you how to connect to a Dev Pod with Cursor via Remote Tunnels."},"749":{"id":"/examples/dev-pod/access-cursor-6","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"Create a pod with the following run command depending on the CPU architecture of the Dev Pod, for configurations, refer to this guide:"},"750":{"id":"/examples/dev-pod/access-cursor-7","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"For x86-based systems:"},"751":{"id":"/examples/dev-pod/access-cursor-8","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"For ARM-based systems:"},"752":{"id":"/examples/dev-pod/access-cursor-9","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"This command will download the Cursor CLI and start a tunnel to the Dev Pod."},"753":{"id":"/examples/dev-pod/access-cursor-10","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#1-create-a-dev-pod-and-launch-a-cursor-dev-tunnel","content":"This command should work with most custom Debian-based containers, including the latest PyTorch and NeMo containers available on ngc.nvidia.com."},"754":{"id":"/examples/dev-pod/access-cursor-11","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#2-grant-access-to-the-vs-code-server","content":"Once the pod is created, you'll need to grant access to the VS Code server which is running the Cursor dev tunnel. Go to the Dev Pod details page, click on the Logs tab, and find the following line:"},"755":{"id":"/examples/dev-pod/access-cursor-12","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#2-grant-access-to-the-vs-code-server","content":"Go to the GitHub login page, and use the code generated in the logs to grant access to the VS Code server."},"756":{"id":"/examples/dev-pod/access-cursor-13","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#3-connect-to-the-dev-pod-with-cursor","content":"Once you have granted access to the VS Code server, you will be able to see the name of the Cursor dev tunnel in the logs."},"757":{"id":"/examples/dev-pod/access-cursor-14","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#3-connect-to-the-dev-pod-with-cursor","content":"If not already available, install version 1.0 of Cursor on your local machine."},"758":{"id":"/examples/dev-pod/access-cursor-15","page_id":"/examples/dev-pod/access-cursor","tags":[],"type":"text","url":"/examples/dev-pod/access-cursor#3-connect-to-the-dev-pod-with-cursor","content":"Open Cursor locally and ensure the remote tunnels extension is installed. Run the Remote-Tunnels: Connect to Tunnel... command in the command pallete in Cursor and authenticate with the same account you granted access to the dev tunnel for. If you granted access to the tunnel using GitHub, use the same GitHub account to authenticate inside Cursor. Once authenticated, select the dev tunnel listed in the Dev Pod logs to connect to the remote resources."},"759":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"page","content":"Access JupyterLab on Dev Pod via ngrok","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok"},"760":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-0","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok","content":"Learn how to access JupyterLab on your Dev Pod via ngrok."},"761":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-1","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-1-enable-jupyterlab-when-creating","content":"Step 1: Enable JupyterLab When Creating"},"762":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-2","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-2-set-jupyterlab-token","content":"Step 2: Set JupyterLab Token"},"763":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-3","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-3-create-ngrok-account","content":"Step 3: Create ngrok Account"},"764":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-4","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-4-get-ngrok-authtoken","content":"Step 4: Get ngrok Authtoken"},"765":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-5","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-5-install-and-set-up-ngrok","content":"Step 5: Install and Set Up ngrok"},"766":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-6","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"Step 6: Start ngrok"},"767":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-7","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","type":"heading","tags":[],"url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-7-access-jupyterlab","content":"Step 7: Access JupyterLab"},"768":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-8","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok","content":"If you do not wish to expose JupyterLab via the Proxy or Host Network options, an example is provided below which installs and uses ngrok in the Dev Pod to access JupyterLab."},"769":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-9","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-1-enable-jupyterlab-when-creating","content":"DGX Cloud Lepton provides an easy way to automatically install and launch JupyterLab in your Dev Pod container. This requires the following two options:"},"770":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-10","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-1-enable-jupyterlab-when-creating","content":"Use a Built-in image provided by DGX Cloud Lepton."},"771":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-11","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-1-enable-jupyterlab-when-creating","content":"Use the Dev pod entrypoint as the Entrypoint."},"772":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-12","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-1-enable-jupyterlab-when-creating","content":"Then, toggle the Enable JupyterLab checkbox to turn on that feature and click the Create button to create the Dev Pod."},"773":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-13","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-2-set-jupyterlab-token","content":"After creation and when the Dev Pod is ready, go to the details page and open the Terminal tab. Run the following command to set a new token for JupyterLab:"},"774":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-14","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-2-set-jupyterlab-token","content":"This command will help set a new token and restart the JupyterLab instance."},"775":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-15","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-3-create-ngrok-account","content":"Create an ngrok account for port forwarding.\nGo to the ngrok signup page and follow the instructions to create an ngrok account."},"776":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-16","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-4-get-ngrok-authtoken","content":"Go to the ngrok authtoken page. Click on the Copy button to copy the authtoken and save it for later use."},"777":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-17","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-5-install-and-set-up-ngrok","content":"Go back to the Dev Pod Terminal tab. In the web terminal, run the following command to install ngrok:"},"778":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-18","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-5-install-and-set-up-ngrok","content":"Then, run the following command to set up ngrok. Replace $NGROK_AUTHTOKEN with the authtoken you copied in step 4."},"779":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-19","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"After the authtoken is saved in the ngrok config, start ngrok on port 18889 using the following command:"},"780":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-20","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"You will see the ngrok tunnel start, and the public URL will be displayed in the terminal."},"781":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-21","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"If you want to run ngrok continuously in the background, follow these steps:"},"782":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-22","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"Run apt install tmux to install tmux."},"783":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-23","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"Run tmux to attach to the tmux session."},"784":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-24","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"Run ngrok http 18889 in the tmux session."},"785":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-25","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"Now you can detach from the tmux session by pressing Ctrl+b and then d."},"786":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-26","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-6-start-ngrok","content":"You can reattach to the tmux session by running tmux a."},"787":{"id":"/examples/dev-pod/access-jupyter-lab-via-ngrok-27","page_id":"/examples/dev-pod/access-jupyter-lab-via-ngrok","tags":[],"type":"text","url":"/examples/dev-pod/access-jupyter-lab-via-ngrok#step-7-access-jupyterlab","content":"Now you can access JupyterLab via the public URL from ngrok and the token you set in step 2."},"788":{"id":"/examples/dev-pod/access-vscode","page_id":"/examples/dev-pod/access-vscode","type":"page","content":"Access Dev Pod with VS Code","tags":[],"url":"/examples/dev-pod/access-vscode"},"789":{"id":"/examples/dev-pod/access-vscode-0","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode","content":"Learn how to access a Dev Pod with VS Code."},"790":{"id":"/examples/dev-pod/access-vscode-1","page_id":"/examples/dev-pod/access-vscode","type":"heading","tags":[],"url":"/examples/dev-pod/access-vscode#create-a-dev-pod-with-vs-code-as-run-command","content":"Create a Dev Pod with VS Code as Run Command"},"791":{"id":"/examples/dev-pod/access-vscode-2","page_id":"/examples/dev-pod/access-vscode","type":"heading","tags":[],"url":"/examples/dev-pod/access-vscode#grant-access-to-the-vs-code-server","content":"Grant access to the VS Code server"},"792":{"id":"/examples/dev-pod/access-vscode-3","page_id":"/examples/dev-pod/access-vscode","type":"heading","tags":[],"url":"/examples/dev-pod/access-vscode#connect-to-the-dev-pod-with-vs-code","content":"Connect to the Dev Pod with VS Code"},"793":{"id":"/examples/dev-pod/access-vscode-4","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode","content":"VS Code is a powerful code editor that can be used to access a Dev Pod. In this guide, we will show you how to connect to a Dev Pod with VS Code via Remote Tunnels."},"794":{"id":"/examples/dev-pod/access-vscode-5","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#create-a-dev-pod-with-vs-code-as-run-command","content":"Create a pod with the following run command, for configurations, refer to this guide:"},"795":{"id":"/examples/dev-pod/access-vscode-6","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#create-a-dev-pod-with-vs-code-as-run-command","content":"This command will download the VS Code CLI and start a tunnel to the Dev Pod."},"796":{"id":"/examples/dev-pod/access-vscode-7","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#grant-access-to-the-vs-code-server","content":"Once the pod is created, you'll need to  grant access to the VS Code server. Go to the Dev Pod details page, click on the Logs tab, and find the following line:"},"797":{"id":"/examples/dev-pod/access-vscode-8","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#grant-access-to-the-vs-code-server","content":"Go to the GitHub login page and use the code generated in the logs to grant access to the VS Code server."},"798":{"id":"/examples/dev-pod/access-vscode-9","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#connect-to-the-dev-pod-with-vs-code","content":"Once you have granted access to the VS Code server, you will be able to see the link to the VS Code server in logs."},"799":{"id":"/examples/dev-pod/access-vscode-10","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#connect-to-the-dev-pod-with-vs-code","content":"Open the link generated in the logs in your browser, you will be able to see the VS Code running in your browser."},"800":{"id":"/examples/dev-pod/access-vscode-11","page_id":"/examples/dev-pod/access-vscode","tags":[],"type":"text","url":"/examples/dev-pod/access-vscode#connect-to-the-dev-pod-with-vs-code","content":"If you'd like to connect to the Dev Pod with VS Code installed on your local machine, you can use Connect to Tunnel function in VS Code with the same authentication. Eg. if you've granted access to the VS Code server with github, you can use the same github account to connect to the Dev Pod with VS Code installed on your local machine."},"801":{"id":"/examples/dev-pod/configure-ssh-and-jupyter","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"page","content":"Install and Configure SSH and Jupyter for Custom Images","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter"},"802":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-0","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter","content":"Learn how to configure SSH service and JupyterLab for nvcr.io/nvidia/pytorch:25.04-py3 image in Dev Pod"},"803":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-1","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#step-1-prepare-the-setup-script","content":"Step 1: Prepare the Setup Script"},"804":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-2","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#configuration-parameters","content":"Configuration Parameters"},"805":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-3","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#step-2-create-dev-pod-with-pytorch-image","content":"Step 2: Create Dev Pod with PyTorch Image"},"806":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-4","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Step 3: Run the Setup Script"},"807":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-5","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#step-4-verify-the-configuration","content":"Step 4: Verify the Configuration"},"808":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-6","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-ssh-setup","content":"Verify SSH Setup"},"809":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-7","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-jupyterlab","content":"Verify JupyterLab"},"810":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-8","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-pytorch-environment","content":"Verify PyTorch Environment"},"811":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-9","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","type":"heading","tags":[],"url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"What This Example Achieves"},"812":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-10","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter","content":"This example shows you how to configure SSH service and JupyterLab for a custom Docker image nvcr.io/nvidia/pytorch:25.04-py3 when using Dev Pod."},"813":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-11","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter","content":"This PyTorch image doesn't come with SSH service and JupyterLab pre-configured, so manual setup is required for remote access and JupyterLab."},"814":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-12","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-1-prepare-the-setup-script","content":"Create a setup script that will install and configure SSH service and JupyterLab on your PyTorch container. Here's the complete script template:"},"815":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-13","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#configuration-parameters","content":"Before running the script, configure the following parameters:"},"816":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-14","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#configuration-parameters","content":"SSHPUB: Your SSH public key (e.g., ssh-rsa AAAxxxxx lepton@sampleDomain.com)"},"817":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-15","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#configuration-parameters","content":"JUPKEY: Your JupyterLab token (optional, leave empty to skip JupyterLab installation)"},"818":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-16","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-2-create-dev-pod-with-pytorch-image","content":"Navigate to Dev Pod Creation: Go to the Dev Pod creation page in your DGX Cloud Lepton dashboard."},"819":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-17","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-2-create-dev-pod-with-pytorch-image","content":"Select Custom Image: In the container configuration section, select custom image and input the image name nvcr.io/nvidia/pytorch:25.04-py3."},"820":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-18","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-2-create-dev-pod-with-pytorch-image","content":"Create the Pod: Complete the pod creation with your desired resource configuration."},"821":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-19","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Open the Web Terminal: Navigate to your Pod details page and switch to the \"Terminal\" tab."},"822":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-20","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Execute the Script: Copy the configured script from Step 1, paste it into the terminal, and press Enter."},"823":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-21","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Wait for Completion: The script will automatically:"},"824":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-22","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Install and configure SSH service on the PyTorch container"},"825":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-23","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Set up SSH keys and authentication"},"826":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-24","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Install and configure JupyterLab (if token provided)"},"827":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-25","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#step-3-run-the-setup-script","content":"Start the services"},"828":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-26","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-ssh-setup","content":"After the script completes successfully, verify that SSH is properly configured:"},"829":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-27","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-ssh-setup","content":"Check SSH Directory: Ensure the .ssh directory exists in the root directory"},"830":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-28","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-ssh-setup","content":"Check Authorized Keys: Verify that the authorized_keys file is present and contains your public key"},"831":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-29","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-ssh-setup","content":"Test Connection: Use the SSH connection command from the Pod details page under the \"Connect\" tab"},"832":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-30","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-jupyterlab","content":"If you configured JupyterLab:"},"833":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-31","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-jupyterlab","content":"Check Process: Verify that JupyterLab is running with pgrep jupyter-lab"},"834":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-32","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-jupyterlab","content":"Check Logs: View JupyterLab logs at /var/log/jupyter.log"},"835":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-33","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-jupyterlab","content":"Access Interface: Connect to JupyterLab through the Pod's network interface on port 18889"},"836":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-34","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#verify-pytorch-environment","content":"Once connected via SSH, you can verify the PyTorch environment:"},"837":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-35","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"By following this example, you will have:"},"838":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-36","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"A fully configured SSH service on the PyTorch 25.04-py3 container"},"839":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-37","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"Key-based SSH authentication"},"840":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-38","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"JupyterLab running on port 18889 (optional)"},"841":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-39","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"Proper environment variable exposure for SSH sessions"},"842":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-40","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"Access to the complete PyTorch development environment"},"843":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-41","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"Security configurations optimized for container environments"},"844":{"id":"/examples/dev-pod/configure-ssh-and-jupyter-42","page_id":"/examples/dev-pod/configure-ssh-and-jupyter","tags":[],"type":"text","url":"/examples/dev-pod/configure-ssh-and-jupyter#what-this-example-achieves","content":"This setup enables you to remotely access your PyTorch Dev Pod for machine learning development, model training, and interactive computing tasks with the full power of the NVIDIA PyTorch container."},"845":{"id":"/examples/dev-pod/container-build","page_id":"/examples/dev-pod/container-build","type":"page","content":"Build Container Image","tags":[],"url":"/examples/dev-pod/container-build"},"846":{"id":"/examples/dev-pod/container-build-0","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build","content":"Learn how to build container image in Dev Pod."},"847":{"id":"/examples/dev-pod/container-build-1","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#prerequisites","content":"Prerequisites"},"848":{"id":"/examples/dev-pod/container-build-2","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#steps","content":"Steps"},"849":{"id":"/examples/dev-pod/container-build-3","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#make-sure-privilege-mode-is-enabled","content":"Make sure privilege mode is enabled"},"850":{"id":"/examples/dev-pod/container-build-4","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#create-a-dev-pod","content":"Create a Dev Pod"},"851":{"id":"/examples/dev-pod/container-build-5","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#install-docker-and-required-plugins","content":"Install Docker and Required Plugins"},"852":{"id":"/examples/dev-pod/container-build-6","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#build-your-container-image","content":"Build Your Container Image"},"853":{"id":"/examples/dev-pod/container-build-7","page_id":"/examples/dev-pod/container-build","type":"heading","tags":[],"url":"/examples/dev-pod/container-build#push-to-remote-repository","content":"Push to Remote Repository"},"854":{"id":"/examples/dev-pod/container-build-8","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build","content":"Docker image is widely used as environment control. In this guide, we will show you how to build a container image in a Dev Pod."},"855":{"id":"/examples/dev-pod/container-build-9","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#prerequisites","content":"A Node Group with privilege mode enabled"},"856":{"id":"/examples/dev-pod/container-build-10","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#make-sure-privilege-mode-is-enabled","content":"Go to the node group page, and make sure the privilege mode is enabled for the node group you want to use. You can enable the privilege mode by clicking the \"Config\" button and select the \"Privilege Mode\" checkbox. Make sure it's enabled for the node group you want to use."},"857":{"id":"/examples/dev-pod/container-build-11","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#create-a-dev-pod","content":"Create a dev pod with the node group you want to use.\nMake sure the dev pod is created with the privilege mode enabled in the Resource selection step.\nIt is recommended to use the whole node for the dev pod with privilege mode enabled. Eg. select the shape with 8 gpus will use the whole node. For configurations, refer to this guide."},"858":{"id":"/examples/dev-pod/container-build-12","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#create-a-dev-pod","content":"The default images allow the root access by default.\nIf you want to use a custom image, you should make sure the image allows sudo access."},"859":{"id":"/examples/dev-pod/container-build-13","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#install-docker-and-required-plugins","content":"Once the dev pod is created, you can connect to the dev pod via web terminal. Then copy the following script to the terminal and run it."},"860":{"id":"/examples/dev-pod/container-build-14","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#install-docker-and-required-plugins","content":"Once the script is executed, you can verify the GPU setup by running the following command."},"861":{"id":"/examples/dev-pod/container-build-15","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#install-docker-and-required-plugins","content":"You should see the GPU setup information like the following."},"862":{"id":"/examples/dev-pod/container-build-16","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#build-your-container-image","content":"Now you can build your container image in the dev pod. First, create a Dockerfile with your desired configuration. Here's a simple sample Dockerfile:"},"863":{"id":"/examples/dev-pod/container-build-17","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#build-your-container-image","content":"Now you can build your container image using the following command:"},"864":{"id":"/examples/dev-pod/container-build-18","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#build-your-container-image","content":"You can change the Dockerfile based on your needs to build your own container image. For more information about the Dockerfile, please refer to the Dockerfile reference."},"865":{"id":"/examples/dev-pod/container-build-19","page_id":"/examples/dev-pod/container-build","tags":[],"type":"text","url":"/examples/dev-pod/container-build#push-to-remote-repository","content":"Once the container image is built, you can push it to a remote repository for further use with docker image push command."},"866":{"id":"/examples/dev-pod/nccl-test","page_id":"/examples/dev-pod/nccl-test","type":"page","content":"Run NCCL Test on Dev Pods","tags":[],"url":"/examples/dev-pod/nccl-test"},"867":{"id":"/examples/dev-pod/nccl-test-0","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test","content":"Learn how to run NCCL test on DGX Cloud Lepton."},"868":{"id":"/examples/dev-pod/nccl-test-1","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-1-create-dev-pods","content":"Step 1: Create Dev Pods"},"869":{"id":"/examples/dev-pod/nccl-test-2","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-2-setup-ssh-keys-for-pods-communication","content":"Step 2: Setup SSH Keys for Pods Communication"},"870":{"id":"/examples/dev-pod/nccl-test-3","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#1-generate-an-ssh-key-on-pod-1","content":"1. Generate an SSH key on Pod 1"},"871":{"id":"/examples/dev-pod/nccl-test-4","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#2-apply-the-public-key-to-pods-2-through-n","content":"2. Apply the public key to Pods 2 through N"},"872":{"id":"/examples/dev-pod/nccl-test-5","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-3-get-the-local-ip-addresses-for-pod-1-to-pod-n","content":"Step 3: Get the local IP addresses for Pod 1 to Pod N"},"873":{"id":"/examples/dev-pod/nccl-test-6","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-4-create-hostfile-on-pod-1","content":"Step 4: Create hostfile on Pod 1"},"874":{"id":"/examples/dev-pod/nccl-test-7","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-5-download-the-nccl-tests-script-on-every-pod","content":"Step 5: Download the nccl-tests script on every Pod"},"875":{"id":"/examples/dev-pod/nccl-test-8","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-6-run-the-nccl-tests-script-on-pod-2-to-pod-n","content":"Step 6: Run the nccl-tests script on Pod 2 to Pod N"},"876":{"id":"/examples/dev-pod/nccl-test-9","page_id":"/examples/dev-pod/nccl-test","type":"heading","tags":[],"url":"/examples/dev-pod/nccl-test#step-7-run-the-nccl-tests-script-on-pod-1","content":"Step 7: Run the nccl-tests script on Pod 1"},"877":{"id":"/examples/dev-pod/nccl-test-10","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test","content":"NCCL test is a tool to verify bandwidth among nodes that have GPUs with InfiniBand (IB) or RoCE connectivity."},"878":{"id":"/examples/dev-pod/nccl-test-11","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test","content":"This example covers how to run NCCL test on a set of Dev Pods."},"879":{"id":"/examples/dev-pod/nccl-test-12","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-1-create-dev-pods","content":"NCCL Test requires 2 or more Dev Pods, each utilizing a full node. In this example, we will create 2 Dev Pods, each uses 8x H100 GPUs."},"880":{"id":"/examples/dev-pod/nccl-test-13","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#1-generate-an-ssh-key-on-pod-1","content":"On Pod 1, generate an SSH key pair and add the public key to the list of authorized keys:"},"881":{"id":"/examples/dev-pod/nccl-test-14","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#2-apply-the-public-key-to-pods-2-through-n","content":"On each of Pod 2 to Pod N, prepare the .ssh directory and add the generated public key from Pod 1 to authorized_keys:"},"882":{"id":"/examples/dev-pod/nccl-test-15","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-3-get-the-local-ip-addresses-for-pod-1-to-pod-n","content":"You can obtain the local IP of the node from the dashboard by clicking on the node name and find it in the node detail page."},"883":{"id":"/examples/dev-pod/nccl-test-16","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-4-create-hostfile-on-pod-1","content":"Create a file named /tmp/hostfile.txt on Pod 1. Each line should contain the private IP address of the nodes where these pods are running."},"884":{"id":"/examples/dev-pod/nccl-test-17","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-6-run-the-nccl-tests-script-on-pod-2-to-pod-n","content":"On Pod 2 to Pod N, run the following command:"},"885":{"id":"/examples/dev-pod/nccl-test-18","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-6-run-the-nccl-tests-script-on-pod-2-to-pod-n","content":"You can find the ssh port from the Pod detail page. Commonly, the ssh port is 2222 since the Pod is utilizing a full node."},"886":{"id":"/examples/dev-pod/nccl-test-19","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-6-run-the-nccl-tests-script-on-pod-2-to-pod-n","content":"You should see \"Done\" printed at the end of the output."},"887":{"id":"/examples/dev-pod/nccl-test-20","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-7-run-the-nccl-tests-script-on-pod-1","content":"Then, on Pod 1, run the following command:"},"888":{"id":"/examples/dev-pod/nccl-test-21","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-7-run-the-nccl-tests-script-on-pod-1","content":"The output will be similar to the following:"},"889":{"id":"/examples/dev-pod/nccl-test-22","page_id":"/examples/dev-pod/nccl-test","tags":[],"type":"text","url":"/examples/dev-pod/nccl-test#step-7-run-the-nccl-tests-script-on-pod-1","content":"This result is obtained by running the NCCL tests on nvcr.io/nvidia/cuda-dl-base:24.10-cuda12.6-devel-ubuntu22.04."},"890":{"id":"/examples/connections/weights-and-biases","page_id":"/examples/connections/weights-and-biases","type":"page","content":"Connecting to Weights & Biases","tags":[],"url":"/examples/connections/weights-and-biases"},"891":{"id":"/examples/connections/weights-and-biases-0","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases","content":"Tutorial for sending training metrics to Weights & Biases"},"892":{"id":"/examples/connections/weights-and-biases-1","page_id":"/examples/connections/weights-and-biases","type":"heading","tags":[],"url":"/examples/connections/weights-and-biases#setup","content":"Setup"},"893":{"id":"/examples/connections/weights-and-biases-2","page_id":"/examples/connections/weights-and-biases","type":"heading","tags":[],"url":"/examples/connections/weights-and-biases#python-package-installation","content":"Python Package Installation"},"894":{"id":"/examples/connections/weights-and-biases-3","page_id":"/examples/connections/weights-and-biases","type":"heading","tags":[],"url":"/examples/connections/weights-and-biases#example-wb-job","content":"Example W&B Job"},"895":{"id":"/examples/connections/weights-and-biases-4","page_id":"/examples/connections/weights-and-biases","type":"heading","tags":[],"url":"/examples/connections/weights-and-biases#integration-with-nvidia-nemo-framework","content":"Integration with NVIDIA NeMo Framework"},"896":{"id":"/examples/connections/weights-and-biases-5","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases","content":"Weights & Biases (W&B) is a widely-used tool for charting training metrics for machine learning jobs, such as loss curves, resource usage, accuracy scores, and more. This makes it easy to validate how a model learns over time and to compare multiple runs to determine the best models for certain outcomes."},"897":{"id":"/examples/connections/weights-and-biases-6","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases","content":"W&B supports a simple Python API to send training information to their servers. To use the API, users will need to create an access token on W&B, install the Python package, and tell W&B which values to track."},"898":{"id":"/examples/connections/weights-and-biases-7","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#setup","content":"First, create a W&B access token by navigating to https://wandb.ai and click Sign Up in the top right to create a free account if not done already. Once logged in, go to https://wandb.ai/settings and go to the bottom to create a new API key. This API key needs to be specified for jobs that use W&B."},"899":{"id":"/examples/connections/weights-and-biases-8","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#python-package-installation","content":"The container used to run your job on DGX Cloud Lepton needs the W&B Python package installed. Some NGC images like the NeMo Framework container (nvcr.io/nvidia/nemo) already have the package installed, while others like the PyTorch image (nvcr.io/nvidia/pytorch) do not. If your container does not have W&B installed already, run this command as part of an entrypoint on container start or in a running container."},"900":{"id":"/examples/connections/weights-and-biases-9","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#python-package-installation","content":"You can check if your container already has W&B installed with:"},"901":{"id":"/examples/connections/weights-and-biases-10","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#python-package-installation","content":"If the above command returns nothing, W&B is not installed already."},"902":{"id":"/examples/connections/weights-and-biases-11","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"The following is a trivial example of a job that sends metrics to W&B using the API. The key points are:"},"903":{"id":"/examples/connections/weights-and-biases-12","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"Import the wandb module"},"904":{"id":"/examples/connections/weights-and-biases-13","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"Initialize the wandb project with wandb.init and specify hyperparameters and the project name"},"905":{"id":"/examples/connections/weights-and-biases-14","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"Tell W&B which values to send with wandb.log()"},"906":{"id":"/examples/connections/weights-and-biases-15","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"To authenticate with W&B, set the WANDB_API_KEY environment variable to your API key created earlier:"},"907":{"id":"/examples/connections/weights-and-biases-16","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"You can also set this environment variable directly in the platform when defining the job."},"908":{"id":"/examples/connections/weights-and-biases-17","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"After running the example code, you should see a new project called my-awesome-project in your W&B account."},"909":{"id":"/examples/connections/weights-and-biases-18","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#example-wb-job","content":"For your own W&B experiments, adding the API key will automate the login process so your own code should run automatically connected to your account."},"910":{"id":"/examples/connections/weights-and-biases-19","page_id":"/examples/connections/weights-and-biases","tags":[],"type":"text","url":"/examples/connections/weights-and-biases#integration-with-nvidia-nemo-framework","content":"NVIDIA NeMo Framework supports W&B natively. To use W&B with NeMo Framework, set your W&B key as an environment variable named WANDB_API_KEY. Refer to the documentation on integrating W&B for your specific NeMo Framework job."},"911":{"id":"/examples/endpoint/deploy-alphafold2-nim","page_id":"/examples/endpoint/deploy-alphafold2-nim","type":"page","content":"Deploy AlphaFold2 NIM","tags":[],"url":"/examples/endpoint/deploy-alphafold2-nim"},"912":{"id":"/examples/endpoint/deploy-alphafold2-nim-0","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Learn how to run the AlphaFold2 NIM on DGX Cloud Lepton."},"913":{"id":"/examples/endpoint/deploy-alphafold2-nim-1","page_id":"/examples/endpoint/deploy-alphafold2-nim","type":"heading","tags":[],"url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"Prerequisites"},"914":{"id":"/examples/endpoint/deploy-alphafold2-nim-2","page_id":"/examples/endpoint/deploy-alphafold2-nim","type":"heading","tags":[],"url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"Create Endpoint from NVIDIA NIM"},"915":{"id":"/examples/endpoint/deploy-alphafold2-nim-3","page_id":"/examples/endpoint/deploy-alphafold2-nim","type":"heading","tags":[],"url":"/examples/endpoint/deploy-alphafold2-nim#saving-alphafold2-model-in-cache","content":"Saving AlphaFold2 Model in Cache"},"916":{"id":"/examples/endpoint/deploy-alphafold2-nim-4","page_id":"/examples/endpoint/deploy-alphafold2-nim","type":"heading","tags":[],"url":"/examples/endpoint/deploy-alphafold2-nim#testing-alphafold2-endpoint","content":"Testing AlphaFold2 Endpoint"},"917":{"id":"/examples/endpoint/deploy-alphafold2-nim-5","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"AlphaFold2 is a protein structure prediction model from Google DeepMind. AlphaFold2 demonstrates state-of-the-art performance at predicting protein structure from amino acid sequences."},"918":{"id":"/examples/endpoint/deploy-alphafold2-nim-6","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"The NVIDIA AlphaFold2 NIM can:"},"919":{"id":"/examples/endpoint/deploy-alphafold2-nim-7","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Predict protein structure given a protein sequence."},"920":{"id":"/examples/endpoint/deploy-alphafold2-nim-8","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Predict a multiple sequence alignment (MSA) for a given sequence against a series of protein sequence databases. The NIM includes the GPU-accelerated MMSeqs2 package for Multiple Sequence Alignment."},"921":{"id":"/examples/endpoint/deploy-alphafold2-nim-9","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Predict a protein structure given a pre-computed MSA of the sequence against protein sequence databases."},"922":{"id":"/examples/endpoint/deploy-alphafold2-nim-10","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"For more information about AlphaFold2, see the AlphaFold2 paper in Nature. If you use this NIM or AlphaFold2, make sure to cite the paper:"},"923":{"id":"/examples/endpoint/deploy-alphafold2-nim-11","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2"},"924":{"id":"/examples/endpoint/deploy-alphafold2-nim-12","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim","content":"Refer to NVIDIA NIM for AlphaFold2 documentation for more information."},"925":{"id":"/examples/endpoint/deploy-alphafold2-nim-13","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"To run the AlphaFold2 NIM, you need:"},"926":{"id":"/examples/endpoint/deploy-alphafold2-nim-14","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."},"927":{"id":"/examples/endpoint/deploy-alphafold2-nim-15","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"Access to at least 1x NVIDIA A100 GPU or higher on DGX Cloud Lepton"},"928":{"id":"/examples/endpoint/deploy-alphafold2-nim-16","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"At least 1.3 TB of available SSD storage"},"929":{"id":"/examples/endpoint/deploy-alphafold2-nim-17","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#prerequisites","content":"At least 24 CPU cores"},"930":{"id":"/examples/endpoint/deploy-alphafold2-nim-18","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"Navigate to the create endpoint page on the dashboard."},"931":{"id":"/examples/endpoint/deploy-alphafold2-nim-19","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"In the NIM Configuration section, specify the container image, registry auth key, and NGC API key."},"932":{"id":"/examples/endpoint/deploy-alphafold2-nim-20","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"Image: Choose Custom image and enter nim/deepmind/alphafold2:2.1.0."},"933":{"id":"/examples/endpoint/deploy-alphafold2-nim-21","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"Registry auth: Select one of the existing registry auth configurations you created in your workspace or create a new one."},"934":{"id":"/examples/endpoint/deploy-alphafold2-nim-22","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."},"935":{"id":"/examples/endpoint/deploy-alphafold2-nim-23","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#create-endpoint-from-nvidia-nim","content":"The AlphaFold2 NIM can take multiple hours to download the dataset initially. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 7200 seconds."},"936":{"id":"/examples/endpoint/deploy-alphafold2-nim-24","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#saving-alphafold2-model-in-cache","content":"Since the AlphaFold2 model can take hours to download, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."},"937":{"id":"/examples/endpoint/deploy-alphafold2-nim-25","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#saving-alphafold2-model-in-cache","content":"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /nim/.cache. Then go to Storage and click + Mount Storage. Select your desired volume, set the from path to /nim/.cache, and set the to path to /nim/.cache."},"938":{"id":"/examples/endpoint/deploy-alphafold2-nim-26","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#saving-alphafold2-model-in-cache","content":"This should now look like the image below."},"939":{"id":"/examples/endpoint/deploy-alphafold2-nim-27","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#saving-alphafold2-model-in-cache","content":"For other endpoint configurations, refer to the Endpoints documentation."},"940":{"id":"/examples/endpoint/deploy-alphafold2-nim-28","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#testing-alphafold2-endpoint","content":"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"},"941":{"id":"/examples/endpoint/deploy-alphafold2-nim-29","page_id":"/examples/endpoint/deploy-alphafold2-nim","tags":[],"type":"text","url":"/examples/endpoint/deploy-alphafold2-nim#testing-alphafold2-endpoint","content":"If you set your endpoint to be publicly accessible, you do not need to set an endpoint token."},"942":{"id":"/examples/endpoint/deploy-gpt-oss","page_id":"/examples/endpoint/deploy-gpt-oss","type":"page","content":"Deploy GPT-OSS-120B","tags":[],"url":"/examples/endpoint/deploy-gpt-oss"},"943":{"id":"/examples/endpoint/deploy-gpt-oss-0","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss","content":"Learn how to deploy GPT-OSS-120B for inference on DGX Cloud Lepton."},"944":{"id":"/examples/endpoint/deploy-gpt-oss-1","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#gpt-oss-120b-deployment-guide","content":"GPT-OSS-120B Deployment Guide"},"945":{"id":"/examples/endpoint/deploy-gpt-oss-2","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#prerequisites","content":"Prerequisites"},"946":{"id":"/examples/endpoint/deploy-gpt-oss-3","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"Option 1: Create Endpoint from NVIDIA NIM via UI"},"947":{"id":"/examples/endpoint/deploy-gpt-oss-4","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#save-model-in-cache","content":"Save Model in Cache"},"948":{"id":"/examples/endpoint/deploy-gpt-oss-5","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#access-token","content":"Access Token"},"949":{"id":"/examples/endpoint/deploy-gpt-oss-6","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#option-2-create-endpoint-from-nvidia-nim-via-cli","content":"Option 2: Create Endpoint from NVIDIA NIM via CLI"},"950":{"id":"/examples/endpoint/deploy-gpt-oss-7","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#install-dgx-cloud-lepton-python-package","content":"Install DGX Cloud Lepton Python package"},"951":{"id":"/examples/endpoint/deploy-gpt-oss-8","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#authenticate-with-the-dgx-cloud-lepton-cli","content":"Authenticate with the DGX Cloud Lepton CLI"},"952":{"id":"/examples/endpoint/deploy-gpt-oss-9","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"Deploy the Endpoint via CLI"},"953":{"id":"/examples/endpoint/deploy-gpt-oss-10","page_id":"/examples/endpoint/deploy-gpt-oss","type":"heading","tags":[],"url":"/examples/endpoint/deploy-gpt-oss#test-endpoint","content":"Test Endpoint"},"954":{"id":"/examples/endpoint/deploy-gpt-oss-11","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#gpt-oss-120b-deployment-guide","content":"This guide demonstrates how to deploy OpenAI's open source GPT-OSS-120B model as an endpoint on DGX Cloud Lepton for serving inference requests. The model is deployed as a NIM which is optimized for high throughput and low latency for your specific GPU architecture. This example showcases two different paths for deploying the NIM - via the UI in a web browser, and using the CLI in a local terminal."},"955":{"id":"/examples/endpoint/deploy-gpt-oss-12","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#prerequisites","content":"To deploy the NIM, you need:"},"956":{"id":"/examples/endpoint/deploy-gpt-oss-13","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#prerequisites","content":"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."},"957":{"id":"/examples/endpoint/deploy-gpt-oss-14","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#prerequisites","content":"Access to at least one NVIDIA H100 GPU or higher on DGX Cloud Lepton."},"958":{"id":"/examples/endpoint/deploy-gpt-oss-15","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"The first path to deploying the NIM is via a web browser. To get started, navigate to the create endpoint page on the dashboard."},"959":{"id":"/examples/endpoint/deploy-gpt-oss-16","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"Specify a name for the endpoint in the Endpoint Name field such as gpt-oss-120b."},"960":{"id":"/examples/endpoint/deploy-gpt-oss-17","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"Select the node group and resource shape to use for the deployment in the Resource section. GPT-OSS-120B requires at least one GPU with a minimum of 80 GB of GPU memory. Deploying with multiple GPUs can yield higher throughput for responses."},"961":{"id":"/examples/endpoint/deploy-gpt-oss-18","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"In the NIM Configuration section, select Built-in under image and enter/select nim/openai/gpt-oss-120b:latest from the list. Additionally, specify the registry authentication and your NGC API key in the following fields:"},"962":{"id":"/examples/endpoint/deploy-gpt-oss-19","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"Registry Auth: Select one of the existing registry auth configurations you created in your workspace or create a new one with access to NGC."},"963":{"id":"/examples/endpoint/deploy-gpt-oss-20","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."},"964":{"id":"/examples/endpoint/deploy-gpt-oss-21","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-1-create-endpoint-from-nvidia-nim-via-ui","content":"GPT-OSS-120B is a large model that can take a long time to download if not already available in storage. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 99999 seconds or higher as necessary."},"965":{"id":"/examples/endpoint/deploy-gpt-oss-22","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#save-model-in-cache","content":"Since models can take hours to download depending on their size, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."},"966":{"id":"/examples/endpoint/deploy-gpt-oss-23","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#save-model-in-cache","content":"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /opt/nim/.cache. Then go to Storages and click + Mount Storage. Select your desired volume, set the from path to /opt/nim/.cache or another location for storing the cache on your volume, and set the to path to /opt/nim/.cache."},"967":{"id":"/examples/endpoint/deploy-gpt-oss-24","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#save-model-in-cache","content":"For other endpoint configurations, refer to the Endpoints documentation."},"968":{"id":"/examples/endpoint/deploy-gpt-oss-25","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#access-token","content":"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. The token appears in the UI alongside your deployed endpoint once it is ready."},"969":{"id":"/examples/endpoint/deploy-gpt-oss-26","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#access-token","content":"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."},"970":{"id":"/examples/endpoint/deploy-gpt-oss-27","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#option-2-create-endpoint-from-nvidia-nim-via-cli","content":"The second path to deploying the NIM is via the DGX Cloud Lepton CLI. To use the CLI, you must first install the Python package."},"971":{"id":"/examples/endpoint/deploy-gpt-oss-28","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#install-dgx-cloud-lepton-python-package","content":"Open a terminal and install Python 3.10 or newer if not already available. Follow the official Python documentation for instructions on installing a compatible version for your device. Then, install the DGX Cloud Lepton Python package in your terminal with:"},"972":{"id":"/examples/endpoint/deploy-gpt-oss-29","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#install-dgx-cloud-lepton-python-package","content":"This will install the Python SDK which includes the CLI."},"973":{"id":"/examples/endpoint/deploy-gpt-oss-30","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#authenticate-with-the-dgx-cloud-lepton-cli","content":"After installing the Python package, authenticate with your workspace by using lep login as shown below:"},"974":{"id":"/examples/endpoint/deploy-gpt-oss-31","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#authenticate-with-the-dgx-cloud-lepton-cli","content":"If a browser tab does not open automatically, navigate to the API tokens page. Click + Create Token in the top right corner, specify a name for your access token, and select the expiration of the token."},"975":{"id":"/examples/endpoint/deploy-gpt-oss-32","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#authenticate-with-the-dgx-cloud-lepton-cli","content":"After creating a token, it will show you your credential. Copy the credential somewhere safe as it will only be shown once. Once saved, go back to your terminal and enter it when prompted. You should now be authenticated."},"976":{"id":"/examples/endpoint/deploy-gpt-oss-33","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#authenticate-with-the-dgx-cloud-lepton-cli","content":"To validate authentication, run lep workspace list and you should see a table containing the workspace you connected to. You are now ready to use the CLI to launch endpoints."},"977":{"id":"/examples/endpoint/deploy-gpt-oss-34","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"The template for launching the NIM via the CLI is as follows:"},"978":{"id":"/examples/endpoint/deploy-gpt-oss-35","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"Some of these values may change depending on your workspace, such as:"},"979":{"id":"/examples/endpoint/deploy-gpt-oss-36","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"--resource-shape gpu.1xh200: Replace with the name of your GPU resource if different, such as gpu.1xh100 or gpu.4xh100."},"980":{"id":"/examples/endpoint/deploy-gpt-oss-37","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"--node-group my-node-group: Replace with the name of your node group to deploy the model on."},"981":{"id":"/examples/endpoint/deploy-gpt-oss-38","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"--secret NGC_API_KEY=NGC_API_KEY: Replace the second instance of NGC_API_KEY with the name of the secret that was created on DGX Cloud Lepton. For example, if your secret is named MY_NGC_KEY, the flag would be --secret NGC_API_KEY=MY_NGC_KEY."},"982":{"id":"/examples/endpoint/deploy-gpt-oss-39","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"--image-pull-secrets my-pull-secret: Replace with the name of the container registry authentication secret you created on DGX Cloud Lepton."},"983":{"id":"/examples/endpoint/deploy-gpt-oss-40","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"--public: This will make the endpoint available to anyone with the link. To require an access token, replace --public with --token <enter token here> and specify the token to require for incoming requests."},"984":{"id":"/examples/endpoint/deploy-gpt-oss-41","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#deploy-the-endpoint-via-cli","content":"After modifying and running the command above, the model deploys as an endpoint and becomes viewable in the UI."},"985":{"id":"/examples/endpoint/deploy-gpt-oss-42","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#test-endpoint","content":"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"},"986":{"id":"/examples/endpoint/deploy-gpt-oss-43","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#test-endpoint","content":"To get your <ENDPOINT_URL>, either copy the URL shown after the Endpoint: string in the UI under your deployed model, or run lep endpoint get -n <endpoint name> | grep external_endpoint. Use this URL to send requests to your model."},"987":{"id":"/examples/endpoint/deploy-gpt-oss-44","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#test-endpoint","content":"If you set your endpoint to be publicly accessible, you do not need to set the authorization token."},"988":{"id":"/examples/endpoint/deploy-gpt-oss-45","page_id":"/examples/endpoint/deploy-gpt-oss","tags":[],"type":"text","url":"/examples/endpoint/deploy-gpt-oss#test-endpoint","content":"LLMs follow the OpenAI API standard. For more information, refer to the official documentation."},"989":{"id":"/examples/endpoint/dia-tts","page_id":"/examples/endpoint/dia-tts","type":"page","content":"Dia Text-to-Speech Example","tags":[],"url":"/examples/endpoint/dia-tts"},"990":{"id":"/examples/endpoint/dia-tts-0","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts","content":"Learn how to deploy a Gradio app for interacting with the open source Dia TTS model."},"991":{"id":"/examples/endpoint/dia-tts-1","page_id":"/examples/endpoint/dia-tts","type":"heading","tags":[],"url":"/examples/endpoint/dia-tts#prerequisites","content":"Prerequisites"},"992":{"id":"/examples/endpoint/dia-tts-2","page_id":"/examples/endpoint/dia-tts","type":"heading","tags":[],"url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Build Custom Dia Container"},"993":{"id":"/examples/endpoint/dia-tts-3","page_id":"/examples/endpoint/dia-tts","type":"heading","tags":[],"url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"Run the Gradio Application"},"994":{"id":"/examples/endpoint/dia-tts-4","page_id":"/examples/endpoint/dia-tts","type":"heading","tags":[],"url":"/examples/endpoint/dia-tts#access-token","content":"Access Token"},"995":{"id":"/examples/endpoint/dia-tts-5","page_id":"/examples/endpoint/dia-tts","type":"heading","tags":[],"url":"/examples/endpoint/dia-tts#testing-endpoint","content":"Testing Endpoint"},"996":{"id":"/examples/endpoint/dia-tts-6","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts","content":"Dia is a lightweight open source text-to-speech (TTS) model developed by Nari Labs which generates realistic audio with fine-grained control over emotion and tone. Their GitHub repository includes a sample frontend application built in Gradio to make it easy to interact with the models. This guide shows how to build their custom Docker container on DGX Cloud Lepton, deploy the Gradio application as an endpoint, and interact with the model via the application."},"997":{"id":"/examples/endpoint/dia-tts-7","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#prerequisites","content":"To follow this example, you need:"},"998":{"id":"/examples/endpoint/dia-tts-8","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#prerequisites","content":"Access to at least one NVIDIA GPU on DGX Cloud Lepton."},"999":{"id":"/examples/endpoint/dia-tts-9","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#prerequisites","content":"Access to a container registry to host the custom image."},"1000":{"id":"/examples/endpoint/dia-tts-10","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#prerequisites","content":"Privileged mode enabled in the DGX Cloud Lepton workspace to build custom containers."},"1001":{"id":"/examples/endpoint/dia-tts-11","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Dia includes a Dockerfile with all dependencies for the demo application. The container must first be built and pushed to a container registry so it can be deployed as an endpoint on DGX Cloud Lepton. The container can be built directly on DGX Cloud Lepton using a dev pod. The full process for building a container using a dev pod can be found here."},"1002":{"id":"/examples/endpoint/dia-tts-12","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"To build the container, create a new dev pod with the following options:"},"1003":{"id":"/examples/endpoint/dia-tts-13","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Enter a pod name, such as dia-builder."},"1004":{"id":"/examples/endpoint/dia-tts-14","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Select a single GPU in your node group for the resource shape."},"1005":{"id":"/examples/endpoint/dia-tts-15","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Check the Enable privileged mode box in the Resource form."},"1006":{"id":"/examples/endpoint/dia-tts-16","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Select a Custom image and specify nvcr.io/nvidia/pytorch:25.06-py3 for the image."},"1007":{"id":"/examples/endpoint/dia-tts-17","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Select Custom entrypoint for the container Entrypoint."},"1008":{"id":"/examples/endpoint/dia-tts-18","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Enter the following as the entrypoint command. Replace docker tag dia-gpu <registry tag> with the location of your registry to push to. For example, if you will push the container to a private registry on nvcr.io named my-private-registry, the command will be docker tag dia-gpu nvcr.io/my-private-registry/dia-gpu. Copy the same tag for the docker push command beneath it as well, such as docker push nvcr.io/my-private-registry/dia-gpu."},"1009":{"id":"/examples/endpoint/dia-tts-19","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Click the Create button at the bottom of the page to run the dev pod and build the image. This process can take 30 minutes or longer depending on connection speed and processing power. Progress can be monitored in the job logs in the UI."},"1010":{"id":"/examples/endpoint/dia-tts-20","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#build-custom-dia-container","content":"Once the dev pod completes, the image should be available at the container registry you specified at the end of the entrypoint command."},"1011":{"id":"/examples/endpoint/dia-tts-21","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"After the container is built, the Gradio application can be deployed as an endpoint. Navigate to the create endpoint page on the dashboard."},"1012":{"id":"/examples/endpoint/dia-tts-22","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"Specify a name for the endpoint in the Endpoint Name field such as dia-app."},"1013":{"id":"/examples/endpoint/dia-tts-23","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"Select the node group and resource shape to use for the deployment in the Resource section. Given the model is small, a single GPU is sufficient."},"1014":{"id":"/examples/endpoint/dia-tts-24","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"For the Image Configuration section, specify the name of your <registry tag> that was used during the container build for the Image field. This is the location that the custom container was pushed to. If the registry requires additional authentication for pulling images, add your registry authentication key in the Private Image Registry Auth field."},"1015":{"id":"/examples/endpoint/dia-tts-25","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"The Gradio application runs on port 7860 by default. Enter 7860 in the Server Port field as this will expose the application on the URL when the endpoint is deployed. If the server port in the container is changed for any reason, the Server Port field needs to match the port that the application runs on inside the container."},"1016":{"id":"/examples/endpoint/dia-tts-26","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#run-the-gradio-application","content":"Enter python app.py for the Run Command field."},"1017":{"id":"/examples/endpoint/dia-tts-27","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#access-token","content":"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. This will be shown in the UI alongside the deployed endpoint once ready."},"1018":{"id":"/examples/endpoint/dia-tts-28","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#access-token","content":"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."},"1019":{"id":"/examples/endpoint/dia-tts-29","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#access-token","content":"Since this example is hosting a web server, it is recommended to enable public access and disable authentication."},"1020":{"id":"/examples/endpoint/dia-tts-30","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#access-token","content":"Once the form is complete, click the Create button to deploy the endpoint."},"1021":{"id":"/examples/endpoint/dia-tts-31","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#testing-endpoint","content":"You can view your endpoint in the Endpoints page. Once it's running, it will look similar to the following:"},"1022":{"id":"/examples/endpoint/dia-tts-32","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#testing-endpoint","content":"To access the application, copy the URL after Endpoint in the job description and open it in a new browser tab. This will open the Gradio frontend where you can experiment with the Dia model."},"1023":{"id":"/examples/endpoint/dia-tts-33","page_id":"/examples/endpoint/dia-tts","tags":[],"type":"text","url":"/examples/endpoint/dia-tts#testing-endpoint","content":"For more information on the Dia model, please refer to their GitHub repo."},"1024":{"id":"/examples/endpoint/multi-llm-nim","page_id":"/examples/endpoint/multi-llm-nim","type":"page","content":"Multi-LLM NIM Deployment","tags":[],"url":"/examples/endpoint/multi-llm-nim"},"1025":{"id":"/examples/endpoint/multi-llm-nim-0","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim","content":"Learn how to deploy a multi-LLM compatible NIM on DGX Cloud Lepton."},"1026":{"id":"/examples/endpoint/multi-llm-nim-1","page_id":"/examples/endpoint/multi-llm-nim","type":"heading","tags":[],"url":"/examples/endpoint/multi-llm-nim#prerequisites","content":"Prerequisites"},"1027":{"id":"/examples/endpoint/multi-llm-nim-2","page_id":"/examples/endpoint/multi-llm-nim","type":"heading","tags":[],"url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Create Endpoint from NVIDIA NIM"},"1028":{"id":"/examples/endpoint/multi-llm-nim-3","page_id":"/examples/endpoint/multi-llm-nim","type":"heading","tags":[],"url":"/examples/endpoint/multi-llm-nim#saving-model-in-cache","content":"Saving Model in Cache"},"1029":{"id":"/examples/endpoint/multi-llm-nim-4","page_id":"/examples/endpoint/multi-llm-nim","type":"heading","tags":[],"url":"/examples/endpoint/multi-llm-nim#access-token","content":"Access Token"},"1030":{"id":"/examples/endpoint/multi-llm-nim-5","page_id":"/examples/endpoint/multi-llm-nim","type":"heading","tags":[],"url":"/examples/endpoint/multi-llm-nim#testing-endpoint","content":"Testing Endpoint"},"1031":{"id":"/examples/endpoint/multi-llm-nim-6","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim","content":"Multi-LLM compatible NIM is a single container that supports a broad range of models for flexible deployment. The multi-LLM NIM container is recommended for models that NVIDIA has not yet created a specific container for. Models can be loaded from local storage, Hugging Face, or NGC. Refer to the official documentation for more information about NVIDIA NIM for LLMs."},"1032":{"id":"/examples/endpoint/multi-llm-nim-7","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#prerequisites","content":"To run the Multi-LLM NIM, you need:"},"1033":{"id":"/examples/endpoint/multi-llm-nim-8","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#prerequisites","content":"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."},"1034":{"id":"/examples/endpoint/multi-llm-nim-9","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#prerequisites","content":"Access to at least one NVIDIA A100 GPU or higher on DGX Cloud Lepton (actual GPU requirements depend on the model being deployed)."},"1035":{"id":"/examples/endpoint/multi-llm-nim-10","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#prerequisites","content":"A Hugging Face access token."},"1036":{"id":"/examples/endpoint/multi-llm-nim-11","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Navigate to the create endpoint page on the dashboard."},"1037":{"id":"/examples/endpoint/multi-llm-nim-12","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Specify a name for the endpoint in the Endpoint Name field."},"1038":{"id":"/examples/endpoint/multi-llm-nim-13","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Select the model to deploy by either loading the model from Hugging Face or from storage attached to your node group. To load a model hosted on Hugging Face, click the Load from Hugging Face button and enter or select the model repo, such as meta-llama/Llama-3.1-8B-Instruct. If deploying a gated model, select your Hugging Face token from the UI or add one using the + Add token button."},"1039":{"id":"/examples/endpoint/multi-llm-nim-14","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"To load a model from storage, click the Load from Storage button and specify the volume to mount and the path to the model in the mounted storage, such as /path/to/my/model."},"1040":{"id":"/examples/endpoint/multi-llm-nim-15","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Select the node group and resource shape to use for the deployment in the Resource section. The number of GPUs will vary depending on the model, but a general rule of thumb is to have at least 2 GB of GPU memory for every billion parameters for a model in BF16 precision. For example, an 8 billion parameter model will need approximately 16 GB of GPU memory to deploy."},"1041":{"id":"/examples/endpoint/multi-llm-nim-16","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"In the NIM Configuration section, specify the registry authentication and your NGC API key."},"1042":{"id":"/examples/endpoint/multi-llm-nim-17","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Registry Auth: Select one of the existing registry auth configurations you created in your workspace or create a new one with access to NGC."},"1043":{"id":"/examples/endpoint/multi-llm-nim-18","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."},"1044":{"id":"/examples/endpoint/multi-llm-nim-19","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#create-endpoint-from-nvidia-nim","content":"Very large models can take a long time to download if not already available in storage. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 7200 seconds or higher as necessary."},"1045":{"id":"/examples/endpoint/multi-llm-nim-20","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#saving-model-in-cache","content":"Since models can take hours to download depending on their size, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."},"1046":{"id":"/examples/endpoint/multi-llm-nim-21","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#saving-model-in-cache","content":"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /opt/nim/.cache. Then go to Storage and click + Mount Storage. Select your desired volume, set the from path to /opt/nim/.cache or another location for storing cache on storage, and set the to path to /opt/nim/.cache."},"1047":{"id":"/examples/endpoint/multi-llm-nim-22","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#saving-model-in-cache","content":"This should now look like the image below."},"1048":{"id":"/examples/endpoint/multi-llm-nim-23","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#saving-model-in-cache","content":"For other endpoint configurations, refer to the Endpoints documentation."},"1049":{"id":"/examples/endpoint/multi-llm-nim-24","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#access-token","content":"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. This will be shown in the UI alongside the deployed endpoint once ready."},"1050":{"id":"/examples/endpoint/multi-llm-nim-25","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#access-token","content":"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."},"1051":{"id":"/examples/endpoint/multi-llm-nim-26","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#testing-endpoint","content":"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"},"1052":{"id":"/examples/endpoint/multi-llm-nim-27","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#testing-endpoint","content":"If you set your endpoint to be publicly accessible, you do not need to set the authorization token."},"1053":{"id":"/examples/endpoint/multi-llm-nim-28","page_id":"/examples/endpoint/multi-llm-nim","tags":[],"type":"text","url":"/examples/endpoint/multi-llm-nim#testing-endpoint","content":"LLMs follow the OpenAI API standard. For more information, refer to the official documentation."},"1054":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"page","content":"Fine-Tuning an LLM for Math Reasoning","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning"},"1055":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-0","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning","content":"Use the fine-tuning tool to add math reasoning capabilities to an LLM"},"1056":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-1","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#introduction","content":"Introduction"},"1057":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-2","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#requirements","content":"Requirements"},"1058":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-3","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#steps","content":"Steps"},"1059":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-4","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Model Preparation"},"1060":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-5","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Configuring a Fine-Tuning Job"},"1061":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-6","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"Monitoring Fine-Tuning"},"1062":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-7","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#final-thoughts","content":"Final Thoughts"},"1063":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-8","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","type":"heading","tags":[],"url":"/examples/fine-tuning/fine-tune-open-math-reasoning#citations","content":"Citations"},"1064":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-9","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning","content":"This guide outlines how to use the Fine-tuning tool on DGX Cloud Lepton to add math reasoning capabilities to the Qwen2.5-Math-1.5B model to improve its accuracy in solving mathematical problems."},"1065":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-10","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#introduction","content":"Large Language Models (LLMs) have continued to demonstrate strong capabilities in a wide range of domains from math to science to creative writing and everything in between. LLMs are typically trained on a very large corpus of diverse text and gain general understanding on a broad list of topics. While these LLMs are valuable out-of-the-box for many use-cases, they usually require focused training on specific domains where accuracy is key. This is where fine-tuning comes in."},"1066":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-11","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#introduction","content":"Fine-tuning is the process of taking a base model and adapting it to learn more about a domain or respond in a specific manner to prompts. A common example is to take an open source model and fine-tune it to improve its accuracy in math benchmarks which is what this guide showcases."},"1067":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-12","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#introduction","content":"This document recreates the OpenMathReasoning model process as proposed by NVIDIA research to add math reasoning capabilities to an open source LLM (Moshkov et al., 2025). The specific example uses the open source Qwen2.5-Math-1.5B model as a base and fine-tunes it using NVIDIA's OpenMathReasoning dataset. This dataset includes millions of samples of math problems taken from the community with reasoning traces generated by DeepSeek-R1 and Qwen2.5-72B-Math-Instruct. The research team at NVIDIA found that running Supervised Fine-Tuning (SFT) with this dataset against top open-source models yielded state-of-the-art performance in various math benchmarks."},"1068":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-13","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#introduction","content":"This step-by-step guide shows how to use the Fine-tuning tool on DGX Cloud Lepton to add reasoning capabilities to Qwen2.5-Math-1.5B."},"1069":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-14","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#requirements","content":"A DGX Cloud Lepton workspace with a shared storage volume"},"1070":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-15","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#requirements","content":"Eight (8) GPUs with 80+ GB of GPU memory"},"1071":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-16","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"As stated in the paper, Qwen2.5-Math-1.5B only has a context length of 4,096 tokens which is insufficient for reasoning models which typically generate several thousands of output tokens. To support the longer reasoning traces, we need to manually update the base model to increase its output sequence length."},"1072":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-17","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"The paper follows the advice from this thread which found that increasing the output token count and the RoPE scaling did not have a significant impact on the model's performance in tasks. To replicate this, we must download the base model from Hugging Face and modify the config so it can handle longer sequences. We will use a Batch Job for this process."},"1073":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-18","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"To create a modified base model, open the Batch Jobs page and click the green Create Job button in the top right of the page."},"1074":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-19","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"In the creation form that opens up, enter the following details:"},"1075":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-20","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Job Name: Enter a name for the job, such as qwen-modification."},"1076":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-21","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Node Group: Select the node group to run the job in. This must be the same as the node group you will use for fine-tuning as it must share storage."},"1077":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-22","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Resource Shape: This process is mainly network-bound and does not require much CPU resources and does not require a GPU. A cpu.medium resource should be sufficient, or a single-GPU resource if CPU-only resource shapes are not available in your node group."},"1078":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-23","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Worker: Enter 1 for a single worker."},"1079":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-24","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Container Image: The default base container should be sufficient. In this example, nvcr.io/nvidia/pytorch:25.08-py3 is used."},"1080":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-25","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Run Command: Enter the following for the command. Note that your STORAGE_DIR might change depending on where you mount storage (see Storage item below):"},"1081":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-26","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Storage: Mount shared storage in the jobs to save the checkpoint after the job finishes. Select the shared storage in your node group and set your mount path to something like /storage. Your STORAGE_DIR in the Run Command above must start with the mount path listed here."},"1082":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-27","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"An example of the container configuration section is shown in the image below."},"1083":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-28","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"Once the form is complete, click the green Create button at the bottom of the page to queue the job once resources are available."},"1084":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-29","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#model-preparation","content":"When the job gets scheduled, it will pull the selected container with storage mounted at the designated location. The script will then download the Qwen2.5-Math-1.5B model to the mounted storage and modify the max_position_embeddings and rope_theta values in the downloaded model's config file as well as max_new_tokens in the generation config to support 128K context length. This modified model will be available in the fine-tuning jobs when loading from storage."},"1085":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-30","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"With the modified model saved to local storage, it is time to launch the fine-tuning job. The fine-tuning tool can be accessed from the explore page in your DGX Cloud Lepton workspace. Click the Fine-tuning box or navigate directly to the fine-tuning page."},"1086":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-31","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Create a new fine-tuning job by selecting the green Create Fine-tune button in the Fine-tuning job page."},"1087":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-32","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"This will open a new form to specify all of the settings for the fine-tuning job. The first prompts the user to select their fine-tuning method, either Supervised Fine-Tuning (SFT) or LoRA, a Parameter-Efficient Fine-Tuning (PEFT) technique. SFT updates the full model weights based on an instruction-following dataset while PEFT adds additional weights to the bottom of the model while freezing the base model weights. For this example, we will select SFT as we want to train the model to specifically respond with math reasoning traces for all prompts."},"1088":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-33","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Add responses to the Name and Resources sections at the top of the form:"},"1089":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-34","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Name: Give the fine-tuning job a name, such as qwen25-open-math-reasoning."},"1090":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-35","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Node Group: Select the same node group that was used for the model preparation in the previous section."},"1091":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-36","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Resource Shape: At least eight GPUs with 80+ GB of GPU memory is suggested for running SFT due to the longer sequence lengths of the reasoning dataset. Adding additional workers for multi-node fine-tuning will increase training throughput but one worker is sufficient for this example."},"1092":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-37","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Storage: Select the same storage volume that was used for the model preparation in the previous section. While you can specify a different path to mount the storage under in the container, it is easiest to use the same mount path as earlier, such as /storage."},"1093":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-38","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"This section should look similar to the following image once complete:"},"1094":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-39","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"For the Model field, select Load from storage and enter the path to where the modified model was saved during the preparation step earlier. For example, if your model was saved to /storage/models per the STORAGE_DIR variable above, enter /storage/models/qwen2.5-math-1.5b as that is the absolute path to where the model is saved. This must start with the storage mount path (ie. /storage) and end with the downloaded model path name (ie. qwen2.5-math-1.5b) as in the image below."},"1095":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-40","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Next we need to specify the Training Dataset which is where we input the OpenMathReasoning dataset listed in the paper."},"1096":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-41","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"While inspecting the dataset on Hugging Face, there are a few splits which contain subsets of the dataset for specific tasks, such as cot for chain-of-thought, tir for tool-integrated reasoning, genselect for selecting promising responses, and additional_problems for a list of more problems. For this guide, we will just select the cot split which should yield noticeable improvements in downstream accuracy. As stated in the paper, each of these stages can be stacked on top of each other. You are welcome to follow these steps again to fine-tune with additional splits."},"1097":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-42","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Additionally, the dataset doesn't have columns named Question and Answer like the fine-tuning tool expects, so we will use the Dataset Mapping concept to rename the columns in the datasets to something that the fine-tuning code understands. In particular, we will take the problem field as the question or input prompt, and the generated_solution field as the answer that we want the model to learn to generate for the prompt."},"1098":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-43","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Enter this information under Training Dataset, and select Load from Hugging Face. Next, enter nvidia/OpenMathReasoning for the dataset, cot for the dataset split, problem for the Question field, and generated_solution for the Answer field as shown in the image below."},"1099":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-44","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"For the validation dataset, we could use a subset of the OpenMathReasoning dataset, but given this has millions of samples, even a small percentage of the dataset will result in a lot of validation data to process, significantly increasing the total training time. Instead, we will use a math benchmark which is significantly smaller but also more indicative of how the model is learning compared to real-world benchmarks. Specifically, we will use the AIME24 dataset which contains 30 math problems with reasoning traces and answers as output."},"1100":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-45","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Under Validation Dataset, select Load from Hugging Face and enter Maxwell-Jia/AIME_2024 for the dataset, train for the split, Problem for the Question field, and Solution for the Answer field as shown in the image below. Note that the dataset mapping is case-sensitive."},"1101":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-46","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Neither the datasets nor the model are gated on Hugging Face, but if any of them were, you would need to add your Hugging Face Token in the indicated field and accept any licenses on the model or dataset's page on Hugging Face."},"1102":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-47","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Next, enter the location for the Checkpoint Output Path. This is where the checkpoints will be saved. This must start with the storage mount path, such as /storage. Enter a location such as /storage/my-checkpoints/qwen2.5-math-1.5b."},"1103":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-48","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Lastly, we need to enter the hyperparameters for the job. Following the guidance of the paper, we can set the following parameters while keeping the other ones default."},"1104":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-49","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Epochs: Enter 3 for doing three full passes of the training dataset. While the paper calls for six epochs, that included the other two dataset splits which we did not include in this example. Three epochs will be enough to yield the same chain-of-thought results as the paper."},"1105":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-50","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Val Every Steps: Enter 100 to run a validation pass every 100 steps. On eight H100 GPUs, this will run validation approximately every 45 minutes."},"1106":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-51","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Context Parallelism Size: Enter 2 to split the input sequences across two GPUs, each to reduce the GPU memory requirement for processing longer sequence lengths."},"1107":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-52","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Global Batch Size: Enter 1024 to process 1,024 sequences per training pass across the cluster. The paper used a batch size of 256 but also packed their sequences by a factor of 4x, effectively yielding a batch size of 1024."},"1108":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-53","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Learning Rate: Enter 0.0003 which is the max learning rate used in the paper."},"1109":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-54","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Min Learning Rate: Enter 0.0000003 which is the minimum learning rate used in the paper that the optimizer will decay to."},"1110":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-55","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Checkpoint Every Steps: Enter 500 to save a checkpoint every 500 steps. This will save a checkpoint of the current model weights to the checkpoint directory specified earlier. On eight H100 GPUs, this will save a checkpoint approximately once every 6 hours."},"1111":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-56","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"The completed form should resemble the following image:"},"1112":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-57","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Lastly, the fine-tuning feature supports Weights and Biases (W&B) for tracking training metrics. If you would like to track your fine-tuning job with W&B, open the Advanced Configuration tab and enter your W&B project and job name that you would like the fine-tuning to be tracked as, and enter your W&B API token in the WandB API Key field."},"1113":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-58","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#configuring-a-fine-tuning-job","content":"Once the form is fully completed, click the green Create button at the bottom of the page to schedule the fine-tuning job."},"1114":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-59","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"After creating your fine-tuning job, you will be taken back to the fine-tuning jobs list where your job will be shown at the top."},"1115":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-60","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"When resources are available, the job will get scheduled and the image will be pulled from the container registry and launch the fine-tuning job once ready."},"1116":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-61","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"Clicking the job will open the job details page which includes more information about the job including listing the settings that were specified during creation."},"1117":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-62","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"You can monitor the job's progress by clicking the Logs tab in the job detail page. Given the large size of the dataset, it can take 15 minutes or more for the dataset to fully load and process. During this time you might not see log output for a while so don't be alarmed if your logs appear to be stuck after this state:"},"1118":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-63","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"Once the dataset has been prepared, training will begin. You will know it has started when you see lines similar to the following:"},"1119":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-64","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"In order, this prints out:"},"1120":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-65","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The number of forward passes or steps that have occurred."},"1121":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-66","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The current epoch in the dataset (zero-based)."},"1122":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-67","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The latest training loss."},"1123":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-68","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The gradient normalization value."},"1124":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-69","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The learning rate used during the indicated step."},"1125":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-70","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The amount of GPU memory used per GPU during the indicated step."},"1126":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-71","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The number of tokens processed per second."},"1127":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-72","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The total number of tokens that were processed in that step."},"1128":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-73","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"As the model is training, you should find that your training loss value decays over time. In our testing, the loss was 0.33 after 100 steps, 0.31 after 200 steps, 0.30 after 300 steps, and so on."},"1129":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-74","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"Every 500 steps, a checkpoint will be generated and saved in the specified directory. To deploy the checkpoint for inference, look for the model/consolidated sub-directory inside the checkpoint directory as this can be deployed directly as an Endpoint on DGX Cloud Lepton."},"1130":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-75","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#monitoring-fine-tuning","content":"The training process should take approximately five days to complete on eight H100 GPUs."},"1131":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-76","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#final-thoughts","content":"If you made it this far, congratulations! You have now fine-tuned an open source model to add math reasoning capabilities which make it more accurate in math-related benchmarks. From here, you can deploy the model as an Endpoint on DGX Cloud Lepton to test it out and send requests, or you could further fine-tune it with additional dataset splits and upload the final result to Hugging Face."},"1132":{"id":"/examples/fine-tuning/fine-tune-open-math-reasoning-77","page_id":"/examples/fine-tuning/fine-tune-open-math-reasoning","tags":[],"type":"text","url":"/examples/fine-tuning/fine-tune-open-math-reasoning#citations","content":"Moshkov, I., Hanley, D., Sorokin, I., Toshniwal, S., Henkel, C., Schifferer, B., Du, W., Gitman, I. (2025). AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.16891"},"1133":{"id":"/features/batch-jobs/configurations","page_id":"/features/batch-jobs/configurations","type":"page","content":"Batch Job Configurations","tags":[],"url":"/features/batch-jobs/configurations"},"1134":{"id":"/features/batch-jobs/configurations-0","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations","content":"Learn how to configure your batch job in DGX Cloud Lepton."},"1135":{"id":"/features/batch-jobs/configurations-1","page_id":"/features/batch-jobs/configurations","type":"heading","tags":[],"url":"/features/batch-jobs/configurations#resource","content":"Resource"},"1136":{"id":"/features/batch-jobs/configurations-2","page_id":"/features/batch-jobs/configurations","type":"heading","tags":[],"url":"/features/batch-jobs/configurations#container","content":"Container"},"1137":{"id":"/features/batch-jobs/configurations-3","page_id":"/features/batch-jobs/configurations","type":"heading","tags":[],"url":"/features/batch-jobs/configurations#advanced","content":"Advanced"},"1138":{"id":"/features/batch-jobs/configurations-4","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations","content":"When creating a batch job, you can configure the resource, container, and additional options."},"1139":{"id":"/features/batch-jobs/configurations-5","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Node Group: Select one or more node groups to determine what resources this job can use."},"1140":{"id":"/features/batch-jobs/configurations-6","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Priority: Set the priority of the job. Defaults to Medium (4). If the specified node group has limited resources, you can set the priority accordingly to get higher-priority resource allocation."},"1141":{"id":"/features/batch-jobs/configurations-7","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Resource shape: The instance type that the job will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details."},"1142":{"id":"/features/batch-jobs/configurations-8","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Nodes: Defaults to no specific nodes, but you can specify the nodes you want to launch the job on."},"1143":{"id":"/features/batch-jobs/configurations-9","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Can preempt lower priority workload: Whether the job can preempt lower-priority workloads. Defaults to false."},"1144":{"id":"/features/batch-jobs/configurations-10","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Can be preempted by higher priority workload: Whether the job can be preempted by higher-priority workloads. Defaults to false."},"1145":{"id":"/features/batch-jobs/configurations-11","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#resource","content":"Workers: The number of workers to launch for the job. Defaults to 1."},"1146":{"id":"/features/batch-jobs/configurations-12","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#container","content":"Image: The container image that will be used to create the job. You can choose from the default image lists or use your own custom image."},"1147":{"id":"/features/batch-jobs/configurations-13","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#container","content":"Private image registry auth (optional): If you are using a private image, specify the image registry authentication."},"1148":{"id":"/features/batch-jobs/configurations-14","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#container","content":"Run Command: The command to run when the container starts."},"1149":{"id":"/features/batch-jobs/configurations-15","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#container","content":"Container Ports: The ports that the container will listen on. In this field, you can add multiple ports, and each port can be specified with a protocol (TCP, UDP, or SCTP) and a port number."},"1150":{"id":"/features/batch-jobs/configurations-16","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#container","content":"Log Collection: Whether to collect the logs from the container. Follows the workspace-level setting by default."},"1151":{"id":"/features/batch-jobs/configurations-17","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Environment Variables: Environment variables are key-value pairs that are passed to the job. They are automatically set as environment variables in the job container, so the runtime can refer to them as needed. Refer to this guide for more details."},"1152":{"id":"/features/batch-jobs/configurations-18","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Your defined environment variables should not start with the name prefix LEPTON_, as this prefix is reserved for predefined environment variables. The following environment variables are predefined and will be available in the job:"},"1153":{"id":"/features/batch-jobs/configurations-19","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"LEPTON_JOB_NAME: The name of the job"},"1154":{"id":"/features/batch-jobs/configurations-20","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"LEPTON_RESOURCE_ACCELERATOR_TYPE: The resource accelerator type of the job"},"1155":{"id":"/features/batch-jobs/configurations-21","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Storage: Mount storage for the job container. Refer to this guide for more details."},"1156":{"id":"/features/batch-jobs/configurations-22","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Shared Memory: The size of the shared memory that will be allocated to the container."},"1157":{"id":"/features/batch-jobs/configurations-23","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Max replica failure retry: Maximum number of times to retry a failed replica. Defaults to zero."},"1158":{"id":"/features/batch-jobs/configurations-24","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Max job failure retry: Maximum number of failure restarts of the entire job."},"1159":{"id":"/features/batch-jobs/configurations-25","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Disable retry when program error occurs: If enabled, the job will not be retried if a program error is detected in the logs."},"1160":{"id":"/features/batch-jobs/configurations-26","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Archive time: The time to keep the job's logs and artifacts after the job is completed. Defaults to 3 days."},"1161":{"id":"/features/batch-jobs/configurations-27","page_id":"/features/batch-jobs/configurations","tags":[],"type":"text","url":"/features/batch-jobs/configurations#advanced","content":"Visibility: Specifies the visibility of the job. If set to private, only the creator can access the job. If set to public, all users in the workspace can access the job."},"1162":{"id":"/features/batch-jobs/predefined-env-vars","page_id":"/features/batch-jobs/predefined-env-vars","type":"page","content":"Environment Variables Reference","tags":[],"url":"/features/batch-jobs/predefined-env-vars"},"1163":{"id":"/features/batch-jobs/predefined-env-vars-0","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars","content":"This page lists all the environment variables that can be used on DGX Cloud Lepton."},"1164":{"id":"/features/batch-jobs/predefined-env-vars-1","page_id":"/features/batch-jobs/predefined-env-vars","type":"heading","tags":[],"url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Basic Environment Variables"},"1165":{"id":"/features/batch-jobs/predefined-env-vars-2","page_id":"/features/batch-jobs/predefined-env-vars","type":"heading","tags":[],"url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Reference for torch.distributed.launch"},"1166":{"id":"/features/batch-jobs/predefined-env-vars-3","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"When a batch job is launched on DGX Cloud Lepton, the following environment variable names are automatically set, with the values corresponding to the job configurations."},"1167":{"id":"/features/batch-jobs/predefined-env-vars-4","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Environment Variable Name"},"1168":{"id":"/features/batch-jobs/predefined-env-vars-5","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Description"},"1169":{"id":"/features/batch-jobs/predefined-env-vars-6","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Sample Value"},"1170":{"id":"/features/batch-jobs/predefined-env-vars-7","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_RESOURCE_ACCELERATOR_NUM"},"1171":{"id":"/features/batch-jobs/predefined-env-vars-8","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Number of hardware accelerators allocated"},"1172":{"id":"/features/batch-jobs/predefined-env-vars-9","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"1"},"1173":{"id":"/features/batch-jobs/predefined-env-vars-10","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_WORKER_HOSTNAME_PREFIX"},"1174":{"id":"/features/batch-jobs/predefined-env-vars-11","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Prefix used for naming worker hostnames"},"1175":{"id":"/features/batch-jobs/predefined-env-vars-12","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"worker"},"1176":{"id":"/features/batch-jobs/predefined-env-vars-13","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_WORKSPACE_ID"},"1177":{"id":"/features/batch-jobs/predefined-env-vars-14","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Identifier for the current workspace"},"1178":{"id":"/features/batch-jobs/predefined-env-vars-15","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"prod01awsuswest"},"1179":{"id":"/features/batch-jobs/predefined-env-vars-16","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_RESOURCE_ACCELERATOR_TYPE"},"1180":{"id":"/features/batch-jobs/predefined-env-vars-17","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Type of hardware accelerator used"},"1181":{"id":"/features/batch-jobs/predefined-env-vars-18","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"NVIDIA-A100-80GB"},"1182":{"id":"/features/batch-jobs/predefined-env-vars-19","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_WORKER_ID"},"1183":{"id":"/features/batch-jobs/predefined-env-vars-20","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Unique identifier for the current worker"},"1184":{"id":"/features/batch-jobs/predefined-env-vars-21","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"env-job-98bw-0-2nm7s"},"1185":{"id":"/features/batch-jobs/predefined-env-vars-22","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_FAILURE_COUNT"},"1186":{"id":"/features/batch-jobs/predefined-env-vars-23","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Number of failed job attempts"},"1187":{"id":"/features/batch-jobs/predefined-env-vars-24","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"0"},"1188":{"id":"/features/batch-jobs/predefined-env-vars-25","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_TOTAL_WORKERS"},"1189":{"id":"/features/batch-jobs/predefined-env-vars-26","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Total number of workers assigned to the job"},"1190":{"id":"/features/batch-jobs/predefined-env-vars-27","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"1"},"1191":{"id":"/features/batch-jobs/predefined-env-vars-28","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_WORKER_INDEX"},"1192":{"id":"/features/batch-jobs/predefined-env-vars-29","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Index of the current worker within the job"},"1193":{"id":"/features/batch-jobs/predefined-env-vars-30","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"0"},"1194":{"id":"/features/batch-jobs/predefined-env-vars-31","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_SUBDOMAIN"},"1195":{"id":"/features/batch-jobs/predefined-env-vars-32","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Subdomain name assigned to the job service"},"1196":{"id":"/features/batch-jobs/predefined-env-vars-33","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"env-job-98bw-job-svc"},"1197":{"id":"/features/batch-jobs/predefined-env-vars-34","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_SERVICE_PREFIX"},"1198":{"id":"/features/batch-jobs/predefined-env-vars-35","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Prefix used for naming services related to the job"},"1199":{"id":"/features/batch-jobs/predefined-env-vars-36","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"env-job-98bw"},"1200":{"id":"/features/batch-jobs/predefined-env-vars-37","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_WORKER_PREFIX"},"1201":{"id":"/features/batch-jobs/predefined-env-vars-38","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Prefix used for naming services"},"1202":{"id":"/features/batch-jobs/predefined-env-vars-39","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"worker"},"1203":{"id":"/features/batch-jobs/predefined-env-vars-40","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_JOB_NAME"},"1204":{"id":"/features/batch-jobs/predefined-env-vars-41","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Name assigned to the job"},"1205":{"id":"/features/batch-jobs/predefined-env-vars-42","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"env-job-98bw"},"1206":{"id":"/features/batch-jobs/predefined-env-vars-43","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"LEPTON_VIRTUAL_ENV"},"1207":{"id":"/features/batch-jobs/predefined-env-vars-44","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"Path to the Python virtual environment"},"1208":{"id":"/features/batch-jobs/predefined-env-vars-45","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#basic-environment-variables","content":"/opt/lepton/venv"},"1209":{"id":"/features/batch-jobs/predefined-env-vars-46","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Using the DGX Cloud Lepton environment variables, you can construct the environment variables for various AI training framework abstractions."},"1210":{"id":"/features/batch-jobs/predefined-env-vars-47","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"For example, if you use torch.distributed.launch, the required environment variables can be set up as:"},"1211":{"id":"/features/batch-jobs/predefined-env-vars-48","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Environment Variable Name"},"1212":{"id":"/features/batch-jobs/predefined-env-vars-49","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Meaning"},"1213":{"id":"/features/batch-jobs/predefined-env-vars-50","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Construction Method"},"1214":{"id":"/features/batch-jobs/predefined-env-vars-51","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"MASTER_ADDR"},"1215":{"id":"/features/batch-jobs/predefined-env-vars-52","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Address of the master node for distributed training"},"1216":{"id":"/features/batch-jobs/predefined-env-vars-53","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"${LEPTON_JOB_WORKER_PREFIX}-0.${LEPTON_SUBDOMAIN}"},"1217":{"id":"/features/batch-jobs/predefined-env-vars-54","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"MASTER_PORT"},"1218":{"id":"/features/batch-jobs/predefined-env-vars-55","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Port for master node communication"},"1219":{"id":"/features/batch-jobs/predefined-env-vars-56","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"29400"},"1220":{"id":"/features/batch-jobs/predefined-env-vars-57","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"WORLD_SIZE"},"1221":{"id":"/features/batch-jobs/predefined-env-vars-58","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Total number of workers assigned to the job"},"1222":{"id":"/features/batch-jobs/predefined-env-vars-59","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"${LEPTON_JOB_TOTAL_WORKERS}"},"1223":{"id":"/features/batch-jobs/predefined-env-vars-60","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"WORKER_ADDRS"},"1224":{"id":"/features/batch-jobs/predefined-env-vars-61","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Addresses for worker nodes"},"1225":{"id":"/features/batch-jobs/predefined-env-vars-62","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"(seq 1 $((LEPTON_JOB_TOTAL_WORKERS - 1)) | xargs -I {} echo ${LEPTON_JOB_WORKER_PREFIX}-{}.${LEPTON_SUBDOMAIN} | paste -sd ',' -)"},"1226":{"id":"/features/batch-jobs/predefined-env-vars-63","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"NODE_RANK"},"1227":{"id":"/features/batch-jobs/predefined-env-vars-64","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Rank of the current worker node in the distributed setup"},"1228":{"id":"/features/batch-jobs/predefined-env-vars-65","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"${LEPTON_JOB_WORKER_INDEX}"},"1229":{"id":"/features/batch-jobs/predefined-env-vars-66","page_id":"/features/batch-jobs/predefined-env-vars","tags":[],"type":"text","url":"/features/batch-jobs/predefined-env-vars#reference-for-torchdistributedlaunch","content":"Here is an example of a short script that sets up the environment variables for a job:"},"1230":{"id":"/features/batch-jobs/templates","page_id":"/features/batch-jobs/templates","type":"page","content":"Templates","tags":[],"url":"/features/batch-jobs/templates"},"1231":{"id":"/features/batch-jobs/templates-0","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates","content":"This page explains MPI and Torchrun templates in DGX Cloud Lepton."},"1232":{"id":"/features/batch-jobs/templates-1","page_id":"/features/batch-jobs/templates","type":"heading","tags":[],"url":"/features/batch-jobs/templates#mpi-template","content":"MPI Template"},"1233":{"id":"/features/batch-jobs/templates-2","page_id":"/features/batch-jobs/templates","type":"heading","tags":[],"url":"/features/batch-jobs/templates#torchrun-template","content":"Torchrun Template"},"1234":{"id":"/features/batch-jobs/templates-3","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates","content":"DGX Cloud Lepton provides two templates to help you get started quickly:"},"1235":{"id":"/features/batch-jobs/templates-4","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates","content":"MPI: For distributed computing with MPI"},"1236":{"id":"/features/batch-jobs/templates-5","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates","content":"Torchrun: For PyTorch distributed training"},"1237":{"id":"/features/batch-jobs/templates-6","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates","content":"To use these templates, select the Template option at the top of the create job page."},"1238":{"id":"/features/batch-jobs/templates-7","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"To use the MPI template, follow the steps below:"},"1239":{"id":"/features/batch-jobs/templates-8","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Go to the Batch Jobs tab and click Create Job."},"1240":{"id":"/features/batch-jobs/templates-9","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Select MPI at the top of the create job page."},"1241":{"id":"/features/batch-jobs/templates-10","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Fill in the MPI Command, job name, and other configurations."},"1242":{"id":"/features/batch-jobs/templates-11","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"MPI Command: The template provides a default command. You can customize it using the predefined environment variables provided by the MPI template."},"1243":{"id":"/features/batch-jobs/templates-12","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"In this documentation, we use the term 'primary' instead of 'master' to align with modern terminology. Please note that the UI, commands, and environment variables may still reference 'master'.\nThe MPI template generates a bash script that sets up the MPI runtime environment. The following environment variables are automatically available in your job:"},"1244":{"id":"/features/batch-jobs/templates-13","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"MASTER_ADDR: Hostname of the primary node (first node)"},"1245":{"id":"/features/batch-jobs/templates-14","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"MASTER_IP: IP address of the primary node"},"1246":{"id":"/features/batch-jobs/templates-15","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"THIS_ADDR: Hostname of the current node"},"1247":{"id":"/features/batch-jobs/templates-16","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"HOST_ADDRS: Comma-separated list of all node hostnames"},"1248":{"id":"/features/batch-jobs/templates-17","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"HOST_IPS: Comma-separated list of all node IP addresses"},"1249":{"id":"/features/batch-jobs/templates-18","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"NNODES: Total workers for the current job"},"1250":{"id":"/features/batch-jobs/templates-19","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"NODE_RANK: Rank of the current node among all workers"},"1251":{"id":"/features/batch-jobs/templates-20","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"NGPUS: Number of GPUs on the current node"},"1252":{"id":"/features/batch-jobs/templates-21","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"HOSTFILE: Path to the file containing all node IPs (default: \"/tmp/hostfile.txt\")"},"1253":{"id":"/features/batch-jobs/templates-22","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Job Name: Enter a descriptive name like mpi-job or your preferred identifier."},"1254":{"id":"/features/batch-jobs/templates-23","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Other Configurations: Use the default settings or customize as needed. See Batch Job configurations for detailed options."},"1255":{"id":"/features/batch-jobs/templates-24","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#mpi-template","content":"Click Create to launch your job."},"1256":{"id":"/features/batch-jobs/templates-25","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"The Torchrun template streamlines PyTorch distributed training. To use it:"},"1257":{"id":"/features/batch-jobs/templates-26","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Go to the Batch Jobs tab and click Create Job."},"1258":{"id":"/features/batch-jobs/templates-27","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Select Torchrun at the top of the create job page."},"1259":{"id":"/features/batch-jobs/templates-28","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Fill in the Torchrun Command, job name, and other configurations."},"1260":{"id":"/features/batch-jobs/templates-29","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Torchrun Command: The template provides a default command. You can customize it using the predefined environment variables."},"1261":{"id":"/features/batch-jobs/templates-30","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"In this documentation, we use the term 'primary' instead of 'master' to align with modern terminology. Please note that the UI, commands, and environment variables may still reference 'master'.\nThe Torchrun template generates a bash script that predefines environment variables for your job:"},"1262":{"id":"/features/batch-jobs/templates-31","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"PET_MASTER_ADDR: Hostname of the primary node (first node)"},"1263":{"id":"/features/batch-jobs/templates-32","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"MASTER_IP: IP address of the primary node"},"1264":{"id":"/features/batch-jobs/templates-33","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"THIS_ADDR: Hostname of the current node"},"1265":{"id":"/features/batch-jobs/templates-34","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"PET_NNODES: Total number of workers for the current job"},"1266":{"id":"/features/batch-jobs/templates-35","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"PET_NODE_RANK: Rank of the current node among all workers"},"1267":{"id":"/features/batch-jobs/templates-36","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"PET_NPROC_PER_NODE: Number of GPUs on the current node\nEnvironment variables prefixed with PET_ are automatically parsed as corresponding torchrun parameters. For example, PET_MASTER_ADDR becomes --master_addr. You can override these by explicitly setting parameters in your torchrun command."},"1268":{"id":"/features/batch-jobs/templates-37","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Job Name: Enter a descriptive name like torchrun-job or your preferred identifier."},"1269":{"id":"/features/batch-jobs/templates-38","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Other Configurations: Use the default settings or customize as needed. Refer to Batch Job configurations for detailed options."},"1270":{"id":"/features/batch-jobs/templates-39","page_id":"/features/batch-jobs/templates","tags":[],"type":"text","url":"/features/batch-jobs/templates#torchrun-template","content":"Click Create to launch your job."},"1271":{"id":"/features/clusters/ray-clusters","page_id":"/features/clusters/ray-clusters","type":"page","content":"RayCluster","tags":[],"url":"/features/clusters/ray-clusters"},"1272":{"id":"/features/clusters/ray-clusters-0","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"How to set up a RayCluster on DGX Cloud Lepton for submitting Ray jobs on compute resources."},"1273":{"id":"/features/clusters/ray-clusters-1","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#prerequisites","content":"Prerequisites"},"1274":{"id":"/features/clusters/ray-clusters-2","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#steps","content":"Steps"},"1275":{"id":"/features/clusters/ray-clusters-3","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Creating a RayCluster"},"1276":{"id":"/features/clusters/ray-clusters-4","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"Connecting to the RayCluster"},"1277":{"id":"/features/clusters/ray-clusters-5","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"Launching a Ray Job"},"1278":{"id":"/features/clusters/ray-clusters-6","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#scaling-the-raycluster","content":"Scaling the RayCluster"},"1279":{"id":"/features/clusters/ray-clusters-7","page_id":"/features/clusters/ray-clusters","type":"heading","tags":[],"url":"/features/clusters/ray-clusters#shutting-down-the-raycluster","content":"Shutting down the RayCluster"},"1280":{"id":"/features/clusters/ray-clusters-8","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"DGX Cloud Lepton supports the creation of RayClusters to manage the scheduling and scaling of machine learning workloads using the open source Ray framework."},"1281":{"id":"/features/clusters/ray-clusters-9","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"A RayCluster is a dedicated reservation of resources on DGX Cloud Lepton with one head node and at least one worker node. You can connect to the RayCluster and submit Ray jobs, which are scheduled and run directly on the cluster. You can run a single large job on the RayCluster, or multiple smaller jobs as resources allow."},"1282":{"id":"/features/clusters/ray-clusters-10","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"RayCluster supports heterogeneous compute types, allowing you to run a head node on a CPU‑only instance and workers on GPU nodes. Additionally, the head node can include GPUs and optionally run jobs as well."},"1283":{"id":"/features/clusters/ray-clusters-11","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"A running RayCluster continues to reserve the requested resources until you delete it. To use the resources for other workflows, such as standard batch jobs, first shut down the RayCluster. Instructions are provided below."},"1284":{"id":"/features/clusters/ray-clusters-12","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"The high‑level steps for running jobs with Ray on DGX Cloud Lepton are:"},"1285":{"id":"/features/clusters/ray-clusters-13","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"Create a RayCluster"},"1286":{"id":"/features/clusters/ray-clusters-14","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"Submit tasks to the RayCluster"},"1287":{"id":"/features/clusters/ray-clusters-15","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters","content":"This guide shows how to set up a RayCluster and launch a basic Ray job on the running cluster."},"1288":{"id":"/features/clusters/ray-clusters-16","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#prerequisites","content":"A DGX Cloud Lepton workspace with an available node group to create a RayCluster"},"1289":{"id":"/features/clusters/ray-clusters-17","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#prerequisites","content":"At least one GPU node and one CPU resource"},"1290":{"id":"/features/clusters/ray-clusters-18","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#prerequisites","content":"Basic understanding of the Ray distributed computing framework"},"1291":{"id":"/features/clusters/ray-clusters-19","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"To create a RayCluster, navigate to the RayCluster creation page and click the Create Cluster button in the top right. This opens the form for creating the cluster."},"1292":{"id":"/features/clusters/ray-clusters-20","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Enter the following details on the form:"},"1293":{"id":"/features/clusters/ray-clusters-21","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Ray Cluster Name: Specify a name for the RayCluster, such as demo-ray-cluster."},"1294":{"id":"/features/clusters/ray-clusters-22","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Container: The RayCluster runs a container on the head node and each worker. The container specified here runs on all nodes in the RayCluster.To use an official Ray image, select a tag from the Built‑in list. Tag format: ray:<ray version>-<python version>-<gpu> where <ray version> is the Ray version, <python version> is the Python version, and <gpu> indicates GPU support.To use a custom container, select Custom under Image and specify the image ID. Ensure Ray is installed in the image. Then select the corresponding Ray Version."},"1295":{"id":"/features/clusters/ray-clusters-23","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Private Image Registry Authentication: Optionally add a private registry key for authenticating with a container registry."},"1296":{"id":"/features/clusters/ray-clusters-24","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Head Node: Select the Node Group and resource shape for the head node. The head node can be CPU‑only (scheduling only) or include GPU resources to run workloads. Specify any storage to mount on the head node using + Mount Storage."},"1297":{"id":"/features/clusters/ray-clusters-25","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Worker Group: Select the Node Group and resource shape for the worker(s). Using the same node group as the head node is recommended. Workers primarily run the workloads for submitted. To request multiple workers, increase Min Replicas. Add a storage mount using + Mount Storage. You can optionally specify a Group Name; otherwise, a name is generated for you."},"1298":{"id":"/features/clusters/ray-clusters-26","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#creating-a-raycluster","content":"Once the form is complete, click the Create button at the bottom of the page. You are returned to the list of RayClusters running in the workspace. When resources are available, the specified containers launch on each resource and the RayCluster starts. Depending on container size and requested resources, it can take a while for the RayCluster to reach the Ready state. When ready, the list looks similar to the following:"},"1299":{"id":"/features/clusters/ray-clusters-27","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"With a RayCluster started, you can connect to the head node to submit jobs or view the dashboard."},"1300":{"id":"/features/clusters/ray-clusters-28","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"To connect to the head node, click the Terminal button to the right of the RayCluster as highlighted in the image below."},"1301":{"id":"/features/clusters/ray-clusters-29","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"This opens a bash session on the head node in a virtual terminal at the bottom of the page. To verify the RayCluster, run ray status in the terminal. The output looks similar to the following, depending on the number of resources selected:"},"1302":{"id":"/features/clusters/ray-clusters-30","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"As shown, there are two nodes in the cluster with a total of 80 CPU cores and 4 GPUs available for scheduling jobs."},"1303":{"id":"/features/clusters/ray-clusters-31","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#connecting-to-the-raycluster","content":"Clicking Dashboard opens a new tab with the Ray dashboard for the cluster, where you can view recent jobs, utilization, resource availability, and more."},"1304":{"id":"/features/clusters/ray-clusters-32","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"To launch a job with Ray, open the terminal on the head node and create a new file named train.py with the following contents:"},"1305":{"id":"/features/clusters/ray-clusters-33","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"This simple script runs on two nodes with one GPU per node. It prints the node name and GPU index used for each worker, as well as the result of a simple matrix multiplication."},"1306":{"id":"/features/clusters/ray-clusters-34","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"The job requires PyTorch, which is not installed in the base Ray images. Install it on the head node and configure Ray to use the package on all workers. In the head node terminal, run:"},"1307":{"id":"/features/clusters/ray-clusters-35","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"This will install PyTorch on the head node which can be used on workers via a Ray runtime environment. To install and use different dependencies which aren't available in the base container, install the packages on the head node with pip3 and update the following line with additional package names by updating the [\"torch\"] list:"},"1308":{"id":"/features/clusters/ray-clusters-36","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"To launch the job, run python3 train.py in the terminal. This allocates two workers with a single GPU per worker in the RayCluster and runs the script. The output should look similar to the following:"},"1309":{"id":"/features/clusters/ray-clusters-37","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"The job will also be viewable in the Recent Jobs list in the dashboard to show status, nodes, usage, and more."},"1310":{"id":"/features/clusters/ray-clusters-38","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#launching-a-ray-job","content":"While simple, this shows how to use the allocated RayCluster to run a multi‑node GPU job. For more complex examples, refer to the Ray documentation, which includes several tutorials covering a wide range of tasks."},"1311":{"id":"/features/clusters/ray-clusters-39","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#scaling-the-raycluster","content":"The RayCluster supports manually scaling workers up and down to meet demand. To add resources to an existing RayCluster, click Edit next to the cluster and set the desired number of replicas. This updates the number of workers to match the replica levels specified in the form. For example, increasing from one to two replicas adds an additional worker. This may take some time as the image is pulled and the replica starts. After the worker is added, verify it by running ray status again:"},"1312":{"id":"/features/clusters/ray-clusters-40","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#scaling-the-raycluster","content":"The output indicates there is now an additional node in the cluster and two extra GPUs available for jobs."},"1313":{"id":"/features/clusters/ray-clusters-41","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#scaling-the-raycluster","content":"You can also scale down to use fewer workers if you do not need the existing capacity. When scaling down, extra workers are deleted and Ray updates automatically to reflect the desired number of workers."},"1314":{"id":"/features/clusters/ray-clusters-42","page_id":"/features/clusters/ray-clusters","tags":[],"type":"text","url":"/features/clusters/ray-clusters#shutting-down-the-raycluster","content":"As mentioned earlier, the RayCluster holds resources until it is shut down, allowing you to submit jobs in a persistent environment. To free up resources for other tasks, or when the RayCluster is no longer needed, delete it. Click the Delete button next to the cluster as shown below."},"1315":{"id":"/features/clusters/slurm-clusters","page_id":"/features/clusters/slurm-clusters","type":"page","content":"Slurm Clusters","tags":[],"url":"/features/clusters/slurm-clusters"},"1316":{"id":"/features/clusters/slurm-clusters-0","page_id":"/features/clusters/slurm-clusters","tags":[],"type":"text","url":"/features/clusters/slurm-clusters","content":"How to set up a Slurm cluster on DGX Cloud Lepton for running Slurm-based workloads."},"1317":{"id":"/features/clusters/slurm-clusters-1","page_id":"/features/clusters/slurm-clusters","tags":[],"type":"text","url":"/features/clusters/slurm-clusters","content":"DGX Cloud Lepton is currently in early access support of Slurm clusters. Slurm clusters are provisioned by request."},"1318":{"id":"/features/clusters/slurm-clusters-2","page_id":"/features/clusters/slurm-clusters","tags":[],"type":"text","url":"/features/clusters/slurm-clusters","content":"To request a Slurm cluster, navigate to the Slurm Cluster page under Clusters and click the Request Slurm Cluster button to fill out the request form. Your NVIDIA Technical Account Manager (TAM) will reach out with more information and next steps."},"1319":{"id":"/features/dev-pods/configurations","page_id":"/features/dev-pods/configurations","type":"page","content":"Dev Pod Configurations","tags":[],"url":"/features/dev-pods/configurations"},"1320":{"id":"/features/dev-pods/configurations-0","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations","content":"Learn how to configure your dev pod in Lepton."},"1321":{"id":"/features/dev-pods/configurations-1","page_id":"/features/dev-pods/configurations","type":"heading","tags":[],"url":"/features/dev-pods/configurations#resource","content":"Resource"},"1322":{"id":"/features/dev-pods/configurations-2","page_id":"/features/dev-pods/configurations","type":"heading","tags":[],"url":"/features/dev-pods/configurations#container","content":"Container"},"1323":{"id":"/features/dev-pods/configurations-3","page_id":"/features/dev-pods/configurations","type":"heading","tags":[],"url":"/features/dev-pods/configurations#storage","content":"Storage"},"1324":{"id":"/features/dev-pods/configurations-4","page_id":"/features/dev-pods/configurations","type":"heading","tags":[],"url":"/features/dev-pods/configurations#advanced","content":"Advanced"},"1325":{"id":"/features/dev-pods/configurations-5","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations","content":"When creating a dev pod, you can configure resources, the container, and other options."},"1326":{"id":"/features/dev-pods/configurations-6","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Node Group: First, select one or more node groups to determine which resources the pod can use."},"1327":{"id":"/features/dev-pods/configurations-7","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Priority: Set the priority of the pod (defaults to Medium (4)). If the specified node group has limited resources, you can raise the priority to get higher priority allocation."},"1328":{"id":"/features/dev-pods/configurations-8","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Resource shape: The instance type the pod will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details."},"1329":{"id":"/features/dev-pods/configurations-9","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Nodes: No specific nodes are selected by default, but you can target particular nodes to launch the pod on."},"1330":{"id":"/features/dev-pods/configurations-10","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Can preempt lower priority workload: Whether the pod can preempt lower-priority workloads (defaults to false)."},"1331":{"id":"/features/dev-pods/configurations-11","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#resource","content":"Can be preempted by higher priority workload: Whether the pod can be preempted by higher-priority workloads (defaults to false)."},"1332":{"id":"/features/dev-pods/configurations-12","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Image: The container image used to create the pod. Choose from default images or use your own custom image."},"1333":{"id":"/features/dev-pods/configurations-13","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"SSH Public Key: The SSH public key used to access the pod. Available for default images with Dev Pod entrypoint. See this guide for details."},"1334":{"id":"/features/dev-pods/configurations-14","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Enable JupyterLab: Whether to enable JupyterLab in the pod (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."},"1335":{"id":"/features/dev-pods/configurations-15","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Private image registry auth (optional): If you are using a private image, specify image registry credentials."},"1336":{"id":"/features/dev-pods/configurations-16","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Entrypoint (Run Command): Entrypoint of the Dev Pod container. Choose from:"},"1337":{"id":"/features/dev-pods/configurations-17","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Dev Pod entrypoint: Automatically applies a run command based on the selected image."},"1338":{"id":"/features/dev-pods/configurations-18","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Image default entrypoint: Uses the image's default run command."},"1339":{"id":"/features/dev-pods/configurations-19","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Custom entrypoint: Specify a custom entrypoint for the container. This overrides the default entrypoint."},"1340":{"id":"/features/dev-pods/configurations-20","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Run as: When using a custom image, choose to run the container as root or the image's default user (defaults to image default user)."},"1341":{"id":"/features/dev-pods/configurations-21","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Container Ports: Ports to expose from the container (maximum of 3). See this guide for details."},"1342":{"id":"/features/dev-pods/configurations-22","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Enable SSH Host Network: Whether to enable SSH Host Network port configuration (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."},"1343":{"id":"/features/dev-pods/configurations-23","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#container","content":"Enable JupyterLab Proxy: Whether to enable JupyterLab Proxy port configuration (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."},"1344":{"id":"/features/dev-pods/configurations-24","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#storage","content":"Mount storage for the pod container. See this guide for details."},"1345":{"id":"/features/dev-pods/configurations-25","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#advanced","content":"Log Collection: Whether to collect logs from the container (follows the workspace-level setting by default)."},"1346":{"id":"/features/dev-pods/configurations-26","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#advanced","content":"Shared Memory: The size of shared memory allocated to the container."},"1347":{"id":"/features/dev-pods/configurations-27","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#advanced","content":"Archive time: How long to keep the job's logs and artifacts after completion (defaults to 3 days)."},"1348":{"id":"/features/dev-pods/configurations-28","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#advanced","content":"Visibility: If set to private, only the creator can access the job. If set to public, all users in the workspace can access the job."},"1349":{"id":"/features/dev-pods/configurations-29","page_id":"/features/dev-pods/configurations","tags":[],"type":"text","url":"/features/dev-pods/configurations#advanced","content":"Environment Variables: Key–value pairs passed to the pod container for use at runtime."},"1350":{"id":"/features/dev-pods/container-ports","page_id":"/features/dev-pods/container-ports","type":"page","content":"Container Ports","tags":[],"url":"/features/dev-pods/container-ports"},"1351":{"id":"/features/dev-pods/container-ports-0","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports","content":"Configure container ports for your Dev Pod"},"1352":{"id":"/features/dev-pods/container-ports-1","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#what-are-container-ports","content":"What are Container Ports?"},"1353":{"id":"/features/dev-pods/container-ports-2","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#configure-container-ports","content":"Configure Container Ports"},"1354":{"id":"/features/dev-pods/container-ports-3","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#protocol","content":"Protocol"},"1355":{"id":"/features/dev-pods/container-ports-4","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#port","content":"Port"},"1356":{"id":"/features/dev-pods/container-ports-5","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#pass-through","content":"Pass Through"},"1357":{"id":"/features/dev-pods/container-ports-6","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#access-container-ports","content":"Access Container Ports"},"1358":{"id":"/features/dev-pods/container-ports-7","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#access-with-ip-address","content":"Access with IP Address"},"1359":{"id":"/features/dev-pods/container-ports-8","page_id":"/features/dev-pods/container-ports","type":"heading","tags":[],"url":"/features/dev-pods/container-ports#access-with-proxy-url","content":"Access with Proxy URL"},"1360":{"id":"/features/dev-pods/container-ports-9","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports","content":"Container ports allow you to expose services running inside your Dev Pod to the outside world. By default, applications running in a container are isolated and cannot be accessed externally. Configuring container ports creates a bridge between your containerized applications and external networks."},"1361":{"id":"/features/dev-pods/container-ports-10","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports","content":"This document guides you through configuring and accessing container ports in your Dev Pod."},"1362":{"id":"/features/dev-pods/container-ports-11","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports","content":"DGX Cloud Lepton does not expose any ports by default for security reasons."},"1363":{"id":"/features/dev-pods/container-ports-12","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports","content":"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."},"1364":{"id":"/features/dev-pods/container-ports-13","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#what-are-container-ports","content":"Before diving into how to configure container ports, let's understand what they are."},"1365":{"id":"/features/dev-pods/container-ports-14","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#what-are-container-ports","content":"Container Port: The port your application listens on inside the container (for example, your web server runs on port 8080)."},"1366":{"id":"/features/dev-pods/container-ports-15","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#what-are-container-ports","content":"Host Port: The port on the host machine that maps to your container port (for example, external users connect to port 45123 on the host, which forwards to port 8080 in the container)."},"1367":{"id":"/features/dev-pods/container-ports-16","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#what-are-container-ports","content":"In DGX Cloud Lepton, you must configure container ports to expose them as host ports externally."},"1368":{"id":"/features/dev-pods/container-ports-17","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#configure-container-ports","content":"When creating a Dev Pod, configure container ports to expose in the Container Configuration > Expose Ports section."},"1369":{"id":"/features/dev-pods/container-ports-18","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#protocol","content":"For each port, specify the protocol type: TCP, UDP, or SCTP."},"1370":{"id":"/features/dev-pods/container-ports-19","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#port","content":"For each port, specify the port number to expose. The port number must be between 1 and 65535."},"1371":{"id":"/features/dev-pods/container-ports-20","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#port","content":"If you are using built-in images and Dev Pod Entrypoint, port 2222 in the container will be used for SSH.\nAdditionally, if you also select Enable JupyterLab, port 18889 in the container will be used for JupyterLab."},"1372":{"id":"/features/dev-pods/container-ports-21","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#pass-through","content":"Choose how to expose the port:"},"1373":{"id":"/features/dev-pods/container-ports-22","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#pass-through","content":"Host Network: A random port between 40000 and 65535 is added to the Dev Pod and mapped to the specified container port. This Host Network port is accessible on the host node IP."},"1374":{"id":"/features/dev-pods/container-ports-23","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#pass-through","content":"Proxy: A public URL is generated for the port, accessible on the public network. Only one port can be exposed through Proxy at a time."},"1375":{"id":"/features/dev-pods/container-ports-24","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#pass-through","content":"For example, you can configure port 2222 with protocol TCP and expose it through Host Network, and port 18889 with protocol TCP and expose it through Proxy."},"1376":{"id":"/features/dev-pods/container-ports-25","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#pass-through","content":"You need to select at least one Pass Through method for each port you configure."},"1377":{"id":"/features/dev-pods/container-ports-26","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#access-container-ports","content":"After the Dev Pod is created, view container port information on the Pod details page under the Connect tab."},"1378":{"id":"/features/dev-pods/container-ports-27","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#access-container-ports","content":"As shown in the image, since we configured Proxy for port 18889, an endpoint URL was generated for it."},"1379":{"id":"/features/dev-pods/container-ports-28","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#access-with-ip-address","content":"For ports exposed through Host Port, access them using the IP address of the host node."},"1380":{"id":"/features/dev-pods/container-ports-29","page_id":"/features/dev-pods/container-ports","tags":[],"type":"text","url":"/features/dev-pods/container-ports#access-with-proxy-url","content":"If you configured a port to be exposed through Proxy, access it with the generated public URL."},"1381":{"id":"/features/dev-pods/create-from-starter-kits","page_id":"/features/dev-pods/create-from-starter-kits","type":"page","content":"Starter Kits on Dev Pods","tags":[],"url":"/features/dev-pods/create-from-starter-kits"},"1382":{"id":"/features/dev-pods/create-from-starter-kits-0","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits","content":"Learn how to run a Starter Kit for a reproducible AI workflow in a Dev Pod."},"1383":{"id":"/features/dev-pods/create-from-starter-kits-1","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#viewing-notebooks","content":"Viewing Notebooks"},"1384":{"id":"/features/dev-pods/create-from-starter-kits-2","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#running-a-notebook","content":"Running a Notebook"},"1385":{"id":"/features/dev-pods/create-from-starter-kits-3","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#pod-name","content":"Pod Name"},"1386":{"id":"/features/dev-pods/create-from-starter-kits-4","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#resource","content":"Resource"},"1387":{"id":"/features/dev-pods/create-from-starter-kits-5","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#container","content":"Container"},"1388":{"id":"/features/dev-pods/create-from-starter-kits-6","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#environment-variables-and-secrets","content":"Environment Variables and Secrets"},"1389":{"id":"/features/dev-pods/create-from-starter-kits-7","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#storage","content":"Storage"},"1390":{"id":"/features/dev-pods/create-from-starter-kits-8","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#advanced","content":"Advanced"},"1391":{"id":"/features/dev-pods/create-from-starter-kits-9","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#launching-the-starter-kit","content":"Launching the Starter Kit"},"1392":{"id":"/features/dev-pods/create-from-starter-kits-10","page_id":"/features/dev-pods/create-from-starter-kits","type":"heading","tags":[],"url":"/features/dev-pods/create-from-starter-kits#accessing-jupyterlab","content":"Accessing JupyterLab"},"1393":{"id":"/features/dev-pods/create-from-starter-kits-11","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits","content":"Starter Kits are a curated set of Jupyter notebooks with self-contained AI/ML examples that run directly on DGX Cloud Lepton Dev Pods."},"1394":{"id":"/features/dev-pods/create-from-starter-kits-12","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits","content":"To view the list of notebooks, navigate to the Dev Pods page and click the green Create Pod button followed by Create From Notebooks. You can also access the list directly with this link."},"1395":{"id":"/features/dev-pods/create-from-starter-kits-13","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#viewing-notebooks","content":"The Starter Kits page shows a list of notebooks to choose from, as shown below."},"1396":{"id":"/features/dev-pods/create-from-starter-kits-14","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#viewing-notebooks","content":"The list shows the title and a brief description of each notebook so you can preview what it covers. Select a notebook from the list or search for a specific topic in the search field at the top of the form. The raw list of notebooks is mirrored from GitHub."},"1397":{"id":"/features/dev-pods/create-from-starter-kits-15","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#viewing-notebooks","content":"Clicking a notebook opens a read-only preview of the full notebook, providing a deeper look at what it accomplishes, as shown below."},"1398":{"id":"/features/dev-pods/create-from-starter-kits-16","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#running-a-notebook","content":"To run a notebook in a Dev Pod, click the Create pod from this notebook button at the top of the preview page. This opens the Dev Pod creation form with some fields pre-configured (such as the container image). Enter the remaining fields to complete the setup, as described below."},"1399":{"id":"/features/dev-pods/create-from-starter-kits-17","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#pod-name","content":"Specify a name for the Dev Pod to launch, such as my-starter-kit."},"1400":{"id":"/features/dev-pods/create-from-starter-kits-18","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#resource","content":"Specify the resource for the Dev Pod to run on."},"1401":{"id":"/features/dev-pods/create-from-starter-kits-19","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#resource","content":"Each notebook may have different requirements. Refer to the Requirements section at the top of the notebook preview for the number of GPUs and any additional requirements."},"1402":{"id":"/features/dev-pods/create-from-starter-kits-20","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#resource","content":"Node Group: Select the desired node group to run the Dev Pod in."},"1403":{"id":"/features/dev-pods/create-from-starter-kits-21","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#resource","content":"Priority: Set the priority of the pod (defaults to Medium (4)). If the specified node group has limited resources, you can set the priority accordingly to get higher priority resource allocation."},"1404":{"id":"/features/dev-pods/create-from-starter-kits-22","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#resource","content":"Resource shape: The instance type that the pod will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details. The number of GPUs must satisfy the notebook's listed requirements as shown in the preview."},"1405":{"id":"/features/dev-pods/create-from-starter-kits-23","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"Image: This field should be pre-populated with the container image the notebook was designed for. While you can try a different container, it is not recommended, as it might not work with the notebook."},"1406":{"id":"/features/dev-pods/create-from-starter-kits-24","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"Entrypoint: Specifies the script to run at container launch. The Starter Kits are pre-configured with an entrypoint to launch Jupyter. Use the default Dev Pod entrypoint option here."},"1407":{"id":"/features/dev-pods/create-from-starter-kits-25","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"SSH Public Key: The SSH public key that will be used to access the pod. This is only available for default images with Dev Pod entrypoint. Refer to this guide for more details."},"1408":{"id":"/features/dev-pods/create-from-starter-kits-26","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"Enable JupyterLab: Defaults to True to enable a direct connection to a JupyterLab session inside the container."},"1409":{"id":"/features/dev-pods/create-from-starter-kits-27","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"Expose JupyterLab Proxy: Ensure this button is checked for a direct URL to the JupyterLab session which can be accessed externally."},"1410":{"id":"/features/dev-pods/create-from-starter-kits-28","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#container","content":"Ensure that Expose JupyterLab Proxy is selected. Otherwise, you won't be able to access JupyterLab."},"1411":{"id":"/features/dev-pods/create-from-starter-kits-29","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#environment-variables-and-secrets","content":"You can specify additional environment variables or secrets to set inside the running container. The name of the selected notebook is pre-configured as the LEPTON_DEVPOD_NOTEBOOK_NAME variable."},"1412":{"id":"/features/dev-pods/create-from-starter-kits-30","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#storage","content":"Some Starter Kits require shared storage to be mounted in order to persist data after the notebook finishes. Check the Requirements section of the notebook to see if shared storage is required. If so, mount storage for the pod container. See this guide for more details."},"1413":{"id":"/features/dev-pods/create-from-starter-kits-31","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#advanced","content":"Log Collection: Whether to collect logs from the container. Follows the workspace-level setting by default."},"1414":{"id":"/features/dev-pods/create-from-starter-kits-32","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#advanced","content":"Shared Memory: The size of the shared memory allocated to the container. Some Starter Kits require more than the default. Check the notebook's Requirements section and specify it here if needed."},"1415":{"id":"/features/dev-pods/create-from-starter-kits-33","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#advanced","content":"Archive time: How long to keep the job's logs and artifacts after the job is completed (defaults to 3 days)."},"1416":{"id":"/features/dev-pods/create-from-starter-kits-34","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#advanced","content":"Visibility: Specifies the visibility of the job. If set to private, only the creator can access the job. If set to public, all users in the workspace can access it."},"1417":{"id":"/features/dev-pods/create-from-starter-kits-35","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#launching-the-starter-kit","content":"Once the Dev Pod creation form is complete, click the Create button at the bottom of the page. This queues a Dev Pod with the requested resources in the specified node group. When resources become available, the container launches and the JupyterLab session begins."},"1418":{"id":"/features/dev-pods/create-from-starter-kits-36","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#accessing-jupyterlab","content":"When the Dev Pod is running, click the job in the Dev Pods list to open the job details page. On this page, you can see additional information, including access via Terminal and JupyterLab."},"1419":{"id":"/features/dev-pods/create-from-starter-kits-37","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#accessing-jupyterlab","content":"First, set a password for accessing Jupyter. Click the Terminal button on the job details page and run the following command, as shown below:"},"1420":{"id":"/features/dev-pods/create-from-starter-kits-38","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#accessing-jupyterlab","content":"After setting a new password for Jupyter, click the JupyterLab tab on the job details page to get a link to the JupyterLab session in your browser. Click the link to open JupyterLab and access the notebook in a new browser tab."},"1421":{"id":"/features/dev-pods/create-from-starter-kits-39","page_id":"/features/dev-pods/create-from-starter-kits","tags":[],"type":"text","url":"/features/dev-pods/create-from-starter-kits#accessing-jupyterlab","content":"JupyterLab opens in a new browser tab running on the requested resources inside the Dev Pod. Enter the password you specified above to authenticate, then click the notebook in the left panel to open it and begin running it."},"1422":{"id":"/features/endpoints/configurations","page_id":"/features/endpoints/configurations","type":"page","content":"Endpoint Configurations","tags":[],"url":"/features/endpoints/configurations"},"1423":{"id":"/features/endpoints/configurations-0","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations","content":"Learn how to create and manage dedicated endpoints in Lepton for AI model deployment, including LLM, custom container image, and NVIDIA NIM, with various configuration options."},"1424":{"id":"/features/endpoints/configurations-1","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#create-endpoint","content":"Create Endpoint"},"1425":{"id":"/features/endpoints/configurations-2","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#autoscaling","content":"Autoscaling"},"1426":{"id":"/features/endpoints/configurations-3","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#access-control","content":"Access Control"},"1427":{"id":"/features/endpoints/configurations-4","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#public-access","content":"Public Access"},"1428":{"id":"/features/endpoints/configurations-5","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#ip-allowlist","content":"IP Allowlist"},"1429":{"id":"/features/endpoints/configurations-6","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#environment-variables-and-secrets","content":"Environment Variables and Secrets"},"1430":{"id":"/features/endpoints/configurations-7","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#storage","content":"Storage"},"1431":{"id":"/features/endpoints/configurations-8","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#advanced-configurations","content":"Advanced Configurations"},"1432":{"id":"/features/endpoints/configurations-9","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#visibility","content":"Visibility"},"1433":{"id":"/features/endpoints/configurations-10","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#shared-memory","content":"Shared Memory"},"1434":{"id":"/features/endpoints/configurations-11","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#health-check-initial-delay-seconds","content":"Health Check Initial Delay (seconds)"},"1435":{"id":"/features/endpoints/configurations-12","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#require-approval-to-make-replicas-ready","content":"Require Approval to Make Replicas Ready"},"1436":{"id":"/features/endpoints/configurations-13","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#pulling-metrics-from-replicas","content":"Pulling Metrics from Replicas"},"1437":{"id":"/features/endpoints/configurations-14","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#header-based-replica-routing","content":"Header-based Replica Routing"},"1438":{"id":"/features/endpoints/configurations-15","page_id":"/features/endpoints/configurations","type":"heading","tags":[],"url":"/features/endpoints/configurations#log-collection","content":"Log Collection"},"1439":{"id":"/features/endpoints/configurations-16","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations","content":"An endpoint is a running instance of an AI model that exposes an HTTP server.\nAny service can run as a dedicated endpoint. The most common use case is deploying an AI model exposed with an OpenAPI interface."},"1440":{"id":"/features/endpoints/configurations-17","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#create-endpoint","content":"You can create an endpoint in several ways. Refer to the following guides for details:"},"1441":{"id":"/features/endpoints/configurations-18","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#create-endpoint","content":"Create from NVIDIA NIM"},"1442":{"id":"/features/endpoints/configurations-19","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#create-endpoint","content":"Create Dedicated LLM Endpoint"},"1443":{"id":"/features/endpoints/configurations-20","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#create-endpoint","content":"Create from Container Image"},"1444":{"id":"/features/endpoints/configurations-21","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"By default, DGX Cloud Lepton creates your endpoints with a single replica and automatically scales down to zero after one hour of inactivity."},"1445":{"id":"/features/endpoints/configurations-22","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"You can override this behavior with three autoscaling options and related flags:"},"1446":{"id":"/features/endpoints/configurations-23","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"Scale replicas to zero based on no‑traffic timeout: Specify the initial number of replicas and the no‑traffic timeout (seconds)."},"1447":{"id":"/features/endpoints/configurations-24","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"Autoscale replicas based on traffic QPM: Specify the minimum and maximum number of replicas and the target queries per minute (QPM). You can also specify the HTTP methods and request paths to include in traffic metrics."},"1448":{"id":"/features/endpoints/configurations-25","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"Autoscale replicas based on GPU utilization: Specify the minimum and maximum number of replicas and the target GPU utilization percentage."},"1449":{"id":"/features/endpoints/configurations-26","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#autoscaling","content":"We do not currently support scaling up from zero replicas. If a deployment is\nscaled down to zero replicas, it will not be able to serve any requests until\nit is scaled up again."},"1450":{"id":"/features/endpoints/configurations-27","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#access-control","content":"DGX Cloud Lepton provides a built‑in access control system for your endpoints. You can create an endpoint with one of the following access control policies:"},"1451":{"id":"/features/endpoints/configurations-28","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#access-control","content":"Public access: Allow access to your endpoint from any IP address with an optional endpoint token authentication."},"1452":{"id":"/features/endpoints/configurations-29","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#access-control","content":"IP allowlist: Allow access to your endpoint only from valid IP addresses or CIDR ranges."},"1453":{"id":"/features/endpoints/configurations-30","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#public-access","content":"By default, the endpoint uses the Public access policy without token authentication, which means it is accessible from any IP address."},"1454":{"id":"/features/endpoints/configurations-31","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#public-access","content":"To require token authentication, click Add Endpoint Token to create a new token. DGX Cloud Lepton automatically generates the token."},"1455":{"id":"/features/endpoints/configurations-32","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#public-access","content":"You can add multiple tokens and modify generated token values."},"1456":{"id":"/features/endpoints/configurations-33","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#ip-allowlist","content":"Select IP allowlist to allow access to your endpoint only from specified IP addresses or CIDR ranges."},"1457":{"id":"/features/endpoints/configurations-34","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#ip-allowlist","content":"Enter one IP address or CIDR range per line, or separate multiple entries with commas."},"1458":{"id":"/features/endpoints/configurations-35","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#environment-variables-and-secrets","content":"Environment variables are key‑value pairs passed to the deployment.\nAll variables are injected into the deployment container and available at runtime."},"1459":{"id":"/features/endpoints/configurations-36","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#environment-variables-and-secrets","content":"Secrets are similar to environment variables, but their values are pre-stored in the platform and not exposed in the development environment.\nLearn more about secrets here."},"1460":{"id":"/features/endpoints/configurations-37","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#environment-variables-and-secrets","content":"Within the deployment, the secret value is available as an environment variable with the same name as the secret."},"1461":{"id":"/features/endpoints/configurations-38","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#environment-variables-and-secrets","content":"Your defined environment variables should not start with the name prefix\nLEPTON_, as this prefix is reserved for predefined environment variables."},"1462":{"id":"/features/endpoints/configurations-39","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#storage","content":"Mount storage for the deployment container. Refer to this guide for details."},"1463":{"id":"/features/endpoints/configurations-40","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#visibility","content":"Public: Visible to all team members in your workspace."},"1464":{"id":"/features/endpoints/configurations-41","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#visibility","content":"Private: Visible only to the creator and administrators."},"1465":{"id":"/features/endpoints/configurations-42","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#shared-memory","content":"The size of shared memory allocated to the container."},"1466":{"id":"/features/endpoints/configurations-43","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#health-check-initial-delay-seconds","content":"By default, two types of probes are configured:"},"1467":{"id":"/features/endpoints/configurations-44","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#health-check-initial-delay-seconds","content":"Readiness probe: Initial delay of 5 seconds; checks every 5 seconds. One success marks the container as ready; 10 consecutive failures mark it as not ready. Ensures the service is ready to accept traffic."},"1468":{"id":"/features/endpoints/configurations-45","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#health-check-initial-delay-seconds","content":"Liveness probe: Initial delay of 600 seconds (10 minutes); checks every 5 seconds. One success marks the container as healthy; 12 consecutive failures mark it as unhealthy. Ensures the service remains healthy during operation."},"1469":{"id":"/features/endpoints/configurations-46","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#health-check-initial-delay-seconds","content":"Some endpoints may need more time to start the container and initialize the model. Specify a custom delay by selecting Custom and entering the delay in seconds."},"1470":{"id":"/features/endpoints/configurations-47","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#require-approval-to-make-replicas-ready","content":"Specify whether approval is required before replicas become ready.\nBy default, replicas are ready immediately after deployment."},"1471":{"id":"/features/endpoints/configurations-48","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#pulling-metrics-from-replicas","content":"Specify whether to pull metrics from replicas.\nBy default, metrics are pulled from all replicas."},"1472":{"id":"/features/endpoints/configurations-49","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#header-based-replica-routing","content":"Configure header‑based replica routing. By default, requests are load‑balanced across all replicas."},"1473":{"id":"/features/endpoints/configurations-50","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#header-based-replica-routing","content":"When enabled, specify which replica to use for a request by including the X-Lepton-Replica-Target header with the replica ID. Example:"},"1474":{"id":"/features/endpoints/configurations-51","page_id":"/features/endpoints/configurations","tags":[],"type":"text","url":"/features/endpoints/configurations#log-collection","content":"Whether to collect logs from replicas.\nBy default, this follows the workspace setting."},"1475":{"id":"/features/endpoints/create-from-container-image","page_id":"/features/endpoints/create-from-container-image","type":"page","content":"Create Endpoints from Container Image","tags":[],"url":"/features/endpoints/create-from-container-image"},"1476":{"id":"/features/endpoints/create-from-container-image-0","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image","content":"Learn how to create endpoints from Docker images."},"1477":{"id":"/features/endpoints/create-from-container-image-1","page_id":"/features/endpoints/create-from-container-image","type":"heading","tags":[],"url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Create Endpoint"},"1478":{"id":"/features/endpoints/create-from-container-image-2","page_id":"/features/endpoints/create-from-container-image","type":"heading","tags":[],"url":"/features/endpoints/create-from-container-image#view-endpoint","content":"View Endpoint"},"1479":{"id":"/features/endpoints/create-from-container-image-3","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image","content":"This guide shows how to create a dedicated endpoint from a Docker image."},"1480":{"id":"/features/endpoints/create-from-container-image-4","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Go to the Endpoints page and click Create Endpoint."},"1481":{"id":"/features/endpoints/create-from-container-image-5","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Select Create Endpoint."},"1482":{"id":"/features/endpoints/create-from-container-image-6","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Select Create from Container Image."},"1483":{"id":"/features/endpoints/create-from-container-image-7","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"For Endpoint name, enter container-image-endpoint or any name you prefer."},"1484":{"id":"/features/endpoints/create-from-container-image-8","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"For Image configuration, select the image, command, and port:"},"1485":{"id":"/features/endpoints/create-from-container-image-9","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Image: Choose the image to use. For example: nvcr.io/nvidia/pytorch:24.11-py3."},"1486":{"id":"/features/endpoints/create-from-container-image-10","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Server Port: Choose the port your process listens on. For example: 8080."},"1487":{"id":"/features/endpoints/create-from-container-image-11","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Run command: Choose the command to run. For example: python -m http.server 8080."},"1488":{"id":"/features/endpoints/create-from-container-image-12","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Private image registry auth (optional): If you use a private registry, add registry credentials on the Registries page."},"1489":{"id":"/features/endpoints/create-from-container-image-13","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"Leave other configurations at their defaults, or refer to endpoint configurations for details."},"1490":{"id":"/features/endpoints/create-from-container-image-14","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#create-endpoint","content":"We recommend setting up an access token for your endpoint instead of making it public."},"1491":{"id":"/features/endpoints/create-from-container-image-15","page_id":"/features/endpoints/create-from-container-image","tags":[],"type":"text","url":"/features/endpoints/create-from-container-image#view-endpoint","content":"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."},"1492":{"id":"/features/endpoints/create-from-nim","page_id":"/features/endpoints/create-from-nim","type":"page","content":"Create Endpoints from NVIDIA NIM","tags":[],"url":"/features/endpoints/create-from-nim"},"1493":{"id":"/features/endpoints/create-from-nim-0","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim","content":"Learn how to create dedicated endpoints from NVIDIA NIM."},"1494":{"id":"/features/endpoints/create-from-nim-1","page_id":"/features/endpoints/create-from-nim","type":"heading","tags":[],"url":"/features/endpoints/create-from-nim#prerequisites","content":"Prerequisites"},"1495":{"id":"/features/endpoints/create-from-nim-2","page_id":"/features/endpoints/create-from-nim","type":"heading","tags":[],"url":"/features/endpoints/create-from-nim#nvidia-registry","content":"NVIDIA Registry"},"1496":{"id":"/features/endpoints/create-from-nim-3","page_id":"/features/endpoints/create-from-nim","type":"heading","tags":[],"url":"/features/endpoints/create-from-nim#ngc-api-key","content":"NGC API Key"},"1497":{"id":"/features/endpoints/create-from-nim-4","page_id":"/features/endpoints/create-from-nim","type":"heading","tags":[],"url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"Create endpoint from NVIDIA NIM"},"1498":{"id":"/features/endpoints/create-from-nim-5","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim","content":"For enhanced performance and seamless compatibility, NVIDIA‑optimized models from the NIM container registry are available on DGX Cloud Lepton."},"1499":{"id":"/features/endpoints/create-from-nim-6","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#prerequisites","content":"These models require an NVIDIA account with access to the NIM container registry."},"1500":{"id":"/features/endpoints/create-from-nim-7","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#nvidia-registry","content":"You must have an NVIDIA account with access to the NIM container registry and configure the registry auth key on DGX Cloud Lepton."},"1501":{"id":"/features/endpoints/create-from-nim-8","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#nvidia-registry","content":"Refer to this guide for details. Once the registry auth key is created, add a private registry via Settings > Registries > New Registry Auth."},"1502":{"id":"/features/endpoints/create-from-nim-9","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#nvidia-registry","content":"Choose NVIDIA as the registry type and paste the registry auth key in the API Key field."},"1503":{"id":"/features/endpoints/create-from-nim-10","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#ngc-api-key","content":"Besides the registry auth key, you also need an NGC API key. Navigate to the NGC API key creation page and click Generate Personal Key."},"1504":{"id":"/features/endpoints/create-from-nim-11","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#ngc-api-key","content":"In the Service Included field, select Public API Endpoints."},"1505":{"id":"/features/endpoints/create-from-nim-12","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#ngc-api-key","content":"Store the NGC API key on DGX Cloud Lepton as a secret."},"1506":{"id":"/features/endpoints/create-from-nim-13","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"Navigate to the Create Endpoint page on the dashboard."},"1507":{"id":"/features/endpoints/create-from-nim-14","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"For Endpoint name, enter nim-endpoint or any name you prefer."},"1508":{"id":"/features/endpoints/create-from-nim-15","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"For Resource, choose an appropriate resource based on the model size."},"1509":{"id":"/features/endpoints/create-from-nim-16","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"For NIM configuration:"},"1510":{"id":"/features/endpoints/create-from-nim-17","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"Select a model image from the list of built‑in models, or enter a custom model image."},"1511":{"id":"/features/endpoints/create-from-nim-18","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"Select the NVIDIA registry auth you created (see registry auth)."},"1512":{"id":"/features/endpoints/create-from-nim-19","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"Select the NGC API key you saved as a secret in your workspace."},"1513":{"id":"/features/endpoints/create-from-nim-20","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"For other endpoint‑related configurations, refer to this guide."},"1514":{"id":"/features/endpoints/create-from-nim-21","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"For NIM engine‑related configurations, refer to this guide. Configure the NIM engine by setting the relevant environment variables."},"1515":{"id":"/features/endpoints/create-from-nim-22","page_id":"/features/endpoints/create-from-nim","tags":[],"type":"text","url":"/features/endpoints/create-from-nim#create-endpoint-from-nvidia-nim","content":"When finished, click Create Endpoint to create the endpoint."},"1516":{"id":"/features/endpoints/create-llm","page_id":"/features/endpoints/create-llm","type":"page","content":"Create LLM Endpoints","tags":[],"url":"/features/endpoints/create-llm"},"1517":{"id":"/features/endpoints/create-llm-0","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm","content":"Learn how to deploy LLM endpoints on DGX Cloud Lepton."},"1518":{"id":"/features/endpoints/create-llm-1","page_id":"/features/endpoints/create-llm","type":"heading","tags":[],"url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Create LLM Endpoints with vLLM"},"1519":{"id":"/features/endpoints/create-llm-2","page_id":"/features/endpoints/create-llm","type":"heading","tags":[],"url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Create LLM Endpoints with SGLang"},"1520":{"id":"/features/endpoints/create-llm-3","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm","content":"In this guide, we'll show you how to create a dedicated endpoint from LLMs with inference engine vLLM and SGLang to serve models from Hugging Face."},"1521":{"id":"/features/endpoints/create-llm-4","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"vLLM is a fast and easy-to-use library for LLM inference and serving. To create a dedicated endpoint with vLLM, follow these steps:"},"1522":{"id":"/features/endpoints/create-llm-5","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Go to the Endpoints page and click Create Endpoint."},"1523":{"id":"/features/endpoints/create-llm-6","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Select Create LLM Endpoint."},"1524":{"id":"/features/endpoints/create-llm-7","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Select vLLM as the inference engine."},"1525":{"id":"/features/endpoints/create-llm-8","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"For Endpoint name, enter vllm-endpoint or any name you prefer."},"1526":{"id":"/features/endpoints/create-llm-9","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"For Model, click Load from Hugging Face and search by keyword. For example, meta-llama/Llama-3.1-8B-Instruct. If the model is gated, provide a Hugging Face token. Create a token in your Hugging Face account and save it as a secret in your workspace."},"1527":{"id":"/features/endpoints/create-llm-10","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"For Resource, choose an appropriate resource based on the model size."},"1528":{"id":"/features/endpoints/create-llm-11","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"For Image configuration, leave defaults as is. To add arguments, expand the command-line arguments section and add your own. vLLM arguments are listed here."},"1529":{"id":"/features/endpoints/create-llm-12","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Leave other configurations at their defaults, or refer to endpoint configurations for details."},"1530":{"id":"/features/endpoints/create-llm-13","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"We recommend setting up an access token for your endpoint instead of making it public."},"1531":{"id":"/features/endpoints/create-llm-14","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."},"1532":{"id":"/features/endpoints/create-llm-15","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"Test the endpoint in the playground by clicking the endpoint you created."},"1533":{"id":"/features/endpoints/create-llm-16","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-vllm","content":"To access the endpoint via API, click the API tab on the endpoint detail page to find the API key and endpoint URL."},"1534":{"id":"/features/endpoints/create-llm-17","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"SGLang is a fast serving framework for large language models and vision language models. It enables faster, more controllable interactions by co-designing the backend runtime and frontend language. To create a dedicated endpoint with SGLang, follow these steps:"},"1535":{"id":"/features/endpoints/create-llm-18","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Go to the Endpoints page and click Create Endpoint."},"1536":{"id":"/features/endpoints/create-llm-19","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Select Create LLM Endpoint."},"1537":{"id":"/features/endpoints/create-llm-20","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Select SGLang as the inference engine."},"1538":{"id":"/features/endpoints/create-llm-21","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"For Endpoint name, enter sglang-endpoint or any name you prefer."},"1539":{"id":"/features/endpoints/create-llm-22","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"For Model, click Load from Hugging Face and search by keyword. For example, meta-llama/Llama-3.1-8B-Instruct. If the model is gated, provide a Hugging Face token. Create a token in your Hugging Face account and save it as a secret in your workspace."},"1540":{"id":"/features/endpoints/create-llm-23","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"For Resource, choose an appropriate resource based on the model size."},"1541":{"id":"/features/endpoints/create-llm-24","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"For Image configuration, leave defaults as is. To add arguments, expand the command-line arguments section and add your own. SGLang arguments are listed here."},"1542":{"id":"/features/endpoints/create-llm-25","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Leave other configurations at their defaults, or refer to endpoint configurations for details."},"1543":{"id":"/features/endpoints/create-llm-26","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"We recommend setting up an access token for your endpoint instead of making it public."},"1544":{"id":"/features/endpoints/create-llm-27","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."},"1545":{"id":"/features/endpoints/create-llm-28","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"Test the endpoint in the playground by clicking the endpoint you created."},"1546":{"id":"/features/endpoints/create-llm-29","page_id":"/features/endpoints/create-llm","tags":[],"type":"text","url":"/features/endpoints/create-llm#create-llm-endpoints-with-sglang","content":"To access the endpoint via API, click the API tab on the endpoint detail page to find the API key and endpoint URL."},"1547":{"id":"/features/nodes/config","page_id":"/features/nodes/config","type":"page","content":"Node Group Configuration","tags":[],"url":"/features/nodes/config"},"1548":{"id":"/features/nodes/config-0","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Learn how to configure the node group on DGX Cloud Lepton."},"1549":{"id":"/features/nodes/config-1","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#storage","content":"Storage"},"1550":{"id":"/features/nodes/config-2","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#scheduling-policy","content":"Scheduling Policy"},"1551":{"id":"/features/nodes/config-3","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#recovery-policy","content":"Recovery Policy"},"1552":{"id":"/features/nodes/config-4","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#auto-pause-schedule","content":"Auto Pause Schedule"},"1553":{"id":"/features/nodes/config-5","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#auto-reboot","content":"Auto Reboot"},"1554":{"id":"/features/nodes/config-6","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#quota","content":"Quota"},"1555":{"id":"/features/nodes/config-7","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#workspace-quota","content":"Workspace Quota"},"1556":{"id":"/features/nodes/config-8","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#user-quota","content":"User Quota"},"1557":{"id":"/features/nodes/config-9","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#shapes","content":"Shapes"},"1558":{"id":"/features/nodes/config-10","page_id":"/features/nodes/config","type":"heading","tags":[],"url":"/features/nodes/config#container-image-acceleration","content":"Container Image Acceleration"},"1559":{"id":"/features/nodes/config-11","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"DGX Cloud Lepton allows you to configure the node group, including the following settings:"},"1560":{"id":"/features/nodes/config-12","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Storage"},"1561":{"id":"/features/nodes/config-13","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Scheduling Policy"},"1562":{"id":"/features/nodes/config-14","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Recovery Policy"},"1563":{"id":"/features/nodes/config-15","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Quota"},"1564":{"id":"/features/nodes/config-16","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Shapes"},"1565":{"id":"/features/nodes/config-17","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config","content":"Container Image Acceleration"},"1566":{"id":"/features/nodes/config-18","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#storage","content":"You can configure the storage for the node group, including Node Local Volume and Static NFS Volume."},"1567":{"id":"/features/nodes/config-19","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#storage","content":"Refer to the Storage guide for details."},"1568":{"id":"/features/nodes/config-20","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#scheduling-policy","content":"Click Scheduling Policy under the config menu to open a modal where you can configure scheduling."},"1569":{"id":"/features/nodes/config-21","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#scheduling-policy","content":"You can configure Gang Scheduling, which determines whether multiple replicas in the same group are scheduled together.\nBy default, this option is enabled."},"1570":{"id":"/features/nodes/config-22","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#recovery-policy","content":"Click Recovery Policy under the config menu to open a modal for configuration."},"1571":{"id":"/features/nodes/config-23","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#recovery-policy","content":"Recovery policy controls the following options:"},"1572":{"id":"/features/nodes/config-24","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#auto-pause-schedule","content":"Whether to automatically pause scheduling for unhealthy nodes."},"1573":{"id":"/features/nodes/config-25","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#auto-pause-schedule","content":"This prevents new workloads from being scheduled to unhealthy nodes and helps avoid prolonged unhealthy states."},"1574":{"id":"/features/nodes/config-26","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#auto-reboot","content":"Hardware issues can cause nodes to become unhealthy. When Auto Reboot is enabled, an unhealthy and drained node (no workloads) will be rebooted automatically to attempt recovery."},"1575":{"id":"/features/nodes/config-27","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#auto-reboot","content":"You can enable Auto Reboot only when Auto Pause Schedule is enabled."},"1576":{"id":"/features/nodes/config-28","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#quota","content":"Click Quota under the config menu to open the Node Group Quota page."},"1577":{"id":"/features/nodes/config-29","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#quota","content":"The page has two sections:"},"1578":{"id":"/features/nodes/config-30","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#quota","content":"Workspace Quota"},"1579":{"id":"/features/nodes/config-31","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#quota","content":"User Quota"},"1580":{"id":"/features/nodes/config-32","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"If a node group is shared by multiple workspaces, you may want to limit total resource usage for the current workspace."},"1581":{"id":"/features/nodes/config-33","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"For example, if you are in a workspace named \"test-workspace\", you'll see a quota card named \"test-workspace\" in the workspace quota section. By default, the quota isn't configured, which means the total resource usage in the node group isn't limited. The priority policy is set to \"Best Effort\"."},"1582":{"id":"/features/nodes/config-34","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Click the edit button in the top-right corner of the card to configure:"},"1583":{"id":"/features/nodes/config-35","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"GPU: Total GPU quota for the workspace (default: unlimited)."},"1584":{"id":"/features/nodes/config-36","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"CPU: Total CPU quota for the workspace (default: unlimited)."},"1585":{"id":"/features/nodes/config-37","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Memory: Total memory quota for the workspace in GB (default: unlimited)."},"1586":{"id":"/features/nodes/config-38","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Max Priority: Maximum priority for the workspace."},"1587":{"id":"/features/nodes/config-39","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Priority Policy: Priority policy for the workspace."},"1588":{"id":"/features/nodes/config-40","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Best Effort: Allocate resources according to workload priority as much as possible."},"1589":{"id":"/features/nodes/config-41","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#workspace-quota","content":"Strict: Allocate resources strictly according to workload priority. If you select \"Strict\", configure the \"Strict Queueing Scope\" as either \"within the workspace\" or \"across workspaces\"."},"1590":{"id":"/features/nodes/config-42","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"You can also configure the user quota for the node group to allocate resource quota to users to run workloads."},"1591":{"id":"/features/nodes/config-43","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"User: The user to allocate the quota to."},"1592":{"id":"/features/nodes/config-44","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"GPU: Total GPU quota for the user (default: unlimited)."},"1593":{"id":"/features/nodes/config-45","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"CPU: Total CPU quota for the user (default: unlimited)."},"1594":{"id":"/features/nodes/config-46","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"Memory: Total memory quota for the user in GB (default: unlimited)."},"1595":{"id":"/features/nodes/config-47","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#user-quota","content":"Max Priority: Maximum priority for the user."},"1596":{"id":"/features/nodes/config-48","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#shapes","content":"Shapes manage the resource configurations available in the node group. Refer to this documentation for details."},"1597":{"id":"/features/nodes/config-49","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"You can enable container image acceleration for the node group. With this feature, a node executing pods can pull container image data from other nodes in the group if the image is already cached there."},"1598":{"id":"/features/nodes/config-50","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"Click Image Acceleration under the config menu to open a configuration modal."},"1599":{"id":"/features/nodes/config-51","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"Select \"Enabled\" to turn on image acceleration for the node group. Select \"Disabled\" to turn it off."},"1600":{"id":"/features/nodes/config-52","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"After enabling, an image‑acceleration daemon is deployed on each node in the group. You can view it on the node detail page. The daemon uses one CPU core and one GB of memory by default, and up to two CPU cores and 16 GB of memory."},"1601":{"id":"/features/nodes/config-53","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"Configure the \"Cache Threshold\" in GB. This is the upper bound of root disk space available for image caching. If not specified, up to 80% of root disk space may be used."},"1602":{"id":"/features/nodes/config-54","page_id":"/features/nodes/config","tags":[],"type":"text","url":"/features/nodes/config#container-image-acceleration","content":"Currently, this feature is not available if nodes have the Docker subsystem installed. We recommend not installing Docker (Lepton installs Containerd). This limitation will be removed in a future release."},"1603":{"id":"/features/nodes/node-operations","page_id":"/features/nodes/node-operations","type":"page","content":"Node Operations","tags":[],"url":"/features/nodes/node-operations"},"1604":{"id":"/features/nodes/node-operations-0","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations","content":"Learn how to operate nodes in a node group on DGX Cloud Lepton."},"1605":{"id":"/features/nodes/node-operations-1","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#actions","content":"Actions"},"1606":{"id":"/features/nodes/node-operations-2","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#pause-schedule","content":"Pause Schedule"},"1607":{"id":"/features/nodes/node-operations-3","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#resume-schedule","content":"Resume Schedule"},"1608":{"id":"/features/nodes/node-operations-4","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#force-reboot","content":"Force Reboot"},"1609":{"id":"/features/nodes/node-operations-5","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#drain","content":"Drain"},"1610":{"id":"/features/nodes/node-operations-6","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#release-protection","content":"Release Protection"},"1611":{"id":"/features/nodes/node-operations-7","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#release","content":"Release"},"1612":{"id":"/features/nodes/node-operations-8","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#health-check","content":"Health Check"},"1613":{"id":"/features/nodes/node-operations-9","page_id":"/features/nodes/node-operations","type":"heading","tags":[],"url":"/features/nodes/node-operations#trigger-health-check","content":"Trigger Health Check"},"1614":{"id":"/features/nodes/node-operations-10","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations","content":"DGX Cloud Lepton provides node-level operations to help you manage nodes more effectively."},"1615":{"id":"/features/nodes/node-operations-11","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#pause-schedule","content":"Pause scheduling on a specific node.\nOnce paused, no new workloads are scheduled to the node."},"1616":{"id":"/features/nodes/node-operations-12","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#resume-schedule","content":"Resume scheduling on a specific node.\nOnce resumed, the node is available for new workloads."},"1617":{"id":"/features/nodes/node-operations-13","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#force-reboot","content":"Force a reboot of a specific node."},"1618":{"id":"/features/nodes/node-operations-14","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#force-reboot","content":"The node may remain unhealthy temporarily during the reboot process."},"1619":{"id":"/features/nodes/node-operations-15","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#drain","content":"Choose from two drain policies:"},"1620":{"id":"/features/nodes/node-operations-16","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#drain","content":"Graceful Drain (recommended): Migrates current workloads to other nodes, then terminates all workloads on the node."},"1621":{"id":"/features/nodes/node-operations-17","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#drain","content":"Drain: Evicts all pods from the node and terminates any active workloads."},"1622":{"id":"/features/nodes/node-operations-18","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#release-protection","content":"Protect a node from being released. When enabled, the node cannot be released through the dashboard or API."},"1623":{"id":"/features/nodes/node-operations-19","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#release","content":"Choose from two release policies:"},"1624":{"id":"/features/nodes/node-operations-20","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#release","content":"Graceful Release (recommended): Migrates workloads to an idle node, then terminates all remaining workloads and releases the node."},"1625":{"id":"/features/nodes/node-operations-21","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#release","content":"Release: Immediately evicts all pods, terminates any active workloads, and releases the node."},"1626":{"id":"/features/nodes/node-operations-22","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#health-check","content":"DGX Cloud Lepton provides health checks to monitor every node across the following dimensions:"},"1627":{"id":"/features/nodes/node-operations-23","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#health-check","content":"GPU: GPU temperature, utilization, power usage, and more."},"1628":{"id":"/features/nodes/node-operations-24","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#health-check","content":"General hardware: CPU, memory, disk, network, PCI, and more."},"1629":{"id":"/features/nodes/node-operations-25","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#health-check","content":"System: FUSE, OS, library status, and more."},"1630":{"id":"/features/nodes/node-operations-26","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#health-check","content":"Others: Tailscale, kubelet, etc."},"1631":{"id":"/features/nodes/node-operations-27","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#trigger-health-check","content":"Health checks run periodically, but you can also manually trigger a health check for a specific node."},"1632":{"id":"/features/nodes/node-operations-28","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#trigger-health-check","content":"Each health check card includes a trigger button with a play icon. Hover over the icon to see a tooltip showing when the check last ran."},"1633":{"id":"/features/nodes/node-operations-29","page_id":"/features/nodes/node-operations","tags":[],"type":"text","url":"/features/nodes/node-operations#trigger-health-check","content":"Press the button to trigger the health check."},"1634":{"id":"/features/nodes/reservations","page_id":"/features/nodes/reservations","type":"page","content":"Reservations","tags":[],"url":"/features/nodes/reservations"},"1635":{"id":"/features/nodes/reservations-0","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations","content":"Learn how to use reservations on DGX Cloud Lepton."},"1636":{"id":"/features/nodes/reservations-1","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#overview","content":"Overview"},"1637":{"id":"/features/nodes/reservations-2","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#creating-a-reservation","content":"Creating a Reservation"},"1638":{"id":"/features/nodes/reservations-3","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#admin-view-of-reservations","content":"Admin View of Reservations"},"1639":{"id":"/features/nodes/reservations-4","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#approval-workflows","content":"Approval Workflows"},"1640":{"id":"/features/nodes/reservations-5","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#deleting-reservations","content":"Deleting Reservations"},"1641":{"id":"/features/nodes/reservations-6","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"Scheduling Workloads with Reservations"},"1642":{"id":"/features/nodes/reservations-7","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#preemption-behavior","content":"Preemption Behavior"},"1643":{"id":"/features/nodes/reservations-8","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#user-roles-and-permissions","content":"User Roles and Permissions"},"1644":{"id":"/features/nodes/reservations-9","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#best-practices","content":"Best Practices"},"1645":{"id":"/features/nodes/reservations-10","page_id":"/features/nodes/reservations","type":"heading","tags":[],"url":"/features/nodes/reservations#frequently-asked-questions","content":"Frequently Asked Questions"},"1646":{"id":"/features/nodes/reservations-11","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations","content":"The DGX Cloud Lepton Reservation feature enables you to carve out a fixed number of nodes within a node group for exclusive use by selected users in a workspace. Reservations guarantee capacity, ensuring critical workloads have access to dedicated compute resources when needed."},"1647":{"id":"/features/nodes/reservations-12","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#overview","content":"A reservation is tied to a specific node group and workspace. Once created and approved, users included in the reservation can launch workloads—batch jobs, dev pods, or inference endpoints—directly against it. Reservations ensure predictable scheduling and isolation from other workloads."},"1648":{"id":"/features/nodes/reservations-13","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#overview","content":"If a reservation has available capacity, eligible workloads are placed there. Otherwise, DGX Cloud Lepton falls back to scheduling on unreserved nodes in the node group."},"1649":{"id":"/features/nodes/reservations-14","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"To create a reservation:"},"1650":{"id":"/features/nodes/reservations-15","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"Navigate to Workspace > Nodes > Config > Reservations."},"1651":{"id":"/features/nodes/reservations-16","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"Click Create Reservation."},"1652":{"id":"/features/nodes/reservations-17","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"Provide the reservation name, select users (either specific users or all users in the workspace), and enter the desired number of nodes."},"1653":{"id":"/features/nodes/reservations-18","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"Submit the request for admin approval."},"1654":{"id":"/features/nodes/reservations-19","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#creating-a-reservation","content":"Figure 1. User view of submitting a new reservation request."},"1655":{"id":"/features/nodes/reservations-20","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#admin-view-of-reservations","content":"Admins can view all active reservation requests, node assignments, and statuses under:"},"1656":{"id":"/features/nodes/reservations-21","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#admin-view-of-reservations","content":"Workspace > Nodes > Config > Reservations"},"1657":{"id":"/features/nodes/reservations-22","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#admin-view-of-reservations","content":"Figure 2. Admin dashboard showing list of reservations with status and actions."},"1658":{"id":"/features/nodes/reservations-23","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#approval-workflows","content":"Manual Approval: Admins can review and approve/deny requests."},"1659":{"id":"/features/nodes/reservations-24","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#approval-workflows","content":"Auto-Approval: Admins can define auto-approval thresholds based on node count. Requests within the limit are automatically granted."},"1660":{"id":"/features/nodes/reservations-25","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#approval-workflows","content":"Figure 3. Admin configuration screen showing auto-approval threshold settings."},"1661":{"id":"/features/nodes/reservations-26","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#approval-workflows","content":"Users can monitor approval status in the dashboard. Notifications are not yet available."},"1662":{"id":"/features/nodes/reservations-27","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#deleting-reservations","content":"Reservations do not expire automatically and must be deleted manually."},"1663":{"id":"/features/nodes/reservations-28","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#deleting-reservations","content":"Deletion can be performed by:"},"1664":{"id":"/features/nodes/reservations-29","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#deleting-reservations","content":"Workspace Admins"},"1665":{"id":"/features/nodes/reservations-30","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#deleting-reservations","content":"Users who are part of the reservation"},"1666":{"id":"/features/nodes/reservations-31","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"Users can target a reservation when launching workloads. DGX Cloud Lepton attempts to place the workload on the reservation's nodes first. If insufficient capacity is available:"},"1667":{"id":"/features/nodes/reservations-32","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"Without preemption: The job waits."},"1668":{"id":"/features/nodes/reservations-33","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"With preemption enabled: The job may evict lower-priority workloads—even on other reservations or unreserved nodes."},"1669":{"id":"/features/nodes/reservations-34","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"Figure 4. Selecting a reservation when launching a workload and choosing the option to burst into other reservations if available."},"1670":{"id":"/features/nodes/reservations-35","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#scheduling-workloads-with-reservations","content":"Workloads from other users not in the reservation cannot use the reserved nodes unless preemption rules allow it."},"1671":{"id":"/features/nodes/reservations-36","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"DGX Cloud Lepton supports workload preemption to manage contention:"},"1672":{"id":"/features/nodes/reservations-37","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"Preemption occurs only within the same reservation or for higher-priority workloads targeting overlapping resources."},"1673":{"id":"/features/nodes/reservations-38","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"If enabled, a running job may be preempted when:"},"1674":{"id":"/features/nodes/reservations-39","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"A higher-priority workload from the same reservation arrives."},"1675":{"id":"/features/nodes/reservations-40","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"The job is scheduled on permissible capacity and a reservation owner submits a workload."},"1676":{"id":"/features/nodes/reservations-41","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#preemption-behavior","content":"For training workloads, checkpointing is the user's responsibility. If a job is preempted and not checkpointed properly, progress may be lost."},"1677":{"id":"/features/nodes/reservations-42","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Role"},"1678":{"id":"/features/nodes/reservations-43","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"View Reservation"},"1679":{"id":"/features/nodes/reservations-44","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Create Reservation"},"1680":{"id":"/features/nodes/reservations-45","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Approve Reservation"},"1681":{"id":"/features/nodes/reservations-46","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Delete Reservation"},"1682":{"id":"/features/nodes/reservations-47","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Workspace Admin"},"1683":{"id":"/features/nodes/reservations-48","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ All reservations"},"1684":{"id":"/features/nodes/reservations-49","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅"},"1685":{"id":"/features/nodes/reservations-50","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅"},"1686":{"id":"/features/nodes/reservations-51","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ All reservations"},"1687":{"id":"/features/nodes/reservations-52","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Workspace User (in reservation)"},"1688":{"id":"/features/nodes/reservations-53","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ Own reservations"},"1689":{"id":"/features/nodes/reservations-54","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ (with rules)"},"1690":{"id":"/features/nodes/reservations-55","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"❌"},"1691":{"id":"/features/nodes/reservations-56","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ Own reservations"},"1692":{"id":"/features/nodes/reservations-57","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Workspace User (not in reservation)"},"1693":{"id":"/features/nodes/reservations-58","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"❌"},"1694":{"id":"/features/nodes/reservations-59","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"✅ (with rules)"},"1695":{"id":"/features/nodes/reservations-60","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"❌"},"1696":{"id":"/features/nodes/reservations-61","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"❌"},"1697":{"id":"/features/nodes/reservations-62","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Create: Any workspace user can create a reservation, but the request must either:"},"1698":{"id":"/features/nodes/reservations-63","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Include themselves in the reservation, or"},"1699":{"id":"/features/nodes/reservations-64","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Be made for all users in the workspaceOtherwise, the request will be rejected with the error:\"Reservations created by non-admin users must contain themselves or allow all users.\""},"1700":{"id":"/features/nodes/reservations-65","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"View: Only users included in the reservation (and all admins) can view or select it when launching workloads."},"1701":{"id":"/features/nodes/reservations-66","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#user-roles-and-permissions","content":"Approve/Delete: Only Workspace Admins can approve or delete any reservation. Users may delete only those they are part of."},"1702":{"id":"/features/nodes/reservations-67","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#best-practices","content":"Reserve only as many nodes as needed to avoid idle capacity."},"1703":{"id":"/features/nodes/reservations-68","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#best-practices","content":"Use auto-approval for self-service team workflows."},"1704":{"id":"/features/nodes/reservations-69","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#best-practices","content":"Enable preemption thoughtfully to maintain fairness and responsiveness."},"1705":{"id":"/features/nodes/reservations-70","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Can users outside a reservation see or use it?A: No. Only admins and users within the reservation can view or launch workloads against it."},"1706":{"id":"/features/nodes/reservations-71","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Can reservations span different GPU types (e.g., H100 + B200)?A: No. Reservations inherit the homogeneity constraint of the node group they are created in."},"1707":{"id":"/features/nodes/reservations-72","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Can I create a reservation on partially used nodes?A: No. A node must be fully idle—no CPU or GPU workload—before it can be added to a reservation."},"1708":{"id":"/features/nodes/reservations-73","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: What happens if I request more nodes than are available?A: The system will reject the request with an error message. No partial reservation is created."},"1709":{"id":"/features/nodes/reservations-74","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Do reservations expire?A: No. Reservations persist until explicitly deleted by an admin or a user who is part of the reservation."},"1710":{"id":"/features/nodes/reservations-75","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Can I increase the size of an existing reservation?A: Not at this time. To change node count, delete and re-create the reservation."},"1711":{"id":"/features/nodes/reservations-76","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: Can I include all users in the workspace in a reservation?A: Yes. You can select either specific users or all users in the workspace when creating a reservation."},"1712":{"id":"/features/nodes/reservations-77","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: How do I know if my workload is scheduled from a reservation?A: The workload detail view in the dashboard shows the reservation ID used for scheduling, if applicable."},"1713":{"id":"/features/nodes/reservations-78","page_id":"/features/nodes/reservations","tags":[],"type":"text","url":"/features/nodes/reservations#frequently-asked-questions","content":"Q: What does preemption look like in practice?A: If a job enables \"Allow burst to other reservation nodes\", it can use the free resources of nodes reserved by another reservation. Once a reservation-bound job is created, it will likely evict the former job. The evicted job may restart if checkpointing is supported."},"1714":{"id":"/features/nodes/resource-shape","page_id":"/features/nodes/resource-shape","type":"page","content":"Resource Shape","tags":[],"url":"/features/nodes/resource-shape"},"1715":{"id":"/features/nodes/resource-shape-0","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"Learn about the resource shapes on DGX Cloud Lepton."},"1716":{"id":"/features/nodes/resource-shape-1","page_id":"/features/nodes/resource-shape","type":"heading","tags":[],"url":"/features/nodes/resource-shape#default-gpu-shapes","content":"Default GPU Shapes"},"1717":{"id":"/features/nodes/resource-shape-2","page_id":"/features/nodes/resource-shape","type":"heading","tags":[],"url":"/features/nodes/resource-shape#custom-shapes","content":"Custom Shapes"},"1718":{"id":"/features/nodes/resource-shape-3","page_id":"/features/nodes/resource-shape","type":"heading","tags":[],"url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"List Shapes from CLI"},"1719":{"id":"/features/nodes/resource-shape-4","page_id":"/features/nodes/resource-shape","type":"heading","tags":[],"url":"/features/nodes/resource-shape#disable-or-enable-default-shapes","content":"Disable or Enable Default Shapes"},"1720":{"id":"/features/nodes/resource-shape-5","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"A resource shape specifies the combination of resources (such as GPU, CPU, memory, and storage) reserved for a workload."},"1721":{"id":"/features/nodes/resource-shape-6","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"For example, the shape 8xH100 GPUs, 128 CPUs, 1 TB Memory reserves 8 H100 GPUs, 128 CPU cores, and 1 TB of memory."},"1722":{"id":"/features/nodes/resource-shape-7","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"DGX Cloud Lepton supports two types of resource shapes:"},"1723":{"id":"/features/nodes/resource-shape-8","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"Default GPU shapes provided by Lepton."},"1724":{"id":"/features/nodes/resource-shape-9","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape","content":"Custom shapes defined by the node group administrator."},"1725":{"id":"/features/nodes/resource-shape-10","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#default-gpu-shapes","content":"DGX Cloud Lepton provides a set of default GPU shapes, designed for general-purpose workloads and focused on GPU allocation."},"1726":{"id":"/features/nodes/resource-shape-11","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#default-gpu-shapes","content":"For default GPU shapes, the upper bound for other resource dimensions (such as vCPU and memory) equals total available resources divided by the number of GPUs.\nFor example, on a node with 8 GPUs and 128 vCPUs, the default 1‑GPU shape is limited to 16 vCPUs (128 vCPUs divided by 8 GPUs)."},"1727":{"id":"/features/nodes/resource-shape-12","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#default-gpu-shapes","content":"While this is the upper bound, the GPU shape initially requests a much smaller CPU/memory footprint to leave room for CPU-only/memory shapes to be co-located on the same node.\nIf required, the GPU shape can then burst up to its maximum allocation as explained above.\nTo reiterate, a 1-GPU shape on an 8-GPU machine can burst up to one-eighth of the node's total CPU and memory."},"1728":{"id":"/features/nodes/resource-shape-13","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#default-gpu-shapes","content":"However, bursting introduces a risk of out‑of‑memory (OOM) events or GPU resource contention if a CPU‑only/memory shape is co‑located with a GPU workload, as both contend for the same limited resources."},"1729":{"id":"/features/nodes/resource-shape-14","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#default-gpu-shapes","content":"If your workload is sensitive to these issues, we recommend defining custom GPU shapes with CPU/memory limits that ensure the combined allocation between CPU‑only and GPU shapes does not exceed total system resources."},"1730":{"id":"/features/nodes/resource-shape-15","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Navigate to your node group details page, click the Config dropdown, and select Shapes. You'll be taken to a new page to define custom shapes."},"1731":{"id":"/features/nodes/resource-shape-16","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Click Add Shape in the top right to open a modal where you can define the shape's name, description, GPU, CPU, and memory."},"1732":{"id":"/features/nodes/resource-shape-17","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Name: The identifier for the shape, used to distinguish it when selecting or configuring resources."},"1733":{"id":"/features/nodes/resource-shape-18","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Number of CPU: The total number of CPU cores allocated to the shape."},"1734":{"id":"/features/nodes/resource-shape-19","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Memory: The amount of memory reserved for the shape, measured in MB."},"1735":{"id":"/features/nodes/resource-shape-20","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Storage: The amount of storage reserved for the shape, measured in GB."},"1736":{"id":"/features/nodes/resource-shape-21","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Number of GPU: The number of GPUs allocated to the shape. Set to \"None\" to create a CPU‑only shape."},"1737":{"id":"/features/nodes/resource-shape-22","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"GPU Type: The specific type of GPU assigned to the shape. Available types are determined by the node group."},"1738":{"id":"/features/nodes/resource-shape-23","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Description (Optional): An optional field to provide additional details or context about the shape."},"1739":{"id":"/features/nodes/resource-shape-24","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"After filling out the form, click Create to create the custom shape."},"1740":{"id":"/features/nodes/resource-shape-25","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#custom-shapes","content":"Unlike default GPU shapes, custom shapes reserve only the specified resources and won't burst beyond those limits."},"1741":{"id":"/features/nodes/resource-shape-26","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"To view available resource shapes using the CLI, first ensure the latest version of the DGX Cloud Lepton CLI is installed. Run the following command in a terminal with Python 3.10+:"},"1742":{"id":"/features/nodes/resource-shape-27","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"CLI version 0.26.4 or newer is required."},"1743":{"id":"/features/nodes/resource-shape-28","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"Once installed, run:"},"1744":{"id":"/features/nodes/resource-shape-29","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"This prompts you to authenticate with your DGX Cloud Lepton workspace. In a GUI‑supported environment such as a desktop, a browser opens to the credentials page in your workspace. Otherwise, a URL is displayed for you to open in a browser."},"1745":{"id":"/features/nodes/resource-shape-30","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."},"1746":{"id":"/features/nodes/resource-shape-31","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"To view your resource shapes, run:"},"1747":{"id":"/features/nodes/resource-shape-32","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#list-shapes-from-cli","content":"This command returns a table showing resource shapes available in each node group. Values in the Shapes column indicate resource names you can use when submitting jobs via the CLI or SDK."},"1748":{"id":"/features/nodes/resource-shape-33","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#disable-or-enable-default-shapes","content":"By default, DGX Cloud Lepton generates and enables default shapes for each node group, based on the accelerator type selected when creating the node group.\nIf these default shapes aren't suitable for your workload, and you want to use only custom shapes, you can disable default shapes."},"1749":{"id":"/features/nodes/resource-shape-34","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#disable-or-enable-default-shapes","content":"Navigate to your node group details page, click the Config dropdown, and select Shapes. You'll be taken to a page where you can disable or enable default shapes."},"1750":{"id":"/features/nodes/resource-shape-35","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#disable-or-enable-default-shapes","content":"In the Default Shapes section, view default shapes for the node group and disable or enable them by clicking Disable or Enable for each row."},"1751":{"id":"/features/nodes/resource-shape-36","page_id":"/features/nodes/resource-shape","tags":[],"type":"text","url":"/features/nodes/resource-shape#disable-or-enable-default-shapes","content":"After disabling a default shape, it appears as disabled in the Custom Shapes section. Users cannot select this shape when creating workloads unless you re‑enable it."},"1752":{"id":"/features/nodes/validations","page_id":"/features/nodes/validations","type":"page","content":"Node Validations","tags":[],"url":"/features/nodes/validations"},"1753":{"id":"/features/nodes/validations-0","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"Learn how to run node validations on DGX Cloud Lepton."},"1754":{"id":"/features/nodes/validations-1","page_id":"/features/nodes/validations","type":"heading","tags":[],"url":"/features/nodes/validations#run-a-validation","content":"Run a Validation"},"1755":{"id":"/features/nodes/validations-2","page_id":"/features/nodes/validations","type":"heading","tags":[],"url":"/features/nodes/validations#view-validation-result-and-logs","content":"View Validation Result and Logs"},"1756":{"id":"/features/nodes/validations-3","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"Validation is required for each node to confirm the health and operational status of the entire node group."},"1757":{"id":"/features/nodes/validations-4","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"DGX Cloud Lepton provides a simple way to run validations to check node group health, including:"},"1758":{"id":"/features/nodes/validations-5","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"IB Ping"},"1759":{"id":"/features/nodes/validations-6","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"Ethernet Ping"},"1760":{"id":"/features/nodes/validations-7","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"NCCL Test"},"1761":{"id":"/features/nodes/validations-8","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"GPU Burn"},"1762":{"id":"/features/nodes/validations-9","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"FIO Test"},"1763":{"id":"/features/nodes/validations-10","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations","content":"DCGM Diagnostics"},"1764":{"id":"/features/nodes/validations-11","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#run-a-validation","content":"You can run a node validation and view past results for the node group under the Validations tab."},"1765":{"id":"/features/nodes/validations-12","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#run-a-validation","content":"Click the Run node validation dropdown and select the validation type you want to run."},"1766":{"id":"/features/nodes/validations-13","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#run-a-validation","content":"An indicator shows the latest result for each validation type: green for running, gray for completed, and red for failed."},"1767":{"id":"/features/nodes/validations-14","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#view-validation-result-and-logs","content":"Click the name field in the validation table row to open the validation result page, where you can view results and logs."},"1768":{"id":"/features/nodes/validations-15","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#view-validation-result-and-logs","content":"The Result tab shows the final validation result."},"1769":{"id":"/features/nodes/validations-16","page_id":"/features/nodes/validations","tags":[],"type":"text","url":"/features/nodes/validations#view-validation-result-and-logs","content":"The Logs tab shows validation logs categorized by node. Select a node from the dropdown in the top-right corner to change the view."},"1770":{"id":"/features/utilities/domains","page_id":"/features/utilities/domains","type":"page","content":"Domains","tags":[],"url":"/features/utilities/domains"},"1771":{"id":"/features/utilities/domains-0","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains","content":"Learn how to use domains on DGX Cloud Lepton."},"1772":{"id":"/features/utilities/domains-1","page_id":"/features/utilities/domains","type":"heading","tags":[],"url":"/features/utilities/domains#create-a-domain","content":"Create a domain"},"1773":{"id":"/features/utilities/domains-2","page_id":"/features/utilities/domains","type":"heading","tags":[],"url":"/features/utilities/domains#manage-domain-routing-rules","content":"Manage domain routing rules"},"1774":{"id":"/features/utilities/domains-3","page_id":"/features/utilities/domains","type":"heading","tags":[],"url":"/features/utilities/domains#route-traffic-to-endpoints-by-weights","content":"Route traffic to endpoints by weights"},"1775":{"id":"/features/utilities/domains-4","page_id":"/features/utilities/domains","type":"heading","tags":[],"url":"/features/utilities/domains#shadow-traffic-to-an-endpoint","content":"Shadow traffic to an endpoint"},"1776":{"id":"/features/utilities/domains-5","page_id":"/features/utilities/domains","type":"heading","tags":[],"url":"/features/utilities/domains#monitor-traffic","content":"Monitor traffic"},"1777":{"id":"/features/utilities/domains-6","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains","content":"Domains manage traffic routing to your endpoints. When you create an endpoint, a default domain is automatically created. You can also create domains manually to route traffic to multiple endpoints based on assigned weights, or to shadow a percentage of traffic to a different deployment for testing."},"1778":{"id":"/features/utilities/domains-7","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#create-a-domain","content":"A domain is automatically created for each endpoint by default."},"1779":{"id":"/features/utilities/domains-8","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#create-a-domain","content":"To create a domain manually, navigate to the domains section under the Utilities tab and click the Add Domain button. In the pop-up window, specify the domain you want to route traffic to."},"1780":{"id":"/features/utilities/domains-9","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#create-a-domain","content":"After creation, the domain appears in Pending Update status. Add the CNAME record to your DNS provider to point to the domain."},"1781":{"id":"/features/utilities/domains-10","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#create-a-domain","content":"If you recently added a domain or updated the DNS record, it may take a few minutes for the status to change from PENDING to ACTIVE."},"1782":{"id":"/features/utilities/domains-11","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#manage-domain-routing-rules","content":"You can manage routing rules for a domain by clicking the Edit button."},"1783":{"id":"/features/utilities/domains-12","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#route-traffic-to-endpoints-by-weights","content":"You can add multiple endpoints to a domain and assign weights to each one. Traffic is routed to endpoints based on the assigned weights."},"1784":{"id":"/features/utilities/domains-13","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#shadow-traffic-to-an-endpoint","content":"Shadowing endpoints redirect a percentage of traffic to a different deployment. This feature is useful for testing new deployments in production. Currently, only one shadowing endpoint is allowed per domain."},"1785":{"id":"/features/utilities/domains-14","page_id":"/features/utilities/domains","tags":[],"type":"text","url":"/features/utilities/domains#monitor-traffic","content":"You can monitor domain traffic by clicking the domain name. Available metrics include QPM, request size, response size, and more."},"1786":{"id":"/features/utilities/fine-tuning","page_id":"/features/utilities/fine-tuning","type":"page","content":"Fine-Tuning","tags":[],"url":"/features/utilities/fine-tuning"},"1787":{"id":"/features/utilities/fine-tuning-0","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning","content":"No-code interface for fine-tuning Large Language Models"},"1788":{"id":"/features/utilities/fine-tuning-1","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#model-architecture-and-fine-tuning-technique-support","content":"Model Architecture and Fine-Tuning Technique Support"},"1789":{"id":"/features/utilities/fine-tuning-2","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#accessing-dgx-cloud-lepton-fine-tuning","content":"Accessing DGX Cloud Lepton Fine-Tuning"},"1790":{"id":"/features/utilities/fine-tuning-3","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#technique","content":"Technique"},"1791":{"id":"/features/utilities/fine-tuning-4","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#name","content":"Name"},"1792":{"id":"/features/utilities/fine-tuning-5","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#resource","content":"Resource"},"1793":{"id":"/features/utilities/fine-tuning-6","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"Determining GPU Resources"},"1794":{"id":"/features/utilities/fine-tuning-7","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#storage","content":"Storage"},"1795":{"id":"/features/utilities/fine-tuning-8","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#model","content":"Model"},"1796":{"id":"/features/utilities/fine-tuning-9","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#load-from-hugging-face","content":"Load from Hugging Face"},"1797":{"id":"/features/utilities/fine-tuning-10","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#load-from-storage","content":"Load from Storage"},"1798":{"id":"/features/utilities/fine-tuning-11","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#datasets","content":"Datasets"},"1799":{"id":"/features/utilities/fine-tuning-12","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#load-from-hugging-face-1","content":"Load from Hugging Face"},"1800":{"id":"/features/utilities/fine-tuning-13","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#load-from-storage-1","content":"Load from Storage"},"1801":{"id":"/features/utilities/fine-tuning-14","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#dataset-mapping","content":"Dataset Mapping"},"1802":{"id":"/features/utilities/fine-tuning-15","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#validation-dataset","content":"Validation Dataset"},"1803":{"id":"/features/utilities/fine-tuning-16","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#checkpoint-output-path","content":"Checkpoint Output Path"},"1804":{"id":"/features/utilities/fine-tuning-17","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#training-configuration","content":"Training Configuration"},"1805":{"id":"/features/utilities/fine-tuning-18","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#advanced-configuration","content":"Advanced Configuration"},"1806":{"id":"/features/utilities/fine-tuning-19","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#launching-the-job","content":"Launching the Job"},"1807":{"id":"/features/utilities/fine-tuning-20","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#monitoring-the-job","content":"Monitoring the Job"},"1808":{"id":"/features/utilities/fine-tuning-21","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#view-checkpoints","content":"View Checkpoints"},"1809":{"id":"/features/utilities/fine-tuning-22","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#deploying-the-model-for-inference","content":"Deploying the Model for Inference"},"1810":{"id":"/features/utilities/fine-tuning-23","page_id":"/features/utilities/fine-tuning","type":"heading","tags":[],"url":"/features/utilities/fine-tuning#troubleshooting","content":"Troubleshooting"},"1811":{"id":"/features/utilities/fine-tuning-24","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning","content":"DGX Cloud Lepton provides a no-code interface for fine-tuning large language models (LLMs). Powered by NVIDIA NeMo Automodel, users can fine-tune most LLMs available on Hugging Face Hub with support for dozens of the most popular model architectures. Fine-tuning on DGX Cloud Lepton provides an easy way to experiment with tailoring the latest open source models to your unique needs. The tool runs directly on your hardware connected to DGX Cloud Lepton, providing a single-pane-of-glass for training, tuning, and deploying models in your own private workspace."},"1812":{"id":"/features/utilities/fine-tuning-25","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#model-architecture-and-fine-tuning-technique-support","content":"DGX Cloud Lepton Fine-Tuning is designed to support a wide range of models including those built on some of the most popular architectures, such as Gemma 3, Llama, Mixtral/Mistral, Qwen3, Nemotron, and more. Base models can either be loaded directly from Hugging Face Hub, or loaded from mounted storage on DGX Cloud Lepton."},"1813":{"id":"/features/utilities/fine-tuning-26","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#model-architecture-and-fine-tuning-technique-support","content":"The Fine-Tuning feature currently supports both Supervised Fine-Tuning (SFT) and Low-Rank Adaptation (LoRA), giving users flexibility depending on their use-case. These techniques are described in more detail below."},"1814":{"id":"/features/utilities/fine-tuning-27","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#accessing-dgx-cloud-lepton-fine-tuning","content":"The DGX Cloud Lepton Fine-Tuning feature can be accessed by clicking the Fine-Tuning button in the Explore page, or navigating directly to the Fine-Tuning page under Utilities."},"1815":{"id":"/features/utilities/fine-tuning-28","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#accessing-dgx-cloud-lepton-fine-tuning","content":"The Fine-Tuning tab displays a list of jobs that have been run previously, if any."},"1816":{"id":"/features/utilities/fine-tuning-29","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#accessing-dgx-cloud-lepton-fine-tuning","content":"To start a new fine-tuning job, click the green Create Fine-tune button on the page. This will open a new form to define the fine-tuning settings which are explained in the following sub-sections."},"1817":{"id":"/features/utilities/fine-tuning-30","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"At the top of the form is an option for selecting the fine-tuning technique, either Supervised Fine-Tuning (SFT) or LoRA which is a Parameter-Efficient Fine-Tuning (PEFT) technique. SFT is selected by default."},"1818":{"id":"/features/utilities/fine-tuning-31","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"SFT updates all or nearly all of the base model's weights using a supplied dataset containing many labeled examples. This approach builds on the base model's existing knowledge while allowing the fine-tuned model to specialize in specific domains or refine the output based on human preferences. Since it updates the full base model, SFT requires significant compute resources, particularly for very large models. The output is a full model checkpoint that can be deployed directly for inference."},"1819":{"id":"/features/utilities/fine-tuning-32","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"Low-Rank Adaptation (LoRA) is a PEFT technique that freezes the base model weights and adds additional low-rank adapter modules, making it more computationally efficient and requiring fewer resources. This creates a small model adapter file that instructs a deployed model how to respond with the new weights. LoRA is best suited for targeted use cases and requires fewer examples to learn the expected output structure."},"1820":{"id":"/features/utilities/fine-tuning-33","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"In summary, choose SFT if:"},"1821":{"id":"/features/utilities/fine-tuning-34","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You have lots of compute to fine-tune the model."},"1822":{"id":"/features/utilities/fine-tuning-35","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You desire deep expertise in a broader field and accuracy is crucial."},"1823":{"id":"/features/utilities/fine-tuning-36","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You have a dataset comprising thousands of examples of labeled data."},"1824":{"id":"/features/utilities/fine-tuning-37","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"Choose LoRA if:"},"1825":{"id":"/features/utilities/fine-tuning-38","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You are compute constrained."},"1826":{"id":"/features/utilities/fine-tuning-39","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You have a very targeted use-case with very specific examples."},"1827":{"id":"/features/utilities/fine-tuning-40","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#technique","content":"You have a small dataset with hundreds of examples."},"1828":{"id":"/features/utilities/fine-tuning-41","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#name","content":"Give the fine-tuning job a name, such as my-finetuning-job. This is how the job will be referenced in the fine-tuning jobs list in the UI."},"1829":{"id":"/features/utilities/fine-tuning-42","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#resource","content":"In this section, specify the Node Group and the resources to allocate for the job."},"1830":{"id":"/features/utilities/fine-tuning-43","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#resource","content":"The Node Group is the specific compute resources the job will run on. If desired, select a subset of nodes to run the job on and/or a job priority to manage how the jobs get scheduled."},"1831":{"id":"/features/utilities/fine-tuning-44","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"Select the number and type of GPUs to run on. While there is no standard rule for determining the required number of GPUs for any arbitrary model or dataset, the general guidelines are:"},"1832":{"id":"/features/utilities/fine-tuning-45","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"Small models (that is, less than 8B parameters for dense models):"},"1833":{"id":"/features/utilities/fine-tuning-46","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"LoRA: Single 80+ GB GPU"},"1834":{"id":"/features/utilities/fine-tuning-47","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"SFT: Eight (8) 80+ GB GPUs"},"1835":{"id":"/features/utilities/fine-tuning-48","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"Larger models: Additional resources will be required"},"1836":{"id":"/features/utilities/fine-tuning-49","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#determining-gpu-resources","content":"Multiple nodes may be necessary, especially for SFT on larger models. To increase the number of nodes, update the Worker option to the desired amount."},"1837":{"id":"/features/utilities/fine-tuning-50","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#storage","content":"Shared storage is required for checkpoints to be available after fine-tuning. The selected node group needs a shared storage volume connected to it which can be mounted in jobs. Select the desired volume and where it should be mounted inside the running job."},"1838":{"id":"/features/utilities/fine-tuning-51","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#storage","content":"If storage is not added, any generated checkpoints will be lost after the job completes."},"1839":{"id":"/features/utilities/fine-tuning-52","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#model","content":"Specify the base model to fine-tune with your dataset. There are two main options: loading from Hugging Face Hub or loading from shared storage."},"1840":{"id":"/features/utilities/fine-tuning-53","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face","content":"To load a model directly from Hugging Face Hub, click the Load from Hugging Face button. The field will update with a drop-down list where you can specify the model to fine-tune. The drop-down list contains several of the most popular text-generation LLMs available on Hugging Face Hub. You can either click an item in the list or start entering the name of a model's Hugging Face repo ID and selecting it from the list. For example, to use NVIDIA's Nemotron-Nano-9B-v2, either enter the full repo ID of nvidia/NVIDIA-Nemotron-Nano-9B-v2 in the field, or start typing Nemotron and select the model from the list."},"1841":{"id":"/features/utilities/fine-tuning-54","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face","content":"When loading a model from Hugging Face, the fine-tuning job will automatically download the model from Hugging Face into local container storage and begin the fine-tuning process using that model as a basis."},"1842":{"id":"/features/utilities/fine-tuning-55","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face","content":"Note that if you are trying to access a gated model which requires approval, you must add your Hugging Face token in the job submission form. More on this below."},"1843":{"id":"/features/utilities/fine-tuning-56","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage","content":"Only models in *safetensors format are currently supported."},"1844":{"id":"/features/utilities/fine-tuning-57","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage","content":"Users can also load a model directly from shared storage in case the model hasn't been uploaded to Hugging Face, such as models that shouldn't be shared publicly or early previews that were trained directly on the platform."},"1845":{"id":"/features/utilities/fine-tuning-58","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage","content":"To load a model from storage, ensure the desired model checkpoint is available on the shared storage that was mounted in the resource configuration section above. The model must be saved to shared storage prior to launching the fine-tuning job."},"1846":{"id":"/features/utilities/fine-tuning-59","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage","content":"After selecting the Load from storage option, the field will update with a Model file path textbox. Specify the absolute path to the directory where the model is saved in shared storage. For example, if you mounted a full NFS share as /nfs-storage in the resource configuration and your checkpoint is available at /my/custom/model inside the NFS, this path would be /nfs-storage/my/custom/model (as /nfs-storage inside the container maps to / of the NFS share). This assumes that the /nfs-storage/my/custom/model directory contains *.safetensors file(s), configs, and any other data that is required for loading the model with Hugging Face Transformers."},"1847":{"id":"/features/utilities/fine-tuning-60","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#datasets","content":"Fine-tuning an LLM requires a dataset for the model to learn from. Datasets must have at least one input and one output field. As with models, datasets can either be loaded directly from Hugging Face or from shared storage mounted in the container."},"1848":{"id":"/features/utilities/fine-tuning-61","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face-1","content":"Datasets can be loaded directly from Hugging Face hub by clicking the Load from Hugging Face option and specifying the repo ID of a dataset, such as nvidia/OpenMathReasoning. When the job launches, this will download the requested dataset into local container storage and will be pre-processed before fine-tuning begins. You can also select a dataset from the drop-down list which contains several of the most popular datasets. Note that some of these datasets might not be a good target for fine-tuning, so only select datasets that you have inspected previously."},"1849":{"id":"/features/utilities/fine-tuning-62","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face-1","content":"After selecting the dataset, optionally add the dataset split to use for fine-tuning. Most datasets have a default split of train which is used for training, but not all datasets will have this name and it needs to be specified. For example, the nvidia/OpenMathReasoning dataset has splits of cot, tir, genselect, and additional_problems. To fine-tune against the cot split, enter cot for the split."},"1850":{"id":"/features/utilities/fine-tuning-63","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-hugging-face-1","content":"Lastly, the Dataset Mapping needs to be specified. See below for more information on these fields."},"1851":{"id":"/features/utilities/fine-tuning-64","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage-1","content":"As with loading a model from storage, loading datasets from storage requires the dataset to be saved and pre-processed on shared storage that was mounted in the Resources section above. Currently, only .jsonl datasets are supported. The dataset needs to include at least one input and an output field. This must be done prior to launching the fine-tuning job."},"1852":{"id":"/features/utilities/fine-tuning-65","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage-1","content":"To load the dataset from storage, specify the absolute path to the dataset file that is mounted in storage, such as /nfs-storage/my/custom/dataset.jsonl, assuming your shared storage was mounted as /nfs-storage."},"1853":{"id":"/features/utilities/fine-tuning-66","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#load-from-storage-1","content":"Lastly, the Dataset Mapping needs to be specified. See below for more information on these fields."},"1854":{"id":"/features/utilities/fine-tuning-67","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"Regardless of how the dataset is loaded, the columns in the dataset need to be mapped to the expected fields for the code to understand. This is referred to as Dataset Mapping. The fine-tuning code will adjust each prompt in the dataset to the following format with the {context} field being optional:"},"1855":{"id":"/features/utilities/fine-tuning-68","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"If your dataset already has an input column named question and an output column named answer, you can copy those fields for the columns. If not, specify the column name in the dataset that maps to each field. For example:"},"1856":{"id":"/features/utilities/fine-tuning-69","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"Context: Add this field if you have required context for the prompt. An example is a paragraph with contextual information, then a Question and Answer which can be found directly in that context. If no context is included, this can be left blank."},"1857":{"id":"/features/utilities/fine-tuning-70","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"Question: This is your input field or prompt. Note that it doesn't have to be a question—it is simply the input which maps to the Answer field below."},"1858":{"id":"/features/utilities/fine-tuning-71","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"Answer: This is your output field and is required. Note that it doesn't have to be a numerical/factual answer—it is simply the expected response to the prompt."},"1859":{"id":"/features/utilities/fine-tuning-72","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"Answer is a required field. At least one of Question or Context must be provided for input."},"1860":{"id":"/features/utilities/fine-tuning-73","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"In the case of the nvidia/OpenMathReasoning dataset, there isn't a question or answer column present and they need to be overridden. This dataset contains several columns, but the two important ones for fine-tuning are problem and generated_solution. problem is the input which maps to the Question field, and generated_solution is the response which maps to the Answer field."},"1861":{"id":"/features/utilities/fine-tuning-74","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#dataset-mapping","content":"A picture showing the fields filled out is below:"},"1862":{"id":"/features/utilities/fine-tuning-75","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#validation-dataset","content":"If you have a unique validation dataset, this process can be repeated specifically for inputting validation data. Refer to the steps above on filling out this information if needed."},"1863":{"id":"/features/utilities/fine-tuning-76","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#checkpoint-output-path","content":"Specify the path to save all training logs, data, and the final checkpoints. This must be an absolute path and should be on mounted shared storage for the checkpoints to persist after the job is completed."},"1864":{"id":"/features/utilities/fine-tuning-77","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#checkpoint-output-path","content":"If you mounted your storage as /nfs-storage, the checkpoint path should begin with /nfs-storage/."},"1865":{"id":"/features/utilities/fine-tuning-78","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#checkpoint-output-path","content":"As training progresses, checkpoints will be periodically saved after a configurable number of steps. All checkpoints are in the *safetensors format for compatibility with Hugging Face and inference serving engines. Inside the checkpoint directory, there will be several files and sub-directories pertaining to the fine-tuning job. In particular, you will find several directories named epoch_X_step_YYYY where X is the current epoch number and YYYY is the current global step. Each of these directories contain model information from the indicated step at that time. The checkpoint can be found in the model/consolidated sub-directory within each epoch_X_step_YYYY directory. For example, if you mounted your shared storage as /nfs-storage and specified your checkpoint path as /nfs-storage/fine-tuning/my-fine-tune-model, the full checkpoint would be saved in /nfs-storage/fine-tuning/my-fine-tune-model/epoch_0_step_1000/model/consolidated. This will be the same any time the checkpoint is saved, so after another 1,000 steps, you will also have /nfs-storage/fine-tuning/my-fine-tune-model/epoch_0_step_2000/model/consolidated."},"1866":{"id":"/features/utilities/fine-tuning-79","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#checkpoint-output-path","content":"Each of these consolidated directories contain the full *safetensors files and associated configs so they can be deployed for inference. If you choose to deploy your model or publish it on Hugging Face, you must specify the consolidated directory."},"1867":{"id":"/features/utilities/fine-tuning-80","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"The Training Configuration field contains several hyperparameters that can be modified if needed. Any optional fields left blank will be filled with default values. The settings are as follows:"},"1868":{"id":"/features/utilities/fine-tuning-81","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Epochs: This is the number of full passes through the training dataset to complete. This must be a positive integer. Typical ranges are from 1-10. Note that if the Max Steps setting takes precedence over Epochs if the max steps has been reached prior to the desired number of epochs. Minimum: 1. Default: 1."},"1869":{"id":"/features/utilities/fine-tuning-82","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Max Steps: This is the total number of backward passes to complete as the model learns. Once the Max Steps value is reached, fine-tuning will complete. This must be a positive integer. This can range from 10s to millions of steps depending on your dataset and other settings. If not specified, fine-tuning will run until the Epochs value is reached. Minimum: 1. Default: Infinite."},"1870":{"id":"/features/utilities/fine-tuning-83","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Val Every Steps: This is how often to run a validation pass using the validation dataset to determine how the model is learning over time. This must be a positive integer. Typical ranges are from 50 - a few thousand steps depending on your dataset size. Minimum: 1. Default: 50."},"1871":{"id":"/features/utilities/fine-tuning-84","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Context Parallelism Size: This splits your context across GPUs to lower GPU memory usage. This is useful for long input and/or output sequences in your dataset, particularly those that include reasoning traces as it reduces the amount of memory required for each GPU to process the prompts. This must be a power-of-two (1, 2, 4, 8, ...). This multiplied by the Tensor Parallelism Size must be less than or equal to the total number of GPUs for your job. If you have long input or output sequences, typically 32K or greater in length, it is recommended to add context parallelism. Minimum: 1. Default: 1."},"1872":{"id":"/features/utilities/fine-tuning-85","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Tensor Parallelism Size: This distributes your model across GPUs to lower GPU memory usage. This is useful with larger models as it lowers the amount of GPU memory required to run the model. This must be a power-of-two (1, 2, 4, 8, ...). This multiplied by the Context Parallelism Size must be less than or equal to the total number of GPUs for your job. If your model is larger than 10B parameters, it is recommended to use tensor parallelism. Minimum: 1. Default: 1."},"1873":{"id":"/features/utilities/fine-tuning-86","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Seed: This is the random seed used during fine-tuning. All other settings equal, fine-tuning will be deterministic when the same seed is used between runs. This must be a non-negative integer. Minimum: 0. Maximum: 65535. Default: 42."},"1874":{"id":"/features/utilities/fine-tuning-87","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Global Batch Size: This is the number of sequences to process during each step of the training process. The global batch size is divided by the number of GPUs so each GPU processes Batch / num GPU sequences during a training step. Higher batch sizes may result in greater training throughput at the cost of additional GPU memory. This must be a positive integer and is typically a power-of-two (1, 2, 4, 8, ...). This must be greater than or equal to the number of GPUs in the job. Minimum: 1. Default: 16."},"1875":{"id":"/features/utilities/fine-tuning-88","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Local Batch Size: This is the amount of pipelining to perform on each GPU. Increasing the local batch size can improve throughput at the cost of additional GPU memory. This must be a positive integer and is most commonly 1 but additional throughput could be gained with 2 or 4 here as well. Minimum: 1. Default: 1."},"1876":{"id":"/features/utilities/fine-tuning-89","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Learning Rate: This is the learning rate used by the optimizer. It must be a positive float. Values typically range from 1e-7 to 1e-3 with most jobs being between 1e-5 and 5e-4. There isn't a perfect science for the optimal learning rate, so experimenting with different values could yield improved accuracy. Minimum: 0. Maximum: 1. Default: 0.0001."},"1877":{"id":"/features/utilities/fine-tuning-90","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Min Learning Rate: This is the minimum learning rate used by the optimizer. It will decay to the minimum as the training progresses. This must be a positive float. Common practice has the minimum learning rate being 10x smaller than the maximum learning rate. Minimum: 0. Maximum: 1. Default: 0.000001."},"1878":{"id":"/features/utilities/fine-tuning-91","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Checkpoint Every Steps: This is how frequently the fine-tuning job will save a checkpoint. After every N steps, the checkpoint will be saved in the epoch_X_step_YYYY/model/consolidated directory within the checkpoint directory. Minimum: 1. Default: 1000."},"1879":{"id":"/features/utilities/fine-tuning-92","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"Tensor Parallelism multiplied by Context Parallelism must be less than or equal to the total number of GPUs in your job."},"1880":{"id":"/features/utilities/fine-tuning-93","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"If you selected LoRA for your fine-tuning technique, you will see two additional settings at the top of the box:"},"1881":{"id":"/features/utilities/fine-tuning-94","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"LoRA Rank: This is the dimension to use for LoRA while fine-tuning and corresponds to the number of parameters in the adapter layers. Larger numbers tend to yield better results. Typical values are powers-of-two (1, 2, 4, 8, ...). This must be a positive integer. Minimum: 1. Default: 16."},"1882":{"id":"/features/utilities/fine-tuning-95","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#training-configuration","content":"LoRA Alpha: This is the alpha parameter for scaling the adaptation strength. Higher values give stronger weight to the adapter layers. Typical values are powers-of-two (1, 2, 4, 8, ...). This must be a positive integer. Minimum: 1. Default: 32."},"1883":{"id":"/features/utilities/fine-tuning-96","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#advanced-configuration","content":"The Advanced Configuration section contains a few additional settings, mainly the Weights and Biases (W&B) integration. The fine-tuning feature has optional support for logging training metrics to W&B servers for an easy way to monitor training progress and compare different runs."},"1884":{"id":"/features/utilities/fine-tuning-97","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#advanced-configuration","content":"If you would like to log results with W&B, specify your desired Project name and the name of the Run for the fine-tuning job to be visible under in W&B. Next, add your W&B API key by either selecting it from the drop-down list or clicking the green + Add key button and following the prompts to add your W&B API key."},"1885":{"id":"/features/utilities/fine-tuning-98","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#advanced-configuration","content":"If the project, run, and API key for W&B are set in this section, your fine-tuning job will automatically be synced with W&B for further viewing."},"1886":{"id":"/features/utilities/fine-tuning-99","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#advanced-configuration","content":"In addition to W&B integration, you can also choose to add labels to your job or change the visibility as needed."},"1887":{"id":"/features/utilities/fine-tuning-100","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"Once all settings in the form have been configured, click the green Create button at the bottom of the page. This will schedule a fine-tuning job on the cluster and run it once resources are ready. You will be taken back to the Fine-tune job page and should see the job listed at the top of the queue per the image above."},"1888":{"id":"/features/utilities/fine-tuning-101","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"In the job tile you can view various information including:"},"1889":{"id":"/features/utilities/fine-tuning-102","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The requested GPU resources."},"1890":{"id":"/features/utilities/fine-tuning-103","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"Node group the job is running on."},"1891":{"id":"/features/utilities/fine-tuning-104","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"When the job was created and by whom."},"1892":{"id":"/features/utilities/fine-tuning-105","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"A list of the training parameters for the job."},"1893":{"id":"/features/utilities/fine-tuning-106","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The base model used for fine-tuning."},"1894":{"id":"/features/utilities/fine-tuning-107","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The training dataset."},"1895":{"id":"/features/utilities/fine-tuning-108","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"Where the checkpoint is saved."},"1896":{"id":"/features/utilities/fine-tuning-109","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The container version the job is running in."},"1897":{"id":"/features/utilities/fine-tuning-110","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The shared storage mounted in the job."},"1898":{"id":"/features/utilities/fine-tuning-111","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"The typical state progression for jobs is Queued -> Starting -> Running -> Completed. The job will first be in the queue until resources are available. It will then transition to Starting once resources have been allocated. Note that a job could be in the Starting state for a while if the image needs to be pulled from the registry."},"1899":{"id":"/features/utilities/fine-tuning-112","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#launching-the-job","content":"Once the container is ready, the job will be Running and the fine-tuning process begins. After fine-tuning finishes, it will transition to Completed and the resources are freed for other jobs."},"1900":{"id":"/features/utilities/fine-tuning-113","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#monitoring-the-job","content":"When the fine-tuning process starts, your job will be in the Running state. Clicking the job in the list will bring up more details. To view the live output including training progress, click the Logs button. This will display progress after each step indicating the latest training loss, the GPU memory usage at that step, how many steps have been completed, and more."},"1901":{"id":"/features/utilities/fine-tuning-114","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#monitoring-the-job","content":"If you added your W&B credentials, you can view the training metrics at wandb.ai as in the image below:"},"1902":{"id":"/features/utilities/fine-tuning-115","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#monitoring-the-job","content":"Ideally, you want your train_loss and val_loss to decrease over time like in the example above. This indicates the model is learning the target dataset."},"1903":{"id":"/features/utilities/fine-tuning-116","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#view-checkpoints","content":"While the job is running, you can open a terminal directly in the container to view checkpoints and other files."},"1904":{"id":"/features/utilities/fine-tuning-117","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#view-checkpoints","content":"To do so, open the job details page by clicking the job in the Fine-tunes list. Click the Replicas tab to list all containers and click the Terminal button for the first container to open a bash session inside the container at the bottom of the page."},"1905":{"id":"/features/utilities/fine-tuning-118","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#view-checkpoints","content":"To find your checkpoint, navigate to the location specified in Checkpoint Output Path section during job creation. For example, if you put /nfs-storage/fine-tuning/my-fine-tune-model for your checkpoint path, navigate to that directory with:"},"1906":{"id":"/features/utilities/fine-tuning-119","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#view-checkpoints","content":"If you run ls in that directory you will find a list of sub-directories named epoch_X_step_YYYY where X is the epoch number and YYYY is the global step number. Each of these sub-directories contains the checkpoints at the indicated step count."},"1907":{"id":"/features/utilities/fine-tuning-120","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#view-checkpoints","content":"Navigate into the latest sub-directory which represents the highest step count. This is your most recent checkpoint. The final consolidated checkpoint will be located in the model/consolidated directory inside of the latest epoch_X_step_YYYY sub-directory."},"1908":{"id":"/features/utilities/fine-tuning-121","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#deploying-the-model-for-inference","content":"After the fine-tuning job is completed, your checkpoint will be saved in the specified shared storage location. View the logs to see where the checkpoint is located - it will be listed towards the end of the logs."},"1909":{"id":"/features/utilities/fine-tuning-122","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#deploying-the-model-for-inference","content":"To deploy the model for inference on DGX Cloud Lepton, refer to the Create LLM Endpoints with vLLM and SGLang guide. When prompted, load your model from storage and specify the full path to the checkpoint. This should end with epoch_X_step_YYYY/model/consolidated."},"1910":{"id":"/features/utilities/fine-tuning-123","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"The table below includes common scenarios and their solutions."},"1911":{"id":"/features/utilities/fine-tuning-124","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"Problem"},"1912":{"id":"/features/utilities/fine-tuning-125","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"Solution"},"1913":{"id":"/features/utilities/fine-tuning-126","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"I see AssertionError: Expected at least two columns in my logs"},"1914":{"id":"/features/utilities/fine-tuning-127","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"The dataset processing code was unable to read the specified keys from the column mapping while defining the dataset. Check to make sure that both the values next to Answer and Question in the dataset mapping are in the training dataset. For example, if you put response next to Answer in the column mapping, there must be a response column in the dataset."},"1915":{"id":"/features/utilities/fine-tuning-128","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"I see CUDA OOM errors in my logs"},"1916":{"id":"/features/utilities/fine-tuning-129","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"This happens when the dataset and or model are too large to fit in GPU memory. There are several ways to resolve this, typically in the following order.* Reduce Local Batch Size to 1 if not already.* Divide the Global Batch Size in half until the error is gone.* If your dataset has long input or output sequences, increase your Context Parallelism Size. If your model is 10B parameters or larger, increase your Tensor Parallelism Size. * Increase the number of GPUs for the job. There isn't a perfect way to find settings for any model and dataset, so some of these steps might need to be combined or repeated."},"1917":{"id":"/features/utilities/fine-tuning-130","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"My dataset combines the input and output into a single column"},"1918":{"id":"/features/utilities/fine-tuning-131","page_id":"/features/utilities/fine-tuning","tags":[],"type":"text","url":"/features/utilities/fine-tuning#troubleshooting","content":"In this case, you will need to do some pre-processing of the dataset on shared storage and split the input and output into different columns. One example is creating a Python script which reads in the dataset, iterates over each line, saves the input and output as different keys/values in a dictionary, and writes the data to a file."},"1919":{"id":"/features/utilities/storage","page_id":"/features/utilities/storage","type":"page","content":"Storage","tags":[],"url":"/features/utilities/storage"},"1920":{"id":"/features/utilities/storage-0","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage","content":"Learn how to use storage on DGX Cloud Lepton."},"1921":{"id":"/features/utilities/storage-1","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#storage-type","content":"Storage Type"},"1922":{"id":"/features/utilities/storage-2","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#add-storage-for-node-group","content":"Add Storage for Node Group"},"1923":{"id":"/features/utilities/storage-3","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#permission-management","content":"Permission management"},"1924":{"id":"/features/utilities/storage-4","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#path","content":"Path"},"1925":{"id":"/features/utilities/storage-5","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#policy","content":"Policy"},"1926":{"id":"/features/utilities/storage-6","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#members","content":"Members"},"1927":{"id":"/features/utilities/storage-7","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#permission-example","content":"Permission Example"},"1928":{"id":"/features/utilities/storage-8","page_id":"/features/utilities/storage","type":"heading","tags":[],"url":"/features/utilities/storage#use-storage-for-workloads","content":"Use Storage for Workloads"},"1929":{"id":"/features/utilities/storage-9","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage","content":"DGX Cloud Lepton provides a storage solution that allows you to store and access your data in a secure and scalable way."},"1930":{"id":"/features/utilities/storage-10","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"There are two types of storage available on DGX Cloud Lepton:"},"1931":{"id":"/features/utilities/storage-11","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"Node Local Volume: Node Local Volume persists data across workload lifecycles on the same node. It stores data directly on the node where your workload runs."},"1932":{"id":"/features/utilities/storage-12","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"Node Local Volume data is not replicated across different nodes."},"1933":{"id":"/features/utilities/storage-13","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"You are responsible for managing and cleaning up the data."},"1934":{"id":"/features/utilities/storage-14","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"Static NFS Volume: Static NFS Volume enables workloads on multiple nodes to share a common filesystem. The data resides on an NFS server that any workload can mount and use."},"1935":{"id":"/features/utilities/storage-15","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"All nodes with connectivity to the NFS server can read and write this data."},"1936":{"id":"/features/utilities/storage-16","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"Performance is governed by network bandwidth, latency, and the capacity of the NFS server."},"1937":{"id":"/features/utilities/storage-17","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#storage-type","content":"Data durability relies on the backup and reliability features provided by the underlying NFS server."},"1938":{"id":"/features/utilities/storage-18","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#add-storage-for-node-group","content":"You can add those two types of storage for your node group."},"1939":{"id":"/features/utilities/storage-19","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#add-storage-for-node-group","content":"Navigate to the storage page on the DGX Cloud Lepton dashboard. You can view the storage list grouped by node groups."},"1940":{"id":"/features/utilities/storage-20","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#add-storage-for-node-group","content":"Each node group has its own Node Local Volume list and Static NFS Volume list. Add storage for each node group separately by clicking the button in the top right corner of the section."},"1941":{"id":"/features/utilities/storage-21","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#add-storage-for-node-group","content":"For both types of storage, specify the volume name and the mount path (on the node) when adding storage."},"1942":{"id":"/features/utilities/storage-22","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#add-storage-for-node-group","content":"Before adding static NFS volume, please make sure NFS is already mounted on all nodes in the node group with the same mount path."},"1943":{"id":"/features/utilities/storage-23","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-management","content":"Permissions allow administrators to configure volume sub-path access for users. The permission system verifies that users requesting jobs with storage are authorized to mount the specified path before job execution. Once a job starts, the mount remains unaffected by permission changes."},"1944":{"id":"/features/utilities/storage-24","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-management","content":"The permission system allows access to all users when no rules are configured. However, once any rule is configured, all access must be explicitly allowed. Only allow rules exist, and permission on a parent directory grants access to all sub-paths."},"1945":{"id":"/features/utilities/storage-25","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-management","content":"Manage the permission for paths within a volume by clicking on the Permission button in the volume row."},"1946":{"id":"/features/utilities/storage-26","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#path","content":"In the Path field, specify the path prefix you want to grant access to. This Path is relative to the volume itself. The permission will only apply to the specified path prefix."},"1947":{"id":"/features/utilities/storage-27","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#path","content":"For example, if you specify /data/test as the path prefix, then the permission will only apply to the path /data/test and all subfolders under /data/test."},"1948":{"id":"/features/utilities/storage-28","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#policy","content":"There are two kinds of permission policies:"},"1949":{"id":"/features/utilities/storage-29","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#policy","content":"Permitting selected members to specific path: This will only grant access to the selected members for the path you specified in the Path field."},"1950":{"id":"/features/utilities/storage-30","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#policy","content":"Access to subfolders if email username matches: This automatically grants users access to all subfolders whose name matches their email username (the part before the @ symbol)."},"1951":{"id":"/features/utilities/storage-31","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#members","content":"If you select the Permitting selected members to specific path policy, you can select the workspace members you want to grant access to."},"1952":{"id":"/features/utilities/storage-32","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Based on the volume directory layout shown below, we will use an example to demonstrate rule precedence."},"1953":{"id":"/features/utilities/storage-33","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Rules:"},"1954":{"id":"/features/utilities/storage-34","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Permitting selected members to specific path -> Allow all-users to /Common"},"1955":{"id":"/features/utilities/storage-35","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Permitting selected members to specific path -> Allow user1 to /Data/dataset1"},"1956":{"id":"/features/utilities/storage-36","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Permitting selected members to specific path -> Allow user1 and user2 to /Data/dataset2"},"1957":{"id":"/features/utilities/storage-37","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Access to subfolders if email username matches -> Allow users /user/<username>"},"1958":{"id":"/features/utilities/storage-38","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Permitting selected members to specific path -> Allow admin1 and admin2 to /"},"1959":{"id":"/features/utilities/storage-39","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"Results in the following permissions:"},"1960":{"id":"/features/utilities/storage-40","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"The 2 Admin users (admin1 and admin2) have access to everything within the volume."},"1961":{"id":"/features/utilities/storage-41","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"User1 has access to /Common, /Data/dataset1, /Data/dataset2 and /User/user1"},"1962":{"id":"/features/utilities/storage-42","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#permission-example","content":"User2 has access to /Common, /Dataset/dataset2 and /user/user2"},"1963":{"id":"/features/utilities/storage-43","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"After adding volumes, you can use them in your workloads."},"1964":{"id":"/features/utilities/storage-44","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"For example, when you are creating a Dev Pod, you can specify the node group as the one you've added the storage to."},"1965":{"id":"/features/utilities/storage-45","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"In the Storage section, click the Mount storage button to select the volume you want to mount."},"1966":{"id":"/features/utilities/storage-46","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"Volume: Select the specific volume you want to mount by name. An indicator shows the volume type."},"1967":{"id":"/features/utilities/storage-47","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"From path: Specifies the directory path within the volume. For example, to mount the root of the volume, set the \"From path\" to /."},"1968":{"id":"/features/utilities/storage-48","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"Mount path: Specifies the path where the directory will be mounted in the container. For example, to mount the volume's root directory to /mnt in the container, set the \"Mount path\" to /mnt."},"1969":{"id":"/features/utilities/storage-49","page_id":"/features/utilities/storage","tags":[],"type":"text","url":"/features/utilities/storage#use-storage-for-workloads","content":"You can mount multiple storage volumes simultaneously. After the pod is created, you can view the mounted storage volumes in the details page."},"1970":{"id":"/features/workspace/breakglass-access-for-support","page_id":"/features/workspace/breakglass-access-for-support","type":"page","content":"Break Glass Access for Support","tags":[],"url":"/features/workspace/breakglass-access-for-support"},"1971":{"id":"/features/workspace/breakglass-access-for-support-0","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support","content":"Learn how NVIDIA support team can request temporary access to your workspace for troubleshooting, and how workspace administrators can manage these access requests."},"1972":{"id":"/features/workspace/breakglass-access-for-support-1","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"Request for Access"},"1973":{"id":"/features/workspace/breakglass-access-for-support-2","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#managing-access-requests","content":"Managing Access Requests"},"1974":{"id":"/features/workspace/breakglass-access-for-support-3","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#request-states","content":"Request States"},"1975":{"id":"/features/workspace/breakglass-access-for-support-4","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Viewing Requests"},"1976":{"id":"/features/workspace/breakglass-access-for-support-5","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Approve or Reject a Request"},"1977":{"id":"/features/workspace/breakglass-access-for-support-6","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#revoke-access","content":"Revoke Access"},"1978":{"id":"/features/workspace/breakglass-access-for-support-7","page_id":"/features/workspace/breakglass-access-for-support","type":"heading","tags":[],"url":"/features/workspace/breakglass-access-for-support#security-and-auditing","content":"Security and Auditing"},"1979":{"id":"/features/workspace/breakglass-access-for-support-8","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support","content":"This section details the process for granting DGX Cloud Lepton support temporary, essential access to customer workspaces for debugging and support."},"1980":{"id":"/features/workspace/breakglass-access-for-support-9","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"Your Technical Account Manager will notify you of pending requests. Administrators can find these requests in the Temporary Access tab within your workspace's Settings, where they will have a Pending status."},"1981":{"id":"/features/workspace/breakglass-access-for-support-10","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"When requesting access to your workspace, the support specialists will specify the permission level and duration of access."},"1982":{"id":"/features/workspace/breakglass-access-for-support-11","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"Permission Levels:"},"1983":{"id":"/features/workspace/breakglass-access-for-support-12","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"Viewer: Allows the specialist to view information within the workspace."},"1984":{"id":"/features/workspace/breakglass-access-for-support-13","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-for-access","content":"User: Grants the specialist permissions equivalent to a standard user in your organization."},"1985":{"id":"/features/workspace/breakglass-access-for-support-14","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#managing-access-requests","content":"Administrators can approve the request, reject it, or revoke access to a workspace."},"1986":{"id":"/features/workspace/breakglass-access-for-support-15","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Each access request can be in one of the following states:"},"1987":{"id":"/features/workspace/breakglass-access-for-support-16","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Pending: The request has been submitted and is awaiting admin approval"},"1988":{"id":"/features/workspace/breakglass-access-for-support-17","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Approved: The request has been granted and access is currently active"},"1989":{"id":"/features/workspace/breakglass-access-for-support-18","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Expired: The granted access has passed its expiration time"},"1990":{"id":"/features/workspace/breakglass-access-for-support-19","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Revoked: The admin manually revoked the access before expiration"},"1991":{"id":"/features/workspace/breakglass-access-for-support-20","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#request-states","content":"Rejected: The admin rejected the request without granting access"},"1992":{"id":"/features/workspace/breakglass-access-for-support-21","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"The access request list displays the following information for each request:"},"1993":{"id":"/features/workspace/breakglass-access-for-support-22","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"User: Email address of the support team member"},"1994":{"id":"/features/workspace/breakglass-access-for-support-23","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"State: Current status of the request"},"1995":{"id":"/features/workspace/breakglass-access-for-support-24","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Role: The access level requested (Viewer or User)"},"1996":{"id":"/features/workspace/breakglass-access-for-support-25","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Duration: How long the access will be active once granted"},"1997":{"id":"/features/workspace/breakglass-access-for-support-26","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Request Time: When the request was submitted"},"1998":{"id":"/features/workspace/breakglass-access-for-support-27","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Accept Time: When the request was approved"},"1999":{"id":"/features/workspace/breakglass-access-for-support-28","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Expiration Time: When the access will automatically expire"},"2000":{"id":"/features/workspace/breakglass-access-for-support-29","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Revoke Time: When the access was revoked"},"2001":{"id":"/features/workspace/breakglass-access-for-support-30","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#viewing-requests","content":"Reason: The explanation provided by the support team member"},"2002":{"id":"/features/workspace/breakglass-access-for-support-31","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"To approve or reject a request:"},"2003":{"id":"/features/workspace/breakglass-access-for-support-32","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Navigate to the Temporary Access tab within Settings."},"2004":{"id":"/features/workspace/breakglass-access-for-support-33","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Before proceeding, verify that the requested role and duration are appropriate."},"2005":{"id":"/features/workspace/breakglass-access-for-support-34","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"To approve a request, click Grant. The status will update to Approved."},"2006":{"id":"/features/workspace/breakglass-access-for-support-35","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"To reject the request, click Reject."},"2007":{"id":"/features/workspace/breakglass-access-for-support-36","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Key Details Regarding Approved Access:"},"2008":{"id":"/features/workspace/breakglass-access-for-support-37","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Automatic Time Limit: Access is automatically limited to the duration specified in the request."},"2009":{"id":"/features/workspace/breakglass-access-for-support-38","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Permissions: Approved access confers either viewer or user permissions as requested."},"2010":{"id":"/features/workspace/breakglass-access-for-support-39","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"Workspace Restriction: Access is restricted solely to the specified Workspace. Other Workspaces remain inaccessible."},"2011":{"id":"/features/workspace/breakglass-access-for-support-40","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#approve-or-reject-a-request","content":"No Addition to Lepton Workspace or NCA: The support specialist will only be able to access the Workspace; they will not be added to your Lepton Workspace or NCA."},"2012":{"id":"/features/workspace/breakglass-access-for-support-41","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#revoke-access","content":"To revoke access before it expires:"},"2013":{"id":"/features/workspace/breakglass-access-for-support-42","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#revoke-access","content":"Navigate to the Temporary Access tab within Settings."},"2014":{"id":"/features/workspace/breakglass-access-for-support-43","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#revoke-access","content":"Identify the request you want to revoke access for and click Revoke."},"2015":{"id":"/features/workspace/breakglass-access-for-support-44","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#revoke-access","content":"The status will update to Revoked, and the requestor will no longer be able to access the workspace."},"2016":{"id":"/features/workspace/breakglass-access-for-support-45","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#security-and-auditing","content":"The system thoroughly audits all temporary access, including the addition of support users to a workspace."},"2017":{"id":"/features/workspace/breakglass-access-for-support-46","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#security-and-auditing","content":"View Access Granting Logs: Access logs for granting temporary access under Utilities → Observability → Audit logs."},"2018":{"id":"/features/workspace/breakglass-access-for-support-47","page_id":"/features/workspace/breakglass-access-for-support","tags":[],"type":"text","url":"/features/workspace/breakglass-access-for-support#security-and-auditing","content":"Monitor Support Personnel Activity: All actions performed by support personnel within a workspace are also audited."},"2019":{"id":"/features/workspace/members","page_id":"/features/workspace/members","type":"page","content":"Members","tags":[],"url":"/features/workspace/members"},"2020":{"id":"/features/workspace/members-0","page_id":"/features/workspace/members","tags":[],"type":"text","url":"/features/workspace/members","content":"Manage workspace collaboration in DGX Cloud Lepton by inviting team members, assigning roles, and controlling access permissions for your projects."},"2021":{"id":"/features/workspace/members-1","page_id":"/features/workspace/members","type":"heading","tags":[],"url":"/features/workspace/members#managing-members","content":"Managing Members"},"2022":{"id":"/features/workspace/members-2","page_id":"/features/workspace/members","tags":[],"type":"text","url":"/features/workspace/members","content":"As a workspace administrator, you can manage your workspace members by inviting, removing, and assigning roles to them.\nThis is an advanced feature that allows you to collaborate with your team members and control their access to the workspace."},"2023":{"id":"/features/workspace/members-3","page_id":"/features/workspace/members","tags":[],"type":"text","url":"/features/workspace/members","content":"If your Enterprise SSO has been integrated, DGX Cloud Lepton members still need to be individually added to DGX Cloud Lepton. See the Workspace section for more information on SSO integrations."},"2024":{"id":"/features/workspace/members-4","page_id":"/features/workspace/members","tags":[],"type":"text","url":"/features/workspace/members#managing-members","content":"Navigate to the Members tab on the left-hand side of the workspace settings page.\nYou will see a list of all members in the current workspace. If you have an admin role, you can invite, remove, and assign roles to members from this page.\nOnce a member is invited, they will receive an email notification with an invitation link to join the workspace."},"2025":{"id":"/features/workspace/members-5","page_id":"/features/workspace/members","tags":[],"type":"text","url":"/features/workspace/members#managing-members","content":"Your workspace members should be in the same NCA account. You can verify this from the NCA dashboard."},"2026":{"id":"/features/workspace/registry","page_id":"/features/workspace/registry","type":"page","content":"Registry","tags":[],"url":"/features/workspace/registry"},"2027":{"id":"/features/workspace/registry-0","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry","content":"Learn how to connect private image registries to your DGX Cloud Lepton workspace, enabling secure deployment of custom container images for your applications."},"2028":{"id":"/features/workspace/registry-1","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-new-registry-auth","content":"Create New Registry Auth"},"2029":{"id":"/features/workspace/registry-2","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#use-a-private-registry","content":"Use a Private Registry"},"2030":{"id":"/features/workspace/registry-3","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#docker-hub","content":"Docker Hub"},"2031":{"id":"/features/workspace/registry-4","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-access-token","content":"Create Access Token"},"2032":{"id":"/features/workspace/registry-5","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-registry-auth","content":"Create Registry Auth"},"2033":{"id":"/features/workspace/registry-6","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#gcp","content":"GCP"},"2034":{"id":"/features/workspace/registry-7","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#get-service-account-key-json-file","content":"Get Service Account Key JSON File"},"2035":{"id":"/features/workspace/registry-8","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-service-account-for-registry-auth","content":"Create Service Account for Registry Auth"},"2036":{"id":"/features/workspace/registry-9","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-service-account-key","content":"Create Service Account Key"},"2037":{"id":"/features/workspace/registry-10","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Create New Registry Auth on DGX Cloud Lepton"},"2038":{"id":"/features/workspace/registry-11","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#azure","content":"Azure"},"2039":{"id":"/features/workspace/registry-12","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#obtain-registry-credentials","content":"Obtain Registry Credentials"},"2040":{"id":"/features/workspace/registry-13","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-azure-registry-auth","content":"Create Azure Registry Auth"},"2041":{"id":"/features/workspace/registry-14","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#github","content":"GitHub"},"2042":{"id":"/features/workspace/registry-15","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-access-token-1","content":"Create Access Token"},"2043":{"id":"/features/workspace/registry-16","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-new-registry-auth-1","content":"Create New Registry Auth"},"2044":{"id":"/features/workspace/registry-17","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#nvidia","content":"NVIDIA"},"2045":{"id":"/features/workspace/registry-18","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-api-key","content":"Create API Key"},"2046":{"id":"/features/workspace/registry-19","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-registry-auth-1","content":"Create Registry Auth"},"2047":{"id":"/features/workspace/registry-20","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#custom","content":"Custom"},"2048":{"id":"/features/workspace/registry-21","page_id":"/features/workspace/registry","type":"heading","tags":[],"url":"/features/workspace/registry#create-custom-registry-auth","content":"Create Custom Registry Auth"},"2049":{"id":"/features/workspace/registry-22","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry","content":"Besides public registries like Docker Hub, you can also bind your private registries to your workspace. This allows you to store and deploy your custom images in a private space."},"2050":{"id":"/features/workspace/registry-23","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"Navigate to the registries tab in the settings page of your workspace, then click the New registry auth button in the top right corner. You can choose a registry source from the list."},"2051":{"id":"/features/workspace/registry-24","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"Currently, we support the following private registry presets:"},"2052":{"id":"/features/workspace/registry-25","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"Docker Hub"},"2053":{"id":"/features/workspace/registry-26","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"Google Container Registry (GCR)"},"2054":{"id":"/features/workspace/registry-27","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"Azure Container Registry (ACR)"},"2055":{"id":"/features/workspace/registry-28","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"GitHub Container Registry (GHCR)"},"2056":{"id":"/features/workspace/registry-29","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"NVIDIA Container Registry (NCR)"},"2057":{"id":"/features/workspace/registry-30","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth","content":"You can also create a custom registry auth by providing the registry URL, username, and password."},"2058":{"id":"/features/workspace/registry-31","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#use-a-private-registry","content":"After you create a registry auth successfully, you can use it to access your private images for workload creation."},"2059":{"id":"/features/workspace/registry-32","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#use-a-private-registry","content":"For example, to use a Dev Pod with a Docker Hub registry, navigate to the pod creation page, and in the Container configuration section, select custom image.\nInput the image address in the Image field, and select the registry auth you created in the Private image registry auth field."},"2060":{"id":"/features/workspace/registry-33","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#use-a-private-registry","content":"With these configurations, you can launch your Dev Pod with a private image."},"2061":{"id":"/features/workspace/registry-34","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#docker-hub","content":"This section guides you through setting up a Docker Hub registry for your workspace."},"2062":{"id":"/features/workspace/registry-35","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"Navigate to the personal access tokens page and click Generate New Token.\nIn the creation form, you need to fill in the following fields:"},"2063":{"id":"/features/workspace/registry-36","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"Token description: A short description for the new access token."},"2064":{"id":"/features/workspace/registry-37","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"Expiration date: Default is None, which means the token will never expire."},"2065":{"id":"/features/workspace/registry-38","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"Access permissions: Select Read-only."},"2066":{"id":"/features/workspace/registry-39","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"Click Generate Token to create the new access token successfully.\nCopy and save the token securely. You will not be able to access it after closing the page."},"2067":{"id":"/features/workspace/registry-40","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token","content":"You can learn more in the official documentation."},"2068":{"id":"/features/workspace/registry-41","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth","content":"Navigate to the registry settings page, click on New registry auth, and select Docker Hub as the source."},"2069":{"id":"/features/workspace/registry-42","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth","content":"You need to fill in the following fields:"},"2070":{"id":"/features/workspace/registry-43","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth","content":"Username: Your Docker Hub username."},"2071":{"id":"/features/workspace/registry-44","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth","content":"Access Token: The personal access token you just created."},"2072":{"id":"/features/workspace/registry-45","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth","content":"After filling in the form, click Submit to create the new registry auth successfully."},"2073":{"id":"/features/workspace/registry-46","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#gcp","content":"This section guides you through setting up a GCP Registry for your workspace."},"2074":{"id":"/features/workspace/registry-47","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#get-service-account-key-json-file","content":"Before creating the registry auth on DGX Cloud Lepton, you need to obtain the service account key JSON file."},"2075":{"id":"/features/workspace/registry-48","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-service-account-for-registry-auth","content":"Navigate to the service accounts page in the GCP console, select the target project, then click the Create service account button."},"2076":{"id":"/features/workspace/registry-49","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-service-account-for-registry-auth","content":"In the second step, you need to grant registry auth role to the service account. In this case, you need to grant Artifact Registry Reader role so that the service account can access the Artifact Registry and pull images."},"2077":{"id":"/features/workspace/registry-50","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-service-account-key","content":"After creating the service account, you need to create a key for it.\nNavigate to the service account page, switch to the KEYS tab, click the ADD KEY button, and select JSON as the key type."},"2078":{"id":"/features/workspace/registry-51","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-service-account-key","content":"Click CREATE to automatically download the JSON file."},"2079":{"id":"/features/workspace/registry-52","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Now that you have the service account key JSON file, you can create a new registry auth for GCP on DGX Cloud Lepton."},"2080":{"id":"/features/workspace/registry-53","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Navigate to the registry settings page, click on New registry auth, and select GCP as the source."},"2081":{"id":"/features/workspace/registry-54","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"In the creation form, you need to fill in the following fields:"},"2082":{"id":"/features/workspace/registry-55","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Name: The name for the new registry, to help you manage registries on DGX Cloud Lepton."},"2083":{"id":"/features/workspace/registry-56","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Registry: The URL of the GCP Artifact Registry, for example, https://us-west1-docker.pkg.dev."},"2084":{"id":"/features/workspace/registry-57","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"Json Key: The JSON key file for the service account. You can upload the file by clicking the Upload Service Account Key button."},"2085":{"id":"/features/workspace/registry-58","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-on-dgx-cloud-lepton","content":"After filling in the form, click Submit to create the new registry auth successfully."},"2086":{"id":"/features/workspace/registry-59","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#azure","content":"This section guides you through setting up an Azure Registry for your workspace."},"2087":{"id":"/features/workspace/registry-60","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#obtain-registry-credentials","content":"Navigate to your Azure portal and locate your Azure Container Registry page.\nIf there are no registries in your Azure account yet, you need to create one first."},"2088":{"id":"/features/workspace/registry-61","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#obtain-registry-credentials","content":"Open the Access keys tab under Settings and toggle the Admin user switch to on.\nCopy the password and save it securely."},"2089":{"id":"/features/workspace/registry-62","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#obtain-registry-credentials","content":"You can learn more about Azure access tokens in the official documentation."},"2090":{"id":"/features/workspace/registry-63","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"Navigate to the registry settings page, click on New registry auth, and select Azure as the source."},"2091":{"id":"/features/workspace/registry-64","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"You need to fill in the following fields:"},"2092":{"id":"/features/workspace/registry-65","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"Registry: The login server URL of your Azure Container Registry, e.g., myregistry.azurecr.io."},"2093":{"id":"/features/workspace/registry-66","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"Username: Your Registry username shown in the Azure portal."},"2094":{"id":"/features/workspace/registry-67","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"Password: Your Registry password just copied."},"2095":{"id":"/features/workspace/registry-68","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-azure-registry-auth","content":"After filling in the form, click on Submit, and you will see the new Azure registry auth created successfully."},"2096":{"id":"/features/workspace/registry-69","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#github","content":"This section guides you through setting up a GitHub registry for your workspace using an access token."},"2097":{"id":"/features/workspace/registry-70","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"First, you need to create an access token with the appropriate scopes.\nNavigate to your GitHub tokens page, click on the Generate new token button."},"2098":{"id":"/features/workspace/registry-71","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Complete the following fields:"},"2099":{"id":"/features/workspace/registry-72","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Note: A note for you to identify the token usage."},"2100":{"id":"/features/workspace/registry-73","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Expiration: Recommended to set as No expiration."},"2101":{"id":"/features/workspace/registry-74","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Scopes: In this case, we only need the read:packages scope."},"2102":{"id":"/features/workspace/registry-75","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Click on the Generate token button to create the access token."},"2103":{"id":"/features/workspace/registry-76","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"Copy and save the token securely. You will no longer be able to access it after closing this page."},"2104":{"id":"/features/workspace/registry-77","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-access-token-1","content":"You can read more in the official documentation."},"2105":{"id":"/features/workspace/registry-78","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"Now that you have the personal access token from GitHub, you can create a new registry auth."},"2106":{"id":"/features/workspace/registry-79","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"Navigate to the registry settings page, click on New registry auth, and select GitHub as the source."},"2107":{"id":"/features/workspace/registry-80","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"You need to fill in the following fields:"},"2108":{"id":"/features/workspace/registry-81","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"GitHub Username: Your GitHub username."},"2109":{"id":"/features/workspace/registry-82","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"Access Token: The personal access token you just created."},"2110":{"id":"/features/workspace/registry-83","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-new-registry-auth-1","content":"After filling in the form, click Submit to create the new registry auth successfully."},"2111":{"id":"/features/workspace/registry-84","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#nvidia","content":"This section guides you through setting up an NVIDIA registry for your workspace."},"2112":{"id":"/features/workspace/registry-85","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"Before creating the registry auth on DGX Cloud Lepton, you need to create an API key for your NVIDIA account.\nNavigate to the API Keys page, click on Generate Personal Key."},"2113":{"id":"/features/workspace/registry-86","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"In the creation form, you need to fill in the following fields:"},"2114":{"id":"/features/workspace/registry-87","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"Key Name: The name for the new API key."},"2115":{"id":"/features/workspace/registry-88","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"Expiration: The expiration date for the new API key."},"2116":{"id":"/features/workspace/registry-89","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"Services Included: The services included in the new API key, select Private Registry here."},"2117":{"id":"/features/workspace/registry-90","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"You can only include Private Registry when you are using Enterprise account on NVIDIA NGC."},"2118":{"id":"/features/workspace/registry-91","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"Click Generate Personal Key to create the API key successfully. Copy and save the API key securely. You will not be able to access it after closing the page."},"2119":{"id":"/features/workspace/registry-92","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-api-key","content":"You can learn more details in the official documentation."},"2120":{"id":"/features/workspace/registry-93","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth-1","content":"Navigate to the registry settings page, click on New registry auth, and select NVIDIA as the source.\nYou need to fill in the following fields:"},"2121":{"id":"/features/workspace/registry-94","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth-1","content":"API Key: The API key you just copied."},"2122":{"id":"/features/workspace/registry-95","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-registry-auth-1","content":"After filling in the form, click Submit to create the new registry auth successfully."},"2123":{"id":"/features/workspace/registry-96","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#custom","content":"Besides the presets, you can also configure a custom registry for your workspace."},"2124":{"id":"/features/workspace/registry-97","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"Navigate to the registry settings page, click on New registry auth, and select Custom as the source."},"2125":{"id":"/features/workspace/registry-98","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"You need to fill in the following fields:"},"2126":{"id":"/features/workspace/registry-99","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"Name: A name for your registry."},"2127":{"id":"/features/workspace/registry-100","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"Registry: The URL of your registry."},"2128":{"id":"/features/workspace/registry-101","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"Username: Your Registry username."},"2129":{"id":"/features/workspace/registry-102","page_id":"/features/workspace/registry","tags":[],"type":"text","url":"/features/workspace/registry#create-custom-registry-auth","content":"Password: Your Registry password."},"2130":{"id":"/features/workspace/roles-and-permissions","page_id":"/features/workspace/roles-and-permissions","type":"page","content":"Roles and Permissions","tags":[],"url":"/features/workspace/roles-and-permissions"},"2131":{"id":"/features/workspace/roles-and-permissions-0","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions","content":"Learn how to create and manage roles and permissions for workspace members in DGX Cloud Lepton, including built-in roles and custom role creation."},"2132":{"id":"/features/workspace/roles-and-permissions-1","page_id":"/features/workspace/roles-and-permissions","type":"heading","tags":[],"url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Creating Custom Roles"},"2133":{"id":"/features/workspace/roles-and-permissions-2","page_id":"/features/workspace/roles-and-permissions","type":"heading","tags":[],"url":"/features/workspace/roles-and-permissions#available-resources","content":"Available Resources"},"2134":{"id":"/features/workspace/roles-and-permissions-3","page_id":"/features/workspace/roles-and-permissions","type":"heading","tags":[],"url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"Assigning Roles to Members"},"2135":{"id":"/features/workspace/roles-and-permissions-4","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions","content":"A role defines a set of permissions (Read or Read/Write) assigned to a DGX Cloud Lepton resource (such as Batch Jobs, Node Groups, and so on)."},"2136":{"id":"/features/workspace/roles-and-permissions-5","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions","content":"Navigate to the Roles tab on the left side of the workspace settings page."},"2137":{"id":"/features/workspace/roles-and-permissions-6","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions","content":"You will see a list of built-in roles at the top: admin, user, and viewer. By default, the user role has read access to all resources, and the admin role has both read and write access to all resources. Built-in roles cannot be edited or deleted."},"2138":{"id":"/features/workspace/roles-and-permissions-7","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"If you have the admin role, you can create a new role."},"2139":{"id":"/features/workspace/roles-and-permissions-8","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Click the Add Role button."},"2140":{"id":"/features/workspace/roles-and-permissions-9","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Enter a name for the role."},"2141":{"id":"/features/workspace/roles-and-permissions-10","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Click Add Permissions and select the resource you want to provide permissions for."},"2142":{"id":"/features/workspace/roles-and-permissions-11","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Select Read Only or Read/Write."},"2143":{"id":"/features/workspace/roles-and-permissions-12","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"You can add permissions for more than one resource to a role."},"2144":{"id":"/features/workspace/roles-and-permissions-13","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#creating-custom-roles","content":"Click Create Role."},"2145":{"id":"/features/workspace/roles-and-permissions-14","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"The following is the list of resources that you can assign permissions for:"},"2146":{"id":"/features/workspace/roles-and-permissions-15","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Alerting (Configure alerts and notifications)"},"2147":{"id":"/features/workspace/roles-and-permissions-16","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Audit Logs (View and export audit logs)"},"2148":{"id":"/features/workspace/roles-and-permissions-17","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Domains (Configure ingress rules and networking)"},"2149":{"id":"/features/workspace/roles-and-permissions-18","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Endpoints & Dev Pods (Access and manage endpoints and devpods)"},"2150":{"id":"/features/workspace/roles-and-permissions-19","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Export Observability (Export observability data and metrics)"},"2151":{"id":"/features/workspace/roles-and-permissions-20","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Jobs (Access and manage all batch jobs)"},"2152":{"id":"/features/workspace/roles-and-permissions-21","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Logs (View and access system and application logs)"},"2153":{"id":"/features/workspace/roles-and-permissions-22","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Members (Manage and invite workspace members)"},"2154":{"id":"/features/workspace/roles-and-permissions-23","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Node Groups (Configure and manage node groups)"},"2155":{"id":"/features/workspace/roles-and-permissions-24","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Node Reservations (Reserve and manage compute nodes)"},"2156":{"id":"/features/workspace/roles-and-permissions-25","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Private Registry Auth (Configure private container registry authentication)"},"2157":{"id":"/features/workspace/roles-and-permissions-26","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Ray Clusters (Access for Ray clusters management)"},"2158":{"id":"/features/workspace/roles-and-permissions-27","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Secrets (Store and manage sensitive configuration)"},"2159":{"id":"/features/workspace/roles-and-permissions-28","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Service Requests (Request services from DGX Cloud Lepton team)"},"2160":{"id":"/features/workspace/roles-and-permissions-29","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Slurm Clusters (Access for Slurm clusters management)"},"2161":{"id":"/features/workspace/roles-and-permissions-30","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Storage Permission (Manage storage mounts and permissions)"},"2162":{"id":"/features/workspace/roles-and-permissions-31","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Templates (Create and manage deployment templates)"},"2163":{"id":"/features/workspace/roles-and-permissions-32","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Tokens (Access for API Tokens)"},"2164":{"id":"/features/workspace/roles-and-permissions-33","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Usages (View resource usage and billing information)"},"2165":{"id":"/features/workspace/roles-and-permissions-34","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#available-resources","content":"Workspace Info (View and edit workspace information)"},"2166":{"id":"/features/workspace/roles-and-permissions-35","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"Once a role is created, it can be assigned to users."},"2167":{"id":"/features/workspace/roles-and-permissions-36","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"Navigate to the Members tab on the left side of the workspace settings page."},"2168":{"id":"/features/workspace/roles-and-permissions-37","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"You will see a list of all the members in your current workspace."},"2169":{"id":"/features/workspace/roles-and-permissions-38","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"If you have the admin role, click the dropdown menu in the Role column for a member and select a role."},"2170":{"id":"/features/workspace/roles-and-permissions-39","page_id":"/features/workspace/roles-and-permissions","tags":[],"type":"text","url":"/features/workspace/roles-and-permissions#assigning-roles-to-members","content":"This new role will override the previous role assigned to the member."},"2171":{"id":"/features/workspace/secret","page_id":"/features/workspace/secret","type":"page","content":"Secrets","tags":[],"url":"/features/workspace/secret"},"2172":{"id":"/features/workspace/secret-0","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret","content":"Securely store and manage sensitive information like API keys and credentials in DGX Cloud Lepton, and use them in your deployments and jobs."},"2173":{"id":"/features/workspace/secret-1","page_id":"/features/workspace/secret","type":"heading","tags":[],"url":"/features/workspace/secret#managing-secrets","content":"Managing Secrets"},"2174":{"id":"/features/workspace/secret-2","page_id":"/features/workspace/secret","type":"heading","tags":[],"url":"/features/workspace/secret#visibility","content":"Visibility"},"2175":{"id":"/features/workspace/secret-3","page_id":"/features/workspace/secret","type":"heading","tags":[],"url":"/features/workspace/secret#using-secrets","content":"Using Secrets"},"2176":{"id":"/features/workspace/secret-4","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret","content":"Secrets are a secure and reusable way to add credentials and other sensitive information to your workspace.\nYou can use secrets to store sensitive data, such as passwords, API keys, and tokens, and then reference them in your deployments, jobs, and pods."},"2177":{"id":"/features/workspace/secret-5","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret","content":"Secrets are similar to environment variables, but the actual value is no longer editable or revealable once created."},"2178":{"id":"/features/workspace/secret-6","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#managing-secrets","content":"Navigate to the Secrets tab on the left-hand side of the workspace settings page.\nYou will see a list of all the secrets you have created. You can add, edit, or delete secrets from this page."},"2179":{"id":"/features/workspace/secret-7","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#managing-secrets","content":"We provide quick ways to add SSH public keys, GitHub, Hugging Face, OpenAI, Datadog tokens, NGC API keys, and custom secrets as secrets to your workspace.\nAll secrets are stored and associated with the workspace in which they are created."},"2180":{"id":"/features/workspace/secret-8","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#visibility","content":"For every secret, you can set the visibility to Public or Private."},"2181":{"id":"/features/workspace/secret-9","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#visibility","content":"Public: The secret is visible to all users in the workspace."},"2182":{"id":"/features/workspace/secret-10","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#visibility","content":"Private: The secret is only visible to you, the user who created it."},"2183":{"id":"/features/workspace/secret-11","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#visibility","content":"By default, all secrets are created as private."},"2184":{"id":"/features/workspace/secret-12","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#using-secrets","content":"On the dashboard, you can add secrets as environment variables to your deployments, jobs, and pods.\nConfigure them under advanced settings when creating or editing a deployment, job, or pod."},"2185":{"id":"/features/workspace/secret-13","page_id":"/features/workspace/secret","tags":[],"type":"text","url":"/features/workspace/secret#using-secrets","content":"To access the secret value in the deployment, you can use the os module:"},"2186":{"id":"/features/workspace/templates","page_id":"/features/workspace/templates","type":"page","content":"Templates","tags":[],"url":"/features/workspace/templates"},"2187":{"id":"/features/workspace/templates-0","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates","content":"Create and use templates for dev pods and batch jobs in DGX Cloud Lepton, with customizable placeholders for flexible configuration."},"2188":{"id":"/features/workspace/templates-1","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#create-a-template","content":"Create a Template"},"2189":{"id":"/features/workspace/templates-2","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#configuration","content":"Configuration"},"2190":{"id":"/features/workspace/templates-3","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#dev-pods","content":"Dev Pods"},"2191":{"id":"/features/workspace/templates-4","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#batch-jobs","content":"Batch Jobs"},"2192":{"id":"/features/workspace/templates-5","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#placeholders","content":"Placeholders"},"2193":{"id":"/features/workspace/templates-6","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#example-for-placeholders","content":"Example for placeholders"},"2194":{"id":"/features/workspace/templates-7","page_id":"/features/workspace/templates","type":"heading","tags":[],"url":"/features/workspace/templates#use-a-template","content":"Use a Template"},"2195":{"id":"/features/workspace/templates-8","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates","content":"Templates are used to create workloads in your workspace. You can create templates for dev pods and batch jobs to improve productivity by reusing the same configuration."},"2196":{"id":"/features/workspace/templates-9","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#create-a-template","content":"The process of creating a template is very similar to creating workloads as described in dev pods and batch jobs.\nYou can specify the configuration of the template, and use placeholders to make the template more flexible."},"2197":{"id":"/features/workspace/templates-10","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#create-a-template","content":"Navigate to the Create Templates page on your workspace, where you can see a detailed form to create a template."},"2198":{"id":"/features/workspace/templates-11","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#create-a-template","content":"For a template, you need to specify a workload type, which can be one of the following:"},"2199":{"id":"/features/workspace/templates-12","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#create-a-template","content":"Dev Pods: Interactive coding with SSH, Jupyter, or your favorite tool."},"2200":{"id":"/features/workspace/templates-13","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#create-a-template","content":"Batch Jobs: Run training, data processing, and other batch jobs."},"2201":{"id":"/features/workspace/templates-14","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#configuration","content":"Now you can follow the page instructions to fill in the configuration for your template based on the selected workload type."},"2202":{"id":"/features/workspace/templates-15","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#dev-pods","content":"The configuration for dev pods is the same as creating a dev pod. You can check the guide for more details."},"2203":{"id":"/features/workspace/templates-16","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#batch-jobs","content":"The configuration for batch jobs is the same as creating a batch job. You can check the guide for more details."},"2204":{"id":"/features/workspace/templates-17","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#placeholders","content":"You can use placeholders in the configuration to make your template more flexible. Use placeholders in the following fields:"},"2205":{"id":"/features/workspace/templates-18","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#placeholders","content":"{{user.email}}: Email address of the workload creator."},"2206":{"id":"/features/workspace/templates-19","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#placeholders","content":"{{user.email_username}}: Username part of the workload creator's email (before @)."},"2207":{"id":"/features/workspace/templates-20","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#placeholders","content":"{% raw %} raw-content {% endraw %}: Content that skips template parsing."},"2208":{"id":"/features/workspace/templates-21","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#example-for-placeholders","content":"Take Dev Pod template as an example, you can use the placeholder {{user.email_username}} in the Job Name or Pod Name field to make the workload name unique for each creator.\nSet Pod Name to {{user.email_username}}-dev-pod, and the workload name will be hey-dev-pod for user with hey@nvidia.com email address."},"2209":{"id":"/features/workspace/templates-22","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#use-a-template","content":"To use a template for dev pods that you have created, follow these steps:"},"2210":{"id":"/features/workspace/templates-23","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#use-a-template","content":"Head to the Create From Container Image page for dev pods."},"2211":{"id":"/features/workspace/templates-24","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#use-a-template","content":"Click the Templates dropdown in the top right corner and select the target template."},"2212":{"id":"/features/workspace/templates-25","page_id":"/features/workspace/templates","tags":[],"type":"text","url":"/features/workspace/templates#use-a-template","content":"The form will be filled with the template configuration automatically, and you can modify fields as needed."},"2213":{"id":"/features/workspace/token","page_id":"/features/workspace/token","type":"page","content":"Token","tags":[],"url":"/features/workspace/token"},"2214":{"id":"/features/workspace/token-0","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token","content":"Learn about Lepton's authentication tokens, including User and Workspace tokens, and how to use them securely with the API, CLI, and SDKs."},"2215":{"id":"/features/workspace/token-1","page_id":"/features/workspace/token","type":"heading","tags":[],"url":"/features/workspace/token#creating-tokens","content":"Creating Tokens"},"2216":{"id":"/features/workspace/token-2","page_id":"/features/workspace/token","type":"heading","tags":[],"url":"/features/workspace/token#viewing-tokens","content":"Viewing Tokens"},"2217":{"id":"/features/workspace/token-3","page_id":"/features/workspace/token","type":"heading","tags":[],"url":"/features/workspace/token#using-tokens","content":"Using Tokens"},"2218":{"id":"/features/workspace/token-4","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token","content":"Tokens could be used to authenticate and authorize requests to DGX Cloud Lepton. They are essential for logging in via the CLI, API, or SDKs."},"2219":{"id":"/features/workspace/token-5","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token","content":"Keep your tokens secure and do not share them with others."},"2220":{"id":"/features/workspace/token-6","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#creating-tokens","content":"You can create a new token on the Settings - Tokens tab on the left-hand side of the workspace settings page.\nWhen creating a new token, following fields can be configured based on your needs:"},"2221":{"id":"/features/workspace/token-7","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#creating-tokens","content":"Token Name: The name of the token."},"2222":{"id":"/features/workspace/token-8","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#creating-tokens","content":"Expiration: The expiration time of the token. Default to 1 day."},"2223":{"id":"/features/workspace/token-9","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#viewing-tokens","content":"You can view your tokens on the Token tab on the left-hand side of the workspace settings page."},"2224":{"id":"/features/workspace/token-10","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#viewing-tokens","content":"You can only see the tokens created by you in the list."},"2225":{"id":"/features/workspace/token-11","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#using-tokens","content":"Token can be used to authenticate requests to the Lepton API. Simply include the token in the Authorization header of your request:"},"2226":{"id":"/features/workspace/token-12","page_id":"/features/workspace/token","tags":[],"type":"text","url":"/features/workspace/token#using-tokens","content":"Workspace ID could be found on the Settings - General."},"2227":{"id":"/reference/cli/get-started","page_id":"/reference/cli/get-started","type":"page","content":"Get Started","tags":[],"url":"/reference/cli/get-started"},"2228":{"id":"/reference/cli/get-started-0","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started","content":"Lepton Command-Line Interface (CLI) reference"},"2229":{"id":"/reference/cli/get-started-1","page_id":"/reference/cli/get-started","type":"heading","tags":[],"url":"/reference/cli/get-started#usage","content":"Usage"},"2230":{"id":"/reference/cli/get-started-2","page_id":"/reference/cli/get-started","type":"heading","tags":[],"url":"/reference/cli/get-started#options","content":"Options"},"2231":{"id":"/reference/cli/get-started-3","page_id":"/reference/cli/get-started","type":"heading","tags":[],"url":"/reference/cli/get-started#commands","content":"Commands"},"2232":{"id":"/reference/cli/get-started-4","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started","content":"Lep is the main entry point for the DGX Cloud Lepton command-line interface.\nIt provides a set of commands to create and develop photons locally and\ndeploy them to DGX Cloud Lepton. It also provides a set of commands to\nmanage resources in the cloud, such as workspaces, deployments, secrets, and\nstorage. To install it, run:"},"2233":{"id":"/reference/cli/get-started-5","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started","content":"pip install -U leptonai"},"2234":{"id":"/reference/cli/get-started-6","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#usage","content":"lep [OPTIONS] COMMAND [ARGS]..."},"2235":{"id":"/reference/cli/get-started-7","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#options","content":"-v,  --version : Show the version and exit."},"2236":{"id":"/reference/cli/get-started-8","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#options","content":"-h,  --help : Show this message and exit."},"2237":{"id":"/reference/cli/get-started-9","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"endpoint : Manage endpoints (formerly called deployments) on the DGX..."},"2238":{"id":"/reference/cli/get-started-10","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"job : Manages Lepton Jobs."},"2239":{"id":"/reference/cli/get-started-11","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"log : Manage and retrieve the logs history of specific jobs,..."},"2240":{"id":"/reference/cli/get-started-12","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"login : Login to the DGX Cloud Lepton."},"2241":{"id":"/reference/cli/get-started-13","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"logout : Logout of the DGX Cloud Lepton."},"2242":{"id":"/reference/cli/get-started-14","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"node : Manage nodes on the DGX Cloud Lepton."},"2243":{"id":"/reference/cli/get-started-15","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"pod : Manages pods on the DGX Cloud Lepton."},"2244":{"id":"/reference/cli/get-started-16","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"secret : Manage secrets on the DGX Cloud Lepton."},"2245":{"id":"/reference/cli/get-started-17","page_id":"/reference/cli/get-started","tags":[],"type":"text","url":"/reference/cli/get-started#commands","content":"workspace : Manage workspace access to the DGX Cloud Lepton."},"2246":{"id":"/reference/cli/lep_endpoint","page_id":"/reference/cli/lep_endpoint","type":"page","content":"lep endpoint","tags":[],"url":"/reference/cli/lep_endpoint"},"2247":{"id":"/reference/cli/lep_endpoint-0","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint","content":"Manage endpoints (formerly called deployments) on the DGX Cloud Lepton."},"2248":{"id":"/reference/cli/lep_endpoint-1","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage","content":"Usage"},"2249":{"id":"/reference/cli/lep_endpoint-2","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options","content":"Options"},"2250":{"id":"/reference/cli/lep_endpoint-3","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#commands","content":"Commands"},"2251":{"id":"/reference/cli/lep_endpoint-4","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-create","content":"lep endpoint create"},"2252":{"id":"/reference/cli/lep_endpoint-5","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-1","content":"Usage"},"2253":{"id":"/reference/cli/lep_endpoint-6","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-1","content":"Options"},"2254":{"id":"/reference/cli/lep_endpoint-7","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-list","content":"lep endpoint list"},"2255":{"id":"/reference/cli/lep_endpoint-8","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-2","content":"Usage"},"2256":{"id":"/reference/cli/lep_endpoint-9","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-2","content":"Options"},"2257":{"id":"/reference/cli/lep_endpoint-10","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-restart","content":"lep endpoint restart"},"2258":{"id":"/reference/cli/lep_endpoint-11","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-3","content":"Usage"},"2259":{"id":"/reference/cli/lep_endpoint-12","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-3","content":"Options"},"2260":{"id":"/reference/cli/lep_endpoint-13","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-remove","content":"lep endpoint remove"},"2261":{"id":"/reference/cli/lep_endpoint-14","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-4","content":"Usage"},"2262":{"id":"/reference/cli/lep_endpoint-15","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-4","content":"Options"},"2263":{"id":"/reference/cli/lep_endpoint-16","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-status","content":"lep endpoint status"},"2264":{"id":"/reference/cli/lep_endpoint-17","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-5","content":"Usage"},"2265":{"id":"/reference/cli/lep_endpoint-18","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-5","content":"Options"},"2266":{"id":"/reference/cli/lep_endpoint-19","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-log","content":"lep endpoint log"},"2267":{"id":"/reference/cli/lep_endpoint-20","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-6","content":"Usage"},"2268":{"id":"/reference/cli/lep_endpoint-21","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-6","content":"Options"},"2269":{"id":"/reference/cli/lep_endpoint-22","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-update","content":"lep endpoint update"},"2270":{"id":"/reference/cli/lep_endpoint-23","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-7","content":"Usage"},"2271":{"id":"/reference/cli/lep_endpoint-24","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-7","content":"Options"},"2272":{"id":"/reference/cli/lep_endpoint-25","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-events","content":"lep endpoint events"},"2273":{"id":"/reference/cli/lep_endpoint-26","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-8","content":"Usage"},"2274":{"id":"/reference/cli/lep_endpoint-27","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-8","content":"Options"},"2275":{"id":"/reference/cli/lep_endpoint-28","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-get","content":"lep endpoint get"},"2276":{"id":"/reference/cli/lep_endpoint-29","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-9","content":"Usage"},"2277":{"id":"/reference/cli/lep_endpoint-30","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-9","content":"Options"},"2278":{"id":"/reference/cli/lep_endpoint-31","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#lep-endpoint-stop","content":"lep endpoint stop"},"2279":{"id":"/reference/cli/lep_endpoint-32","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#usage-10","content":"Usage"},"2280":{"id":"/reference/cli/lep_endpoint-33","page_id":"/reference/cli/lep_endpoint","type":"heading","tags":[],"url":"/reference/cli/lep_endpoint#options-10","content":"Options"},"2281":{"id":"/reference/cli/lep_endpoint-34","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint","content":"Manage endpoints (formerly called deployments) on the DGX Cloud Lepton."},"2282":{"id":"/reference/cli/lep_endpoint-35","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage","content":"lep endpoint [OPTIONS] COMMAND [ARGS]..."},"2283":{"id":"/reference/cli/lep_endpoint-36","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options","content":"--help : Show this message and exit."},"2284":{"id":"/reference/cli/lep_endpoint-37","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"create : Creates an endpoint from either a photon or container image."},"2285":{"id":"/reference/cli/lep_endpoint-38","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"events : Lists events of the endpoint"},"2286":{"id":"/reference/cli/lep_endpoint-39","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"get : Shows Endpoint detail and optionally saves its spec JSON."},"2287":{"id":"/reference/cli/lep_endpoint-40","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"list : Lists all endpoints in the current workspace."},"2288":{"id":"/reference/cli/lep_endpoint-41","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"log : Gets the log of an endpoint."},"2289":{"id":"/reference/cli/lep_endpoint-42","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"remove : Removes an endpoint."},"2290":{"id":"/reference/cli/lep_endpoint-43","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"restart : Restarts an endpoint."},"2291":{"id":"/reference/cli/lep_endpoint-44","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"status : Gets the status of an endpoint."},"2292":{"id":"/reference/cli/lep_endpoint-45","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"stop : Stops a deployment by its name."},"2293":{"id":"/reference/cli/lep_endpoint-46","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#commands","content":"update : Updates an endpoint."},"2294":{"id":"/reference/cli/lep_endpoint-47","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-create","content":"Creates an endpoint from either a photon or container image."},"2295":{"id":"/reference/cli/lep_endpoint-48","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-1","content":"lep endpoint create [OPTIONS]"},"2296":{"id":"/reference/cli/lep_endpoint-49","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-n,  --name TEXT : Name of the endpoint being created. [required]"},"2297":{"id":"/reference/cli/lep_endpoint-50","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-f,  --file FILE : If provided, load the endpoint spec from this JSON file before applying CLI overrides. The file can be obtained from the dashboard's UI → CLI → 'Use spec file', or by running: lep endpoint get -i <endpoint_id> --path <download_path>."},"2298":{"id":"/reference/cli/lep_endpoint-51","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-p,  --photon TEXT : Name of the photon to run."},"2299":{"id":"/reference/cli/lep_endpoint-52","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-i,  --photon-id TEXT : Specific version id of the photon to run. If not specified, we will run the most recent version of the photon."},"2300":{"id":"/reference/cli/lep_endpoint-53","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--container-image TEXT : Container image to run."},"2301":{"id":"/reference/cli/lep_endpoint-54","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--container-port INTEGER : Guest OS port to listen to in the container. If not specified, default to 8080."},"2302":{"id":"/reference/cli/lep_endpoint-55","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--container-command TEXT : Command to run in the container. Your command should listen to the port specified by --container-port."},"2303":{"id":"/reference/cli/lep_endpoint-56","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--cserve : Enable cserve mode and attach cserve options to the deployment spec."},"2304":{"id":"/reference/cli/lep_endpoint-57","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--cserve-options TEXT : JSON string for cserve options (stored at spec.cserve)."},"2305":{"id":"/reference/cli/lep_endpoint-58","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-rs,  --resource-shape TEXT : Resource shape for the endpoint. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."},"2306":{"id":"/reference/cli/lep_endpoint-59","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--min-replicas INTEGER : (Will be deprecated soon) Minimum number of replicas."},"2307":{"id":"/reference/cli/lep_endpoint-60","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--max-replicas INTEGER : (Will be deprecated) Maximum number of replicas."},"2308":{"id":"/reference/cli/lep_endpoint-61","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--mount TEXT : Persistent storage to be mounted to the endpoint, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."},"2309":{"id":"/reference/cli/lep_endpoint-62","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-e,  --env TEXT : Environment variables to pass to the endpoint, in the format NAME=VALUE."},"2310":{"id":"/reference/cli/lep_endpoint-63","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-s,  --secret TEXT : Secrets to pass to the endpoint, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."},"2311":{"id":"/reference/cli/lep_endpoint-64","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--public : Make the endpoint public (no IP restriction). Mutually exclusive with --ip-whitelist. Can be combined with --tokens (public + tokens); '--public --tokens' is equivalent to '--tokens'. If neither --ip-whitelist nor --tokens is provided, the endpoint defaults to public access."},"2312":{"id":"/reference/cli/lep_endpoint-65","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--ip-whitelist TEXT : IP addresses or CIDR ranges that are allowed to access the endpoint. Can be specified multiple times or as comma-separated values. Examples: --ip-whitelist 192.168.1.1,10.0.0.0/8 or --ip-whitelist 192.168.1.1 --ip-whitelist 10.0.0.0/8. Mutually exclusive with --public. This sets the IP allowlist in the deployment's authentication configuration. Note: --tokens are completely independent of IP access control."},"2313":{"id":"/reference/cli/lep_endpoint-66","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--tokens TEXT : Additional tokens that can be used to access the endpoint. See docs for details on access control. These are completely independent of IP access control (--public and --ip-whitelist)."},"2314":{"id":"/reference/cli/lep_endpoint-67","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--no-traffic-timeout INTEGER : (Will be deprecated soon)If specified, the endpoint will be scaled down to 0 replicas after the specified number of seconds without traffic. Minimum is 60 seconds if set. Note that actual timeout may be up to 30 seconds longer than the specified value."},"2315":{"id":"/reference/cli/lep_endpoint-68","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--target-gpu-utilization INTEGER : (Will be deprecated soon)If min and max replicas are set, if the gpu utilization is higher than the target gpu utilization, autoscaler will scale up the replicas. If the gpu utilization is lower than the target gpu utilization, autoscaler will scale down the replicas. The value should be between 0 and 99."},"2316":{"id":"/reference/cli/lep_endpoint-69","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--initial-delay-seconds INTEGER : If specified, the endpoint will allow the specified amount of seconds for the photon to initialize before it starts the service. Usually you should not need this. If you have an endpoint that takes a long time to initialize, set it to a longer value."},"2317":{"id":"/reference/cli/lep_endpoint-70","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--rerun : If specified, shutdown the endpoint of the same endpoint name and rerun it. Note that this may cause downtime of the photon if it is for production use, so use with caution. In a production environment, you should do photon create, push, and lep endpoint update instead."},"2318":{"id":"/reference/cli/lep_endpoint-71","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--public-photon : If specified, get the photon from the public photon registry. This is only supported for remote execution."},"2319":{"id":"/reference/cli/lep_endpoint-72","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--image-pull-secrets TEXT : Secrets to use for pulling images."},"2320":{"id":"/reference/cli/lep_endpoint-73","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-ng,  --node-group TEXT : Node group for the endpoint. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."},"2321":{"id":"/reference/cli/lep_endpoint-74","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--visibility TEXT : Visibility of the endpoint. Can be 'public' or 'private'. If private, the endpoint will only be viewable by the creator and workspace admin."},"2322":{"id":"/reference/cli/lep_endpoint-75","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-r,  -replicas, --replicas-static INTEGER : Use this option if you want a fixed number of replicas and want to turn off autoscaling.\nFor example, to set a fixed number of replicas to 2, you can use:\n--replicas-static 2 or\n-r 2"},"2323":{"id":"/reference/cli/lep_endpoint-76","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-ad,  --autoscale-down TEXT : Use this option if you want to have replicas but scale down after a specified time of no traffic.\nFor example, to set 2 replicas and scale down after 3600 seconds of no traffic,\nuse: --autoscale-down 2,3600s or --autoscale-down 2,3600\n(Note: Do not include spaces around the comma.)"},"2324":{"id":"/reference/cli/lep_endpoint-77","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-agu,  --autoscale-gpu-util TEXT : Use this option to set a threshold for GPU utilization and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 50% threshold,\nuse: --autoscale-gpu-util 1,3,50% or --autoscale-gpu-util 1,3,50\n(Note: Do not include spaces around the comma.)"},"2325":{"id":"/reference/cli/lep_endpoint-78","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"If the GPU utilization is higher than the target GPU utilization,\nthe autoscaler will scale up the replicas.\nIf the GPU utilization is lower than the target GPU utilization,\nthe autoscaler will scale down the replicas.\nThe threshold value should be between 0 and 99."},"2326":{"id":"/reference/cli/lep_endpoint-79","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-aq,  --autoscale-qpm TEXT : Use this option to set a threshold for QPM and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 2.5 QPM,\nuse: --autoscale-qpm 1,3,2.5\n(Note: Do not include spaces around the comma.)"},"2327":{"id":"/reference/cli/lep_endpoint-80","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"This sets up autoscaling based on queries per minute,\nscaling between 1 and 3 replicas when QPM per replica exceeds 2.5."},"2328":{"id":"/reference/cli/lep_endpoint-81","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."},"2329":{"id":"/reference/cli/lep_endpoint-82","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--privileged : Run the endpoint in privileged mode."},"2330":{"id":"/reference/cli/lep_endpoint-83","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-ni,  --node-id TEXT : Node for the endpoint. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"},"2331":{"id":"/reference/cli/lep_endpoint-84","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-qp,  --queue-priority TEXT : Set the priority for this endpoint (dedicated node groups only)."},"2332":{"id":"/reference/cli/lep_endpoint-85","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-cbp,  --can-be-preempted : Allow this endpoint to be preempted by higher priority workloads."},"2333":{"id":"/reference/cli/lep_endpoint-86","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"-cp,  --can-preempt : Allow this endpoint to preempt lower priority workloads."},"2334":{"id":"/reference/cli/lep_endpoint-87","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--shared-memory-size INTEGER : Specify the shared memory size for this endpoint, in MiB."},"2335":{"id":"/reference/cli/lep_endpoint-88","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--with-reservation TEXT : Assign the endpoint to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups)."},"2336":{"id":"/reference/cli/lep_endpoint-89","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--allow-burst-to-other-reservation : If set, the endpoint can temporarily use free resources from nodes reserved by other reservations. Be aware that when a new workload bound to those reservations starts, your endpoint may be evicted."},"2337":{"id":"/reference/cli/lep_endpoint-90","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--replica-spread TEXT : Controls how endpoint replicas are distributed across different nodes to improve availability, but it may lead to resource fragmentation."},"2338":{"id":"/reference/cli/lep_endpoint-91","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"Preferred (p): Attempts to spread replicas across different nodes when possible.\nRequired (r): Enforces strict replica spreading where each replica must be scheduled on a different node. Replicas cannot start if there are not enough nodes."},"2339":{"id":"/reference/cli/lep_endpoint-92","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"Usage examples:\n--replica-spread required/r (strict)\n--replica-spread preferred/p (soft)"},"2340":{"id":"/reference/cli/lep_endpoint-93","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--ingress-timeout-seconds INTEGER : Ingress request timeout in seconds (300-6000). If not specified, defaults to 300."},"2341":{"id":"/reference/cli/lep_endpoint-94","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--load-balance [least-request|sticky-routing-default|sticky-routing-by-host-name|sticky-routing-by-resolved-ip] : Load balancing strategy: least-request | sticky-routing-default | sticky-routing-by-host-name | sticky-routing-by-resolved-ip"},"2342":{"id":"/reference/cli/lep_endpoint-95","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-1","content":"--help : Show this message and exit."},"2343":{"id":"/reference/cli/lep_endpoint-96","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-list","content":"Lists all endpoints in the current workspace."},"2344":{"id":"/reference/cli/lep_endpoint-97","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-2","content":"lep endpoint list [OPTIONS]"},"2345":{"id":"/reference/cli/lep_endpoint-98","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-2","content":"-n,  --name TEXT : Filter endpoints by name (case-insensitive substring). Can be specified multiple times."},"2346":{"id":"/reference/cli/lep_endpoint-99","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-2","content":"--help : Show this message and exit."},"2347":{"id":"/reference/cli/lep_endpoint-100","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-restart","content":"Restarts an endpoint."},"2348":{"id":"/reference/cli/lep_endpoint-101","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-3","content":"lep endpoint restart [OPTIONS]"},"2349":{"id":"/reference/cli/lep_endpoint-102","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-3","content":"-n,  --name TEXT : The endpoint name to restart. [required]"},"2350":{"id":"/reference/cli/lep_endpoint-103","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-3","content":"--help : Show this message and exit."},"2351":{"id":"/reference/cli/lep_endpoint-104","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-remove","content":"Removes an endpoint."},"2352":{"id":"/reference/cli/lep_endpoint-105","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-4","content":"lep endpoint remove [OPTIONS]"},"2353":{"id":"/reference/cli/lep_endpoint-106","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-4","content":"-n,  --name TEXT : The endpoint name to remove. [required]"},"2354":{"id":"/reference/cli/lep_endpoint-107","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-4","content":"--help : Show this message and exit."},"2355":{"id":"/reference/cli/lep_endpoint-108","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-status","content":"Gets the status of an endpoint."},"2356":{"id":"/reference/cli/lep_endpoint-109","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-5","content":"lep endpoint status [OPTIONS]"},"2357":{"id":"/reference/cli/lep_endpoint-110","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-5","content":"-n,  --name TEXT : The endpoint name to get status. [required]"},"2358":{"id":"/reference/cli/lep_endpoint-111","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-5","content":"-t,  --show-tokens : Show tokens for the endpoint. Use with caution as this displays the tokens in plain text, and may be visible to others if you log the output."},"2359":{"id":"/reference/cli/lep_endpoint-112","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-5","content":"-d,  --detail : Show the endpoint detail"},"2360":{"id":"/reference/cli/lep_endpoint-113","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-5","content":"--help : Show this message and exit."},"2361":{"id":"/reference/cli/lep_endpoint-114","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-log","content":"Gets the log of an endpoint. If replica is not specified, the first\nreplica is selected. Otherwise, the log of the specified replica is shown.\nTo get the list of replicas, use lep endpoint status."},"2362":{"id":"/reference/cli/lep_endpoint-115","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-6","content":"lep endpoint log [OPTIONS]"},"2363":{"id":"/reference/cli/lep_endpoint-116","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-6","content":"-n,  --name TEXT : The endpoint name to get log. [required]"},"2364":{"id":"/reference/cli/lep_endpoint-117","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-6","content":"-r,  --replica TEXT : The replica name to get log."},"2365":{"id":"/reference/cli/lep_endpoint-118","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-6","content":"--help : Show this message and exit."},"2366":{"id":"/reference/cli/lep_endpoint-119","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-update","content":"Updates an endpoint. Note that for all the update options, changes are made\nas replacements, and not incrementals. For example, if you specify\n--tokens, old tokens are replaced by the new set of tokens."},"2367":{"id":"/reference/cli/lep_endpoint-120","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-7","content":"lep endpoint update [OPTIONS]"},"2368":{"id":"/reference/cli/lep_endpoint-121","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-n,  --name TEXT : The endpoint name to update. [required]"},"2369":{"id":"/reference/cli/lep_endpoint-122","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-i,  --id TEXT : The new photon id to update to. Use latest for the latest id."},"2370":{"id":"/reference/cli/lep_endpoint-123","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--container-image TEXT : Update the container image for a container-based endpoint. Not supported for photon-based endpoints."},"2371":{"id":"/reference/cli/lep_endpoint-124","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--min-replicas INTEGER : Number of replicas to update to. Pass 0 to scale the number of replicas to zero, in which case the deployment status page will show the endpoint to be not ready until you scale it back with a positive number of replicas."},"2372":{"id":"/reference/cli/lep_endpoint-125","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."},"2373":{"id":"/reference/cli/lep_endpoint-126","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--public : Make the endpoint public (clears IP allowlist). To restrict access, use --ip-whitelist. Mutually exclusive with --ip-whitelist. Note: public can be combined with --tokens; tokens are independent and will not be removed unless explicitly updated (use --remove-tokens to clear)."},"2374":{"id":"/reference/cli/lep_endpoint-127","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--ip-whitelist TEXT : IP addresses or CIDR ranges that are allowed to access the endpoint. Can be specified multiple times or as comma-separated values. Examples: --ip-whitelist 192.168.1.1,10.0.0.0/8 or --ip-whitelist 192.168.1.1 --ip-whitelist 10.0.0.0/8. Mutually exclusive with --public. This sets the IP allowlist in the deployment's authentication configuration."},"2375":{"id":"/reference/cli/lep_endpoint-128","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--tokens TEXT : Access tokens that can be used to access the endpoint. See docs for details on access control. If no tokens is specified, we will not change the tokens of the endpoint."},"2376":{"id":"/reference/cli/lep_endpoint-129","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--visibility TEXT : Visibility of the endpoint. Can be 'public' or 'private'. If private, the endpoint will only be viewable by the creator and workspace admin."},"2377":{"id":"/reference/cli/lep_endpoint-130","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-r,  -replicas, --replicas-static INTEGER : Use this option if you want a fixed number of replicas and want to turn off autoscaling.\nFor example, to set a fixed number of replicas to 2, you can use:\n--replicas-static 2 or\n-r 2"},"2378":{"id":"/reference/cli/lep_endpoint-131","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-ad,  --autoscale-down TEXT : Use this option if you want to have replicas but scale down after a specified time of no traffic.\nFor example, to set 2 replicas and scale down after 3600 seconds of no traffic,\nuse: --autoscale-down 2,3600s or --autoscale-down 2,3600\n(Note: Do not include spaces around the comma.)"},"2379":{"id":"/reference/cli/lep_endpoint-132","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-agu,  --autoscale-gpu-util TEXT : Use this option to set a threshold for GPU utilization and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 50% threshold,\nuse: --autoscale-gpu-util 1,3,50% or --autoscale-gpu-util 1,3,50\n(Note: Do not include spaces around the comma.)"},"2380":{"id":"/reference/cli/lep_endpoint-133","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"If the GPU utilization is higher than the target GPU utilization,\nthe autoscaler will scale up the replicas.\nIf the GPU utilization is lower than the target GPU utilization,\nthe autoscaler will scale down the replicas.\nThe threshold value should be between 0 and 99."},"2381":{"id":"/reference/cli/lep_endpoint-134","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-aq,  --autoscale-qpm TEXT : Use this option to set a threshold for QPM and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 2.5 QPM,\nuse: --autoscale-qpm 1,3,2.5\n(Note: Do not include spaces around the comma.)"},"2382":{"id":"/reference/cli/lep_endpoint-135","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"This sets up autoscaling based on queries per minute,\nscaling between 1 and 3 replicas when QPM per replica exceeds 2.5."},"2383":{"id":"/reference/cli/lep_endpoint-136","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."},"2384":{"id":"/reference/cli/lep_endpoint-137","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--shared-memory-size INTEGER : Update the shared memory size for this endpoint, in MiB."},"2385":{"id":"/reference/cli/lep_endpoint-138","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--replica-spread TEXT : Controls how endpoint replicas are distributed across different nodes to improve availability, but it may lead to resource fragmentation."},"2386":{"id":"/reference/cli/lep_endpoint-139","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"Preferred (p): Attempts to spread replicas across different nodes when possible.\nRequired (r): Enforces strict replica spreading where each replica must be scheduled on a different node. Replicas cannot start if there are not enough nodes."},"2387":{"id":"/reference/cli/lep_endpoint-140","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"Usage examples:\n--replica-spread required/r (strict)\n--replica-spread preferred/p (soft)"},"2388":{"id":"/reference/cli/lep_endpoint-141","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--ingress-timeout-seconds INTEGER : Ingress request timeout in seconds (300-6000)."},"2389":{"id":"/reference/cli/lep_endpoint-142","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--load-balance [least-request|sticky-routing-default|sticky-routing-by-host-name|sticky-routing-by-resolved-ip] : Load balancing strategy: least-request | sticky-routing-default | sticky-routing-by-host-name | sticky-routing-by-resolved-ip"},"2390":{"id":"/reference/cli/lep_endpoint-143","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--cserve : Enable cserve mode and attach cserve options to the deployment spec."},"2391":{"id":"/reference/cli/lep_endpoint-144","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--cserve-options TEXT : JSON string for cserve options (stored at spec.cserve)."},"2392":{"id":"/reference/cli/lep_endpoint-145","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-7","content":"--help : Show this message and exit."},"2393":{"id":"/reference/cli/lep_endpoint-146","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-events","content":"Lists events of the endpoint"},"2394":{"id":"/reference/cli/lep_endpoint-147","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-8","content":"lep endpoint events [OPTIONS]"},"2395":{"id":"/reference/cli/lep_endpoint-148","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-8","content":"-n,  --name TEXT : The endpoint name to get status. 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Directory or full filename accepted.\nIf a directory is provided, the file will be saved as endpoint-spec-[name].json."},"2401":{"id":"/reference/cli/lep_endpoint-154","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-9","content":"--help : Show this message and exit."},"2402":{"id":"/reference/cli/lep_endpoint-155","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#lep-endpoint-stop","content":"Stops a deployment by its name."},"2403":{"id":"/reference/cli/lep_endpoint-156","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#usage-10","content":"lep endpoint stop [OPTIONS]"},"2404":{"id":"/reference/cli/lep_endpoint-157","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-10","content":"-n,  --name TEXT : The endpoint name to stop. [required]"},"2405":{"id":"/reference/cli/lep_endpoint-158","page_id":"/reference/cli/lep_endpoint","tags":[],"type":"text","url":"/reference/cli/lep_endpoint#options-10","content":"--help : Show this message and exit."},"2406":{"id":"/reference/cli/lep_ingress","page_id":"/reference/cli/lep_ingress","type":"page","content":"lep ingress","tags":[],"url":"/reference/cli/lep_ingress"},"2407":{"id":"/reference/cli/lep_ingress-0","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress","content":"Manage ingress on the DGX Cloud Lepton."},"2408":{"id":"/reference/cli/lep_ingress-1","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#usage","content":"Usage"},"2409":{"id":"/reference/cli/lep_ingress-2","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#options","content":"Options"},"2410":{"id":"/reference/cli/lep_ingress-3","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#commands","content":"Commands"},"2411":{"id":"/reference/cli/lep_ingress-4","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#lep-ingress-list","content":"lep ingress list"},"2412":{"id":"/reference/cli/lep_ingress-5","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#usage-1","content":"Usage"},"2413":{"id":"/reference/cli/lep_ingress-6","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#options-1","content":"Options"},"2414":{"id":"/reference/cli/lep_ingress-7","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#lep-ingress-create","content":"lep ingress create"},"2415":{"id":"/reference/cli/lep_ingress-8","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#usage-2","content":"Usage"},"2416":{"id":"/reference/cli/lep_ingress-9","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#options-2","content":"Options"},"2417":{"id":"/reference/cli/lep_ingress-10","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#lep-ingress-get","content":"lep ingress get"},"2418":{"id":"/reference/cli/lep_ingress-11","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#usage-3","content":"Usage"},"2419":{"id":"/reference/cli/lep_ingress-12","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#options-3","content":"Options"},"2420":{"id":"/reference/cli/lep_ingress-13","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#lep-ingress-delete","content":"lep ingress delete"},"2421":{"id":"/reference/cli/lep_ingress-14","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#usage-4","content":"Usage"},"2422":{"id":"/reference/cli/lep_ingress-15","page_id":"/reference/cli/lep_ingress","type":"heading","tags":[],"url":"/reference/cli/lep_ingress#options-4","content":"Options"},"2423":{"id":"/reference/cli/lep_ingress-16","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress","content":"Manage ingress on the DGX Cloud Lepton."},"2424":{"id":"/reference/cli/lep_ingress-17","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#usage","content":"lep ingress [OPTIONS] COMMAND [ARGS]..."},"2425":{"id":"/reference/cli/lep_ingress-18","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options","content":"--help : Show this message and exit."},"2426":{"id":"/reference/cli/lep_ingress-19","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#commands","content":"create : Create an ingress"},"2427":{"id":"/reference/cli/lep_ingress-20","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#commands","content":"delete : Delete an ingress by name"},"2428":{"id":"/reference/cli/lep_ingress-21","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#commands","content":"get : Get an ingress by name and print it in json"},"2429":{"id":"/reference/cli/lep_ingress-22","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#commands","content":"list : List all ingress"},"2430":{"id":"/reference/cli/lep_ingress-23","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#lep-ingress-list","content":"List all ingress"},"2431":{"id":"/reference/cli/lep_ingress-24","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#usage-1","content":"lep ingress list [OPTIONS]"},"2432":{"id":"/reference/cli/lep_ingress-25","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options-1","content":"--help : Show this message and exit."},"2433":{"id":"/reference/cli/lep_ingress-26","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#lep-ingress-create","content":"Create an ingress"},"2434":{"id":"/reference/cli/lep_ingress-27","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#usage-2","content":"lep ingress create [OPTIONS]"},"2435":{"id":"/reference/cli/lep_ingress-28","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options-2","content":"-d,  --domain-name TEXT : domain-name of the 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[required]"},"2440":{"id":"/reference/cli/lep_ingress-33","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options-3","content":"--help : Show this message and exit."},"2441":{"id":"/reference/cli/lep_ingress-34","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#lep-ingress-delete","content":"Delete a ingress by name"},"2442":{"id":"/reference/cli/lep_ingress-35","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#usage-4","content":"lep ingress delete [OPTIONS]"},"2443":{"id":"/reference/cli/lep_ingress-36","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options-4","content":"-n,  --name TEXT : name of the ingress [required]"},"2444":{"id":"/reference/cli/lep_ingress-37","page_id":"/reference/cli/lep_ingress","tags":[],"type":"text","url":"/reference/cli/lep_ingress#options-4","content":"--help : 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Lepton Jobs."},"2490":{"id":"/reference/cli/lep_job-44","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage","content":"lep job [OPTIONS] COMMAND [ARGS]..."},"2491":{"id":"/reference/cli/lep_job-45","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options","content":"--help : Show this message and exit."},"2492":{"id":"/reference/cli/lep_job-46","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"clone : Creates a copy of an existing job by its ID."},"2493":{"id":"/reference/cli/lep_job-47","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"create : Creates a job."},"2494":{"id":"/reference/cli/lep_job-48","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"events : Prints the events of a job by its ID."},"2495":{"id":"/reference/cli/lep_job-49","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"get : Gets detailed information about jobs."},"2496":{"id":"/reference/cli/lep_job-50","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"list : Lists all jobs in the current workspace."},"2497":{"id":"/reference/cli/lep_job-51","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"log : Gets the log of a job."},"2498":{"id":"/reference/cli/lep_job-52","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"nodes : Prints the nodes id of a job."},"2499":{"id":"/reference/cli/lep_job-53","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"remove : Removes a single job."},"2500":{"id":"/reference/cli/lep_job-54","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"remove-all : Removes all jobs matching the specified filters."},"2501":{"id":"/reference/cli/lep_job-55","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"replicas : Prints the replicas id of a job."},"2502":{"id":"/reference/cli/lep_job-56","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"start : Starts a job by its ID."},"2503":{"id":"/reference/cli/lep_job-57","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"stop : Stops a job by its ID."},"2504":{"id":"/reference/cli/lep_job-58","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#commands","content":"stop-all : Stop all jobs matching the specified filters."},"2505":{"id":"/reference/cli/lep_job-59","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-create","content":"Creates a job."},"2506":{"id":"/reference/cli/lep_job-60","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-create","content":"For advanced uses, check\nhttps://kubernetes.io/docs/concepts/workloads/controllers/job/."},"2507":{"id":"/reference/cli/lep_job-61","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-1","content":"lep job create [OPTIONS]"},"2508":{"id":"/reference/cli/lep_job-62","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-n,  --name TEXT : Job name [required]"},"2509":{"id":"/reference/cli/lep_job-63","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-f,  --file TEXT : If provided, load the job spec from this JSON file before applying CLI overrides. The file can be obtained from the dashboard's UI → CLI → 'Use spec file', or by running: lep job get -i <job_id> --path <download_path>."},"2510":{"id":"/reference/cli/lep_job-64","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-t,  --template TEXT : template ID to render the job specification from."},"2511":{"id":"/reference/cli/lep_job-65","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--run TEXT : Command string (\"run\") to substitute into the template."},"2512":{"id":"/reference/cli/lep_job-66","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--container-image TEXT : Container image for the job. If not set, default to leptonai.config.BASE_IMAGE"},"2513":{"id":"/reference/cli/lep_job-67","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--container-port TEXT : Ports to expose for the job, in the format [portnumber]:[protocol(tcp/udp/sctp)]."},"2514":{"id":"/reference/cli/lep_job-68","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--port TEXT : Deprecated flag, use --container-port instead."},"2515":{"id":"/reference/cli/lep_job-69","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--command TEXT : Command string to run for the job."},"2516":{"id":"/reference/cli/lep_job-70","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."},"2517":{"id":"/reference/cli/lep_job-71","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-w,  --num-workers INTEGER : Number of workers to use for the job. For example, when you do a distributed training job of 4 replicas, use --num-workers 4."},"2518":{"id":"/reference/cli/lep_job-72","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--segment-count INTEGER : Segment count for GB200 node groups. Must satisfy: 1 <= segment_count < num_workers, and num_workers % segment_count == 0. Workers within the same segment are scheduled into one NVL72 domain."},"2519":{"id":"/reference/cli/lep_job-73","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--max-failure-retry INTEGER : Maximum number of failures to retry per worker."},"2520":{"id":"/reference/cli/lep_job-74","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--max-job-failure-retry INTEGER : Maximum number of failures to retry per whole job."},"2521":{"id":"/reference/cli/lep_job-75","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-e,  --env TEXT : Environment variables to pass to the job, in the format NAME=VALUE."},"2522":{"id":"/reference/cli/lep_job-76","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-s,  --secret TEXT : Secrets to pass to the job, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."},"2523":{"id":"/reference/cli/lep_job-77","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--mount TEXT : Persistent storage to be mounted to the job, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."},"2524":{"id":"/reference/cli/lep_job-78","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--image-pull-secrets TEXT : Secrets to use for pulling images."},"2525":{"id":"/reference/cli/lep_job-79","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--intra-job-communication BOOLEAN : Enable intra-job communication. If --num-workers is set, this is automatically enabled."},"2526":{"id":"/reference/cli/lep_job-80","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--privileged : Run the job in privileged mode."},"2527":{"id":"/reference/cli/lep_job-81","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--ttl-seconds-after-finished INTEGER : (advanced feature) limits the lifetime of a job that has finished execution (either Completed or Failed). If not set, we will have it default to 72 hours. Ref: https://kubernetes.io/docs/concepts/workloads/controllers/job/#ttl-mechanism-for-finished-jobs"},"2528":{"id":"/reference/cli/lep_job-82","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."},"2529":{"id":"/reference/cli/lep_job-83","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-ng,  --node-group TEXT : Node group for the job. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."},"2530":{"id":"/reference/cli/lep_job-84","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-ni,  --node-id TEXT : Node for the job. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"},"2531":{"id":"/reference/cli/lep_job-85","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-qp,  --queue-priority TEXT : Set the priority for this job (feature available only for dedicated node groups).\nCould be one of low-1, low-2, low-3, mid-4, mid-5, mid-6, high-7, high-8, high-9,Options: 1-9 or keywords: l / low (will be 1), m / mid (will be 4), h / high (will be 7).\nExamples: -qp 1, -qp 9, -qp low, -qp mid, -qp high, -qp l, -qp m, -qp h"},"2532":{"id":"/reference/cli/lep_job-86","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-cbp,  --can-be-preempted : Allow this job to be preempted by higher priority jobs (only for dedicated node groups)."},"2533":{"id":"/reference/cli/lep_job-87","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"-cp,  --can-preempt : Allow this job to preempt lower priority jobs (only for dedicated node groups)."},"2534":{"id":"/reference/cli/lep_job-88","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--visibility TEXT : Visibility of the job. Can be 'public' or 'private'. If private, the job will only be viewable by the creator and workspace admin."},"2535":{"id":"/reference/cli/lep_job-89","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--shared-memory-size INTEGER : Specify the shared memory size for this job, in MiB."},"2536":{"id":"/reference/cli/lep_job-90","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--with-reservation TEXT : Assign the job to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups). If not provided, the job will be scheduled as usual."},"2537":{"id":"/reference/cli/lep_job-91","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--start-at TEXT : Schedule the job to start at a specific time. If not specified, the job will start immediately.\nSupported formats: - Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS\nExample: 2024/12/25 13:10:01"},"2538":{"id":"/reference/cli/lep_job-92","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"Alternate Format: YYYY-MM-DD HH:MM:SS\nExample: 2024-12-25 13:10:01"},"2539":{"id":"/reference/cli/lep_job-93","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:05 (1:10:05 AM of the current day)"},"2540":{"id":"/reference/cli/lep_job-94","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"tomorrow or tm:\nExample Variations:\ntomorrow (defaults to midnight of the next day)\ntomorrow 13 (1 PM of the next day)\ntomorrow 13:10 (1:10 PM of the next day)\ntomorrow 13:10:05 (1:10:05 PM of the next day)"},"2541":{"id":"/reference/cli/lep_job-95","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"Note: By default, all times are interpreted in your local timezone. To specify UTC time, prefix your input with 'UTC:' or 'utc:'. For example:"},"2542":{"id":"/reference/cli/lep_job-96","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"UTC:2024/12/25 13:10:01"},"2543":{"id":"/reference/cli/lep_job-97","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"utc:tomorrow 13:10"},"2544":{"id":"/reference/cli/lep_job-98","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--allow-burst-to-other-reservation : If set, the job can temporarily use free resources from nodes reserved by other reservations. Be aware that when a new workload bound to those reservations starts, your job may be evicted."},"2545":{"id":"/reference/cli/lep_job-99","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-1","content":"--help : Show this message and exit."},"2546":{"id":"/reference/cli/lep_job-100","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-list","content":"Lists all jobs in the current workspace."},"2547":{"id":"/reference/cli/lep_job-101","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-list","content":"You can filter jobs by: - State: Case-insensitive prefix match (e.g., 'run'\nmatches 'Running') - User: Case-insensitive prefix match (e.g., 'alice'\nmatches 'alice123') - Name/ID: Case-insensitive substring match (e.g.,\n'train' matches 'training-job-123') - Node Group: Case-insensitive substring\nmatch"},"2548":{"id":"/reference/cli/lep_job-102","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-list","content":"Multiple filters can be combined. For example: lep job list -s queue -u\nalice -n train -ng h100"},"2549":{"id":"/reference/cli/lep_job-103","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-2","content":"lep job list [OPTIONS]"},"2550":{"id":"/reference/cli/lep_job-104","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-2","content":"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."},"2551":{"id":"/reference/cli/lep_job-105","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-2","content":"-u,  --user TEXT : Filter jobs by user. Case-insensitive and matches the beginning of the username. Can specify multiple users. Example: 'alice' will match 'alice123'"},"2552":{"id":"/reference/cli/lep_job-106","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-2","content":"-n,  --name-or-id TEXT : Filter jobs by name or id. Case-insensitive and matches any part of the name or id. Can specify multiple names or ids. Example: 'train' will match 'training-job-123'"},"2553":{"id":"/reference/cli/lep_job-107","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-2","content":"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."},"2554":{"id":"/reference/cli/lep_job-108","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-2","content":"--help : Show this message and exit."},"2555":{"id":"/reference/cli/lep_job-109","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-remove-all","content":"Removes all jobs matching the specified filters. At least one filter must be\nprovided. For safety, name and user filters require exact matches. State\nfilter remains flexible. The --user option is required to prevent accidental\noperations on other users' jobs."},"2556":{"id":"/reference/cli/lep_job-110","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-3","content":"lep job remove-all [OPTIONS]"},"2557":{"id":"/reference/cli/lep_job-111","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-3","content":"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."},"2558":{"id":"/reference/cli/lep_job-112","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-3","content":"-u,  --user TEXT : Filter jobs by exact user ID match. Case-sensitive. Can specify multiple users. For safety, this is an exact match. This option is required to prevent accidental operations on other users' jobs. [required]"},"2559":{"id":"/reference/cli/lep_job-113","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-3","content":"-n,  --name TEXT : Filter jobs by exact name match. Case-sensitive. Can specify multiple names. For safety, this is an exact match."},"2560":{"id":"/reference/cli/lep_job-114","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-3","content":"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."},"2561":{"id":"/reference/cli/lep_job-115","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-3","content":"--help : Show this message and exit."},"2562":{"id":"/reference/cli/lep_job-116","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-stop-all","content":"Stop all jobs matching the specified filters. At least one filter must be\nprovided. For safety, name and user filters require exact matches. State\nfilter remains flexible. The --user option is required to prevent accidental\noperations on other users' jobs."},"2563":{"id":"/reference/cli/lep_job-117","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-4","content":"lep job stop-all [OPTIONS]"},"2564":{"id":"/reference/cli/lep_job-118","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-4","content":"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."},"2565":{"id":"/reference/cli/lep_job-119","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-4","content":"-u,  --user TEXT : Filter jobs by exact user ID match. Case-sensitive. Can specify multiple users. For safety, this is an exact match. This option is required to prevent accidental operations on other users' jobs. [required]"},"2566":{"id":"/reference/cli/lep_job-120","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-4","content":"-n,  --name TEXT : Filter jobs by exact name match. Case-sensitive. Can specify multiple names. For safety, this is an exact match."},"2567":{"id":"/reference/cli/lep_job-121","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-4","content":"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."},"2568":{"id":"/reference/cli/lep_job-122","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-4","content":"--help : Show this message and exit."},"2569":{"id":"/reference/cli/lep_job-123","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-get","content":"Gets detailed information about jobs."},"2570":{"id":"/reference/cli/lep_job-124","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-get","content":"You can search by either name or id: - If searching by name, returns all\njobs with that exact name - If searching by id, returns the specific job\nwith that id"},"2571":{"id":"/reference/cli/lep_job-125","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-get","content":"Args: name: Job name to search for (exact match) id: Job id to\nsearch for (exact match) path: Optional path (file or directory) to save\nthe job spec JSON"},"2572":{"id":"/reference/cli/lep_job-126","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-5","content":"lep job get [OPTIONS]"},"2573":{"id":"/reference/cli/lep_job-127","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-5","content":"-n,  --name TEXT : Job name"},"2574":{"id":"/reference/cli/lep_job-128","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-5","content":"-i,  --id TEXT : Job id"},"2575":{"id":"/reference/cli/lep_job-129","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-5","content":"-p,  --path PATH : Optional local path to save the job spec JSON. Directory or full filename accepted.\nIf a directory is provided, the file will be saved as job-spec-[job_id].json so it can be reused with lep job create --file."},"2576":{"id":"/reference/cli/lep_job-130","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-5","content":"--help : Show this message and exit."},"2577":{"id":"/reference/cli/lep_job-131","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-remove","content":"Removes a single job."},"2578":{"id":"/reference/cli/lep_job-132","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-remove","content":"You can remove a job by either name or id: - If removing by name, only the\nnewest job with that exact name will be removed - If removing by id, the\nspecific job with that id will be removed"},"2579":{"id":"/reference/cli/lep_job-133","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-remove","content":"For removing multiple jobs with the same name, use 'lep job remove-all'\ninstead."},"2580":{"id":"/reference/cli/lep_job-134","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-remove","content":"Args: id: Job id to remove (exact match) name: Job name to remove\n(exact match, removes only the newest matching job)"},"2581":{"id":"/reference/cli/lep_job-135","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-6","content":"lep job remove [OPTIONS]"},"2582":{"id":"/reference/cli/lep_job-136","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-6","content":"-i,  --id TEXT : The ID of the job to remove."},"2583":{"id":"/reference/cli/lep_job-137","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-6","content":"-n,  --name TEXT : The name of the job to remove. If multiple jobs share the same name, only the newest one will be removed. Please use remove-all if you want remove multiple jobs with the same name."},"2584":{"id":"/reference/cli/lep_job-138","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-6","content":"--help : Show this message and exit."},"2585":{"id":"/reference/cli/lep_job-139","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-clone","content":"Creates a copy of an existing job by its ID."},"2586":{"id":"/reference/cli/lep_job-140","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-clone","content":"The cloned job will: - Have the same configuration as the original job -\nHave a new name with '-clone' suffix"},"2587":{"id":"/reference/cli/lep_job-141","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-clone","content":"Args: id: ID of the job to clone"},"2588":{"id":"/reference/cli/lep_job-142","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-7","content":"lep job clone [OPTIONS]"},"2589":{"id":"/reference/cli/lep_job-143","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-7","content":"-i,  --id TEXT : The job id to get events. [required]"},"2590":{"id":"/reference/cli/lep_job-144","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-7","content":"--help : Show this message and exit."},"2591":{"id":"/reference/cli/lep_job-145","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-log","content":"Gets the log of a job. If replica is not specified, the first replica is\nselected. Otherwise, the log of the specified replica is shown. To get the\nlist of replicas, use lep job replicas."},"2592":{"id":"/reference/cli/lep_job-146","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-8","content":"lep job log [OPTIONS]"},"2593":{"id":"/reference/cli/lep_job-147","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-8","content":"-i,  --id TEXT : The job id to get log. [required]"},"2594":{"id":"/reference/cli/lep_job-148","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-8","content":"-r,  --replica TEXT : The replica name to get log."},"2595":{"id":"/reference/cli/lep_job-149","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-8","content":"--help : Show this message and exit."},"2596":{"id":"/reference/cli/lep_job-150","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-replicas","content":"Prints the replicas id of a job."},"2597":{"id":"/reference/cli/lep_job-151","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-9","content":"lep job replicas [OPTIONS]"},"2598":{"id":"/reference/cli/lep_job-152","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-9","content":"-i,  --id TEXT : The job id to get replicas. [required]"},"2599":{"id":"/reference/cli/lep_job-153","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-9","content":"--help : Show this message and exit."},"2600":{"id":"/reference/cli/lep_job-154","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-nodes","content":"Prints the nodes id of a job."},"2601":{"id":"/reference/cli/lep_job-155","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-10","content":"lep job nodes [OPTIONS]"},"2602":{"id":"/reference/cli/lep_job-156","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-10","content":"-i,  --id TEXT : The job id to get replicas. [required]"},"2603":{"id":"/reference/cli/lep_job-157","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-10","content":"--help : Show this message and exit."},"2604":{"id":"/reference/cli/lep_job-158","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-stop","content":"Stops a job by its ID."},"2605":{"id":"/reference/cli/lep_job-159","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-stop","content":"Args: id: ID of the job to stop"},"2606":{"id":"/reference/cli/lep_job-160","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-11","content":"lep job stop [OPTIONS]"},"2607":{"id":"/reference/cli/lep_job-161","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-11","content":"-i,  --id TEXT : The job id to stop. [required]"},"2608":{"id":"/reference/cli/lep_job-162","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-11","content":"--help : Show this message and exit."},"2609":{"id":"/reference/cli/lep_job-163","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-start","content":"Starts a job by its ID."},"2610":{"id":"/reference/cli/lep_job-164","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-start","content":"Args: id: ID of the job to start"},"2611":{"id":"/reference/cli/lep_job-165","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-12","content":"lep job start [OPTIONS]"},"2612":{"id":"/reference/cli/lep_job-166","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-12","content":"-i,  --id TEXT : The job id to start. [required]"},"2613":{"id":"/reference/cli/lep_job-167","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-12","content":"--help : Show this message and exit."},"2614":{"id":"/reference/cli/lep_job-168","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-events","content":"Prints the events of a job by its ID."},"2615":{"id":"/reference/cli/lep_job-169","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#lep-job-events","content":"Args: id: ID of the job to get events"},"2616":{"id":"/reference/cli/lep_job-170","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#usage-13","content":"lep job events [OPTIONS]"},"2617":{"id":"/reference/cli/lep_job-171","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-13","content":"-i,  --id TEXT : The job id to get events. [required]"},"2618":{"id":"/reference/cli/lep_job-172","page_id":"/reference/cli/lep_job","tags":[],"type":"text","url":"/reference/cli/lep_job#options-13","content":"--help : Show this message and exit."},"2619":{"id":"/reference/cli/lep_log","page_id":"/reference/cli/lep_log","type":"page","content":"lep log","tags":[],"url":"/reference/cli/lep_log"},"2620":{"id":"/reference/cli/lep_log-0","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log","content":"Manage and retrieve the logs history of specific jobs, deployments and replicas."},"2621":{"id":"/reference/cli/lep_log-1","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#usage","content":"Usage"},"2622":{"id":"/reference/cli/lep_log-2","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#options","content":"Options"},"2623":{"id":"/reference/cli/lep_log-3","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#commands","content":"Commands"},"2624":{"id":"/reference/cli/lep_log-4","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#lep-log-get","content":"lep log get"},"2625":{"id":"/reference/cli/lep_log-5","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#usage-1","content":"Usage"},"2626":{"id":"/reference/cli/lep_log-6","page_id":"/reference/cli/lep_log","type":"heading","tags":[],"url":"/reference/cli/lep_log#options-1","content":"Options"},"2627":{"id":"/reference/cli/lep_log-7","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log","content":"Manage and retrieve the logs history of specific jobs, deployments and\nreplicas."},"2628":{"id":"/reference/cli/lep_log-8","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#usage","content":"lep log [OPTIONS] COMMAND [ARGS]..."},"2629":{"id":"/reference/cli/lep_log-9","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options","content":"--help : Show this message and exit."},"2630":{"id":"/reference/cli/lep_log-10","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#commands","content":"get : Retrieve and display logs from deployments, jobs, or replicas"},"2631":{"id":"/reference/cli/lep_log-11","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#lep-log-get","content":"Retrieve and display logs from deployments, jobs, or replicas"},"2632":{"id":"/reference/cli/lep_log-12","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#usage-1","content":"lep log get [OPTIONS]"},"2633":{"id":"/reference/cli/lep_log-13","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"-d,  --deployment TEXT : The name of the deployment or a LeptonDeployment object."},"2634":{"id":"/reference/cli/lep_log-14","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"-j,  --job TEXT : Specifies the job ID. To find the job ID, use the 'lep job list' command."},"2635":{"id":"/reference/cli/lep_log-15","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"-jn,  --job-name TEXT : Specifies the job name. If multiple jobs share this name, the logs for the newest job found will be retrieved by default."},"2636":{"id":"/reference/cli/lep_log-16","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--replica TEXT : The name of the replica or a Replica object."},"2637":{"id":"/reference/cli/lep_log-17","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--start TEXT : The start time in ISO format.\nPlease note that all times must be in UTC.\nKeywords such as “now,” “today,” and “yesterday” will be interpreted as UTC timestamps.\nFor example, “now” corresponds to datetime.now(timezone.utc)."},"2638":{"id":"/reference/cli/lep_log-18","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"now\nExample: now (indicates the current time in UTC)"},"2639":{"id":"/reference/cli/lep_log-19","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS.123456\nExample: 2024/12/25 13:10:01.123456"},"2640":{"id":"/reference/cli/lep_log-20","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"Alternate Format: YYYY-MM-DD HH:MM:SS.123456\nExample: 2024-12-25 13:10:01.123456"},"2641":{"id":"/reference/cli/lep_log-21","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day in UTC)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:01 (1:10:01 AM of the current day)\ntoday 01:10:01.123456 (1:10:01 AM with microseconds precision)"},"2642":{"id":"/reference/cli/lep_log-22","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"yesterday or yd:\nExample Variations:\nyesterday (defaults to midnight of the previous day in UTC)\nyesterday 13 (1 PM of the previous day)\nyesterday 13:10 (1:10 PM of the previous day)\nyesterday 13:10:05 (1:10:05 PM of the previous day)\nyesterday 13:10:01.123456 (1:10:01 PM with microseconds precision on the previous day)"},"2643":{"id":"/reference/cli/lep_log-23","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--end TEXT : The end time in ISO format.\nPlease note that all times must be in UTC.\nKeywords such as “now,” “today,” and “yesterday” will be interpreted as UTC timestamps.\nFor example, “now” corresponds to datetime.now(timezone.utc)."},"2644":{"id":"/reference/cli/lep_log-24","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"now\nExample: now (indicates the current time in UTC)"},"2645":{"id":"/reference/cli/lep_log-25","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS.123456\nExample: 2024/12/25 13:10:01.123456"},"2646":{"id":"/reference/cli/lep_log-26","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"Alternate Format: YYYY-MM-DD HH:MM:SS.123456\nExample: 2024-12-25 13:10:01.123456"},"2647":{"id":"/reference/cli/lep_log-27","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day in UTC)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:01 (1:10:01 AM of the current day)\ntoday 01:10:01.123456 (1:10:01 AM with microseconds precision)"},"2648":{"id":"/reference/cli/lep_log-28","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"yesterday or yd:\nExample Variations:\nyesterday (defaults to midnight of the previous day in UTC)\nyesterday 13 (1 PM of the previous day)\nyesterday 13:10 (1:10 PM of the previous day)\nyesterday 13:10:05 (1:10:05 PM of the previous day)\nyesterday 13:10:01.123456 (1:10:01 PM with microseconds precision on the previous day)"},"2649":{"id":"/reference/cli/lep_log-29","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--limit INTEGER : The maximum number of result lines to return.(default: 5000) [default: 5000]"},"2650":{"id":"/reference/cli/lep_log-30","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--path PATH : Local directory path to save the log TXT files."},"2651":{"id":"/reference/cli/lep_log-31","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--query TEXT : Specify the query string"},"2652":{"id":"/reference/cli/lep_log-32","page_id":"/reference/cli/lep_log","tags":[],"type":"text","url":"/reference/cli/lep_log#options-1","content":"--help : Show this message and exit."},"2653":{"id":"/reference/cli/lep_login","page_id":"/reference/cli/lep_login","type":"page","content":"lep login","tags":[],"url":"/reference/cli/lep_login"},"2654":{"id":"/reference/cli/lep_login-0","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login","content":"Login to the DGX Cloud Lepton. This will open a browser window to the Lepton login page if credentials are not given. You will be redirected to a page with the credentials string. Copy the string and paste it into the terminal, and you will be logged in."},"2655":{"id":"/reference/cli/lep_login-1","page_id":"/reference/cli/lep_login","type":"heading","tags":[],"url":"/reference/cli/lep_login#usage","content":"Usage"},"2656":{"id":"/reference/cli/lep_login-2","page_id":"/reference/cli/lep_login","type":"heading","tags":[],"url":"/reference/cli/lep_login#options","content":"Options"},"2657":{"id":"/reference/cli/lep_login-3","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login","content":"Login to the DGX Cloud Lepton. This will open a browser window to the Lepton\nlogin page if credentials are not given. You will be redirected to a page with\nthe credentials string. Copy the string and paste it into the terminal, and\nyou will be logged in."},"2658":{"id":"/reference/cli/lep_login-4","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login#usage","content":"lep login [OPTIONS]"},"2659":{"id":"/reference/cli/lep_login-5","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login#options","content":"-c,  --credentials TEXT : The credentials of the lepton login info."},"2660":{"id":"/reference/cli/lep_login-6","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login#options","content":"-u,  --workspace-url TEXT : The url of the workspace to login to."},"2661":{"id":"/reference/cli/lep_login-7","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login#options","content":"-l,  --lepton-classic : Login to the classic Lepton AI workspace."},"2662":{"id":"/reference/cli/lep_login-8","page_id":"/reference/cli/lep_login","tags":[],"type":"text","url":"/reference/cli/lep_login#options","content":"--help : Show this message and exit."},"2663":{"id":"/reference/cli/lep_logout","page_id":"/reference/cli/lep_logout","type":"page","content":"lep logout","tags":[],"url":"/reference/cli/lep_logout"},"2664":{"id":"/reference/cli/lep_logout-0","page_id":"/reference/cli/lep_logout","tags":[],"type":"text","url":"/reference/cli/lep_logout","content":"Logout of the DGX Cloud Lepton."},"2665":{"id":"/reference/cli/lep_logout-1","page_id":"/reference/cli/lep_logout","type":"heading","tags":[],"url":"/reference/cli/lep_logout#usage","content":"Usage"},"2666":{"id":"/reference/cli/lep_logout-2","page_id":"/reference/cli/lep_logout","type":"heading","tags":[],"url":"/reference/cli/lep_logout#options","content":"Options"},"2667":{"id":"/reference/cli/lep_logout-3","page_id":"/reference/cli/lep_logout","tags":[],"type":"text","url":"/reference/cli/lep_logout","content":"Logout of the DGX Cloud Lepton."},"2668":{"id":"/reference/cli/lep_logout-4","page_id":"/reference/cli/lep_logout","tags":[],"type":"text","url":"/reference/cli/lep_logout#usage","content":"lep logout [OPTIONS]"},"2669":{"id":"/reference/cli/lep_logout-5","page_id":"/reference/cli/lep_logout","tags":[],"type":"text","url":"/reference/cli/lep_logout#options","content":"--purge : Purge the credentials of the lepton login info."},"2670":{"id":"/reference/cli/lep_logout-6","page_id":"/reference/cli/lep_logout","tags":[],"type":"text","url":"/reference/cli/lep_logout#options","content":"--help : Show this message and exit."},"2671":{"id":"/reference/cli/lep_node","page_id":"/reference/cli/lep_node","type":"page","content":"lep node","tags":[],"url":"/reference/cli/lep_node"},"2672":{"id":"/reference/cli/lep_node-0","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node","content":"Manage nodes on the DGX Cloud Lepton."},"2673":{"id":"/reference/cli/lep_node-1","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#usage","content":"Usage"},"2674":{"id":"/reference/cli/lep_node-2","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#options","content":"Options"},"2675":{"id":"/reference/cli/lep_node-3","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#commands","content":"Commands"},"2676":{"id":"/reference/cli/lep_node-4","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#lep-node-list","content":"lep node list"},"2677":{"id":"/reference/cli/lep_node-5","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#usage-1","content":"Usage"},"2678":{"id":"/reference/cli/lep_node-6","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#options-1","content":"Options"},"2679":{"id":"/reference/cli/lep_node-7","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#lep-node-resource-shape","content":"lep node resource-shape"},"2680":{"id":"/reference/cli/lep_node-8","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#usage-2","content":"Usage"},"2681":{"id":"/reference/cli/lep_node-9","page_id":"/reference/cli/lep_node","type":"heading","tags":[],"url":"/reference/cli/lep_node#options-2","content":"Options"},"2682":{"id":"/reference/cli/lep_node-10","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node","content":"Manage nodes on the DGX Cloud Lepton."},"2683":{"id":"/reference/cli/lep_node-11","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#usage","content":"lep node [OPTIONS] COMMAND [ARGS]..."},"2684":{"id":"/reference/cli/lep_node-12","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options","content":"--help : Show this message and exit."},"2685":{"id":"/reference/cli/lep_node-13","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#commands","content":"list : Lists all node groups in the current workspace."},"2686":{"id":"/reference/cli/lep_node-14","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#commands","content":"resource-shape : List resource shapes per node group."},"2687":{"id":"/reference/cli/lep_node-15","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#lep-node-list","content":"Lists all node groups in the current workspace."},"2688":{"id":"/reference/cli/lep_node-16","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#lep-node-list","content":"If the --detail or -d option is provided, additional information about each\nnode will be displayed."},"2689":{"id":"/reference/cli/lep_node-17","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#usage-1","content":"lep node list [OPTIONS]"},"2690":{"id":"/reference/cli/lep_node-18","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options-1","content":"-d,  --detail : Show all the nodes"},"2691":{"id":"/reference/cli/lep_node-19","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options-1","content":"-ng,  --node-group TEXT : Show all the nodes in specific node groups by their IDs or names. Can use partial name/ID (e.g., 'h100' will match any name/ID containing 'h100'). Can specify multiple values."},"2692":{"id":"/reference/cli/lep_node-20","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options-1","content":"--help : Show this message and exit."},"2693":{"id":"/reference/cli/lep_node-21","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#lep-node-resource-shape","content":"List resource shapes per node group."},"2694":{"id":"/reference/cli/lep_node-22","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#lep-node-resource-shape","content":"Columns: - Node Group: name on first line, id on second line - Shapes:\none shape per block, first line is name, second line shows colored\navailability for pod/endpoint/job - Detailed: description and colored\nresource details"},"2695":{"id":"/reference/cli/lep_node-23","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#usage-2","content":"lep node resource-shape [OPTIONS]"},"2696":{"id":"/reference/cli/lep_node-24","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options-2","content":"-ng,  --node-group TEXT : Show resource shapes for specific node groups by their IDs or names. Can use partial name/ID (e.g., 'h100' will match any name/ID containing 'h100'). Can specify multiple values."},"2697":{"id":"/reference/cli/lep_node-25","page_id":"/reference/cli/lep_node","tags":[],"type":"text","url":"/reference/cli/lep_node#options-2","content":"--help : Show this message and exit."},"2698":{"id":"/reference/cli/lep_pod","page_id":"/reference/cli/lep_pod","type":"page","content":"lep pod","tags":[],"url":"/reference/cli/lep_pod"},"2699":{"id":"/reference/cli/lep_pod-0","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod","content":"Manages pods on the DGX Cloud Lepton."},"2700":{"id":"/reference/cli/lep_pod-1","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage","content":"Usage"},"2701":{"id":"/reference/cli/lep_pod-2","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options","content":"Options"},"2702":{"id":"/reference/cli/lep_pod-3","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#commands","content":"Commands"},"2703":{"id":"/reference/cli/lep_pod-4","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-create","content":"lep pod create"},"2704":{"id":"/reference/cli/lep_pod-5","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-1","content":"Usage"},"2705":{"id":"/reference/cli/lep_pod-6","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-1","content":"Options"},"2706":{"id":"/reference/cli/lep_pod-7","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-get","content":"lep pod get"},"2707":{"id":"/reference/cli/lep_pod-8","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-2","content":"Usage"},"2708":{"id":"/reference/cli/lep_pod-9","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-2","content":"Options"},"2709":{"id":"/reference/cli/lep_pod-10","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-list","content":"lep pod list"},"2710":{"id":"/reference/cli/lep_pod-11","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-3","content":"Usage"},"2711":{"id":"/reference/cli/lep_pod-12","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-3","content":"Options"},"2712":{"id":"/reference/cli/lep_pod-13","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-remove","content":"lep pod remove"},"2713":{"id":"/reference/cli/lep_pod-14","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-4","content":"Usage"},"2714":{"id":"/reference/cli/lep_pod-15","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-4","content":"Options"},"2715":{"id":"/reference/cli/lep_pod-16","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-ssh","content":"lep pod ssh"},"2716":{"id":"/reference/cli/lep_pod-17","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-5","content":"Usage"},"2717":{"id":"/reference/cli/lep_pod-18","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-5","content":"Options"},"2718":{"id":"/reference/cli/lep_pod-19","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#lep-pod-stop","content":"lep pod stop"},"2719":{"id":"/reference/cli/lep_pod-20","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#usage-6","content":"Usage"},"2720":{"id":"/reference/cli/lep_pod-21","page_id":"/reference/cli/lep_pod","type":"heading","tags":[],"url":"/reference/cli/lep_pod#options-6","content":"Options"},"2721":{"id":"/reference/cli/lep_pod-22","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod","content":"Manages pods on the DGX Cloud Lepton."},"2722":{"id":"/reference/cli/lep_pod-23","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage","content":"lep pod [OPTIONS] COMMAND [ARGS]..."},"2723":{"id":"/reference/cli/lep_pod-24","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options","content":"--help : Show this message and exit."},"2724":{"id":"/reference/cli/lep_pod-25","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"create : Creates a pod with the given resource shape, mount, env and..."},"2725":{"id":"/reference/cli/lep_pod-26","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"get : Show pod detail and optionally save its spec JSON."},"2726":{"id":"/reference/cli/lep_pod-27","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"list : Lists all pods in the current workspace."},"2727":{"id":"/reference/cli/lep_pod-28","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"remove : Removes a pod."},"2728":{"id":"/reference/cli/lep_pod-29","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"ssh : SSH into a running pod."},"2729":{"id":"/reference/cli/lep_pod-30","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#commands","content":"stop : Stops a pod by its name."},"2730":{"id":"/reference/cli/lep_pod-31","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-create","content":"Creates a pod with the given resource shape, mount, env and secret."},"2731":{"id":"/reference/cli/lep_pod-32","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-1","content":"lep pod create [OPTIONS]"},"2732":{"id":"/reference/cli/lep_pod-33","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-n,  --name TEXT : Name of the pod to create. [required]"},"2733":{"id":"/reference/cli/lep_pod-34","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-t,  --template TEXT : Template ID to render the pod specification from."},"2734":{"id":"/reference/cli/lep_pod-35","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--run TEXT : Command string (\"run\") to substitute into the template."},"2735":{"id":"/reference/cli/lep_pod-36","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-f,  --file FILE : If provided, load the pod spec from this JSON file before applying CLI overrides. The file could be generated by UI → CLI → 'Use spec file', or by running: pod get -i <pod_id> --path <download_path>."},"2736":{"id":"/reference/cli/lep_pod-37","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."},"2737":{"id":"/reference/cli/lep_pod-38","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--mount TEXT : Persistent storage to be mounted to the deployment, in the format STORAGE_PATH:MOUNT_PATH."},"2738":{"id":"/reference/cli/lep_pod-39","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-e,  --env TEXT : Environment variables to pass to the deployment, in the format NAME=VALUE."},"2739":{"id":"/reference/cli/lep_pod-40","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-s,  --secret TEXT : Secrets to pass to the deployment, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."},"2740":{"id":"/reference/cli/lep_pod-41","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--image-pull-secrets TEXT : Secrets to use for pulling images."},"2741":{"id":"/reference/cli/lep_pod-42","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-ng,  --node-group TEXT : Node group for the pod. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."},"2742":{"id":"/reference/cli/lep_pod-43","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--container-image TEXT : Container image to run."},"2743":{"id":"/reference/cli/lep_pod-44","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--container-command TEXT : Command to run in the container."},"2744":{"id":"/reference/cli/lep_pod-45","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."},"2745":{"id":"/reference/cli/lep_pod-46","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-ni,  --node-id TEXT : Node for the pod. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"},"2746":{"id":"/reference/cli/lep_pod-47","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-qp,  --queue-priority TEXT : Set the priority for this pod (dedicated node groups only)."},"2747":{"id":"/reference/cli/lep_pod-48","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-cbp,  --can-be-preempted : Allow this pod to be preempted by higher priority workloads."},"2748":{"id":"/reference/cli/lep_pod-49","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"-cp,  --can-preempt : Allow this pod to preempt lower priority workloads."},"2749":{"id":"/reference/cli/lep_pod-50","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--container-port TEXT : Container ports to expose. Format: [port]:[protocol]:[strategy][:strategy].\n[port] : 1-65535\n[protocol] : tcp | udp | sctp\n[strategy] : proxy | hostmap - hostmap: host port (random 40000-65535) mapped on node IP - proxy : generate public URL; only ONE port can enable proxy"},"2750":{"id":"/reference/cli/lep_pod-51","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"Examples:\n8080:tcp:proxy -> proxy only\n8080:udp:hostmap -> host mapping only\n8080:tcp:proxy:hostmap -> both strategies (note: only first proxy will take effect)"},"2751":{"id":"/reference/cli/lep_pod-52","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"Notice: Exposing container ports may increase your service's security risk. Please implement appropriate authentication and security controls; you are solely responsible for the security of any services exposed."},"2752":{"id":"/reference/cli/lep_pod-53","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--with-reservation TEXT : Assign the pod to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups)."},"2753":{"id":"/reference/cli/lep_pod-54","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--allow-burst-to-other-reservation : If set, the pod can temporarily use free resources from nodes reserved by other reservations. Be aware that when a new workload bound to those reservations starts, your pod may be evicted."},"2754":{"id":"/reference/cli/lep_pod-55","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--privileged : Run the pod in privileged mode."},"2755":{"id":"/reference/cli/lep_pod-56","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-1","content":"--help : Show this message and exit."},"2756":{"id":"/reference/cli/lep_pod-57","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-get","content":"Show pod detail and optionally save its spec JSON."},"2757":{"id":"/reference/cli/lep_pod-58","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-2","content":"lep pod get [OPTIONS]"},"2758":{"id":"/reference/cli/lep_pod-59","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-2","content":"-n,  --name TEXT : Pod name [required]"},"2759":{"id":"/reference/cli/lep_pod-60","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-2","content":"-p,  --path PATH : Optional local path to save the pod spec JSON. Directory or full filename accepted.\nIf a directory is provided, the file will be saved as pod-spec-[name].json."},"2760":{"id":"/reference/cli/lep_pod-61","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-2","content":"--help : Show this message and exit."},"2761":{"id":"/reference/cli/lep_pod-62","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-list","content":"Lists all pods in the current workspace."},"2762":{"id":"/reference/cli/lep_pod-63","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-3","content":"lep pod list [OPTIONS]"},"2763":{"id":"/reference/cli/lep_pod-64","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-3","content":"-p,  --pattern TEXT : Regular expression pattern to filter pod names."},"2764":{"id":"/reference/cli/lep_pod-65","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-3","content":"-d,  --detail : Show SSH/TCP/JupyterLab columns in the table."},"2765":{"id":"/reference/cli/lep_pod-66","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-3","content":"--help : Show this message and exit."},"2766":{"id":"/reference/cli/lep_pod-67","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-remove","content":"Removes a pod."},"2767":{"id":"/reference/cli/lep_pod-68","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-4","content":"lep pod remove [OPTIONS]"},"2768":{"id":"/reference/cli/lep_pod-69","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-4","content":"-n,  --name TEXT : The pod name to remove. [required]"},"2769":{"id":"/reference/cli/lep_pod-70","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-4","content":"--help : Show this message and exit."},"2770":{"id":"/reference/cli/lep_pod-71","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-ssh","content":"SSH into a running pod."},"2771":{"id":"/reference/cli/lep_pod-72","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-5","content":"lep pod ssh [OPTIONS]"},"2772":{"id":"/reference/cli/lep_pod-73","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-5","content":"-n,  --name TEXT : The pod name to ssh. [required]"},"2773":{"id":"/reference/cli/lep_pod-74","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-5","content":"--help : Show this message and exit."},"2774":{"id":"/reference/cli/lep_pod-75","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#lep-pod-stop","content":"Stops a pod by its name."},"2775":{"id":"/reference/cli/lep_pod-76","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#usage-6","content":"lep pod stop [OPTIONS]"},"2776":{"id":"/reference/cli/lep_pod-77","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-6","content":"-n,  --name TEXT : The endpoint name to stop. [required]"},"2777":{"id":"/reference/cli/lep_pod-78","page_id":"/reference/cli/lep_pod","tags":[],"type":"text","url":"/reference/cli/lep_pod#options-6","content":"--help : Show this message and exit."},"2778":{"id":"/reference/cli/lep_raycluster/page","page_id":"/reference/cli/lep_raycluster/page","type":"page","content":"lep raycluster","tags":[],"url":"/reference/cli/lep_raycluster/page"},"2779":{"id":"/reference/cli/lep_raycluster/page-0","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page","content":"Manage Ray clusters on DGX Cloud Lepton."},"2780":{"id":"/reference/cli/lep_raycluster/page-1","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage","content":"Usage"},"2781":{"id":"/reference/cli/lep_raycluster/page-2","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options","content":"Options"},"2782":{"id":"/reference/cli/lep_raycluster/page-3","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#commands","content":"Commands"},"2783":{"id":"/reference/cli/lep_raycluster/page-4","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-list","content":"lep raycluster list"},"2784":{"id":"/reference/cli/lep_raycluster/page-5","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-1","content":"Usage"},"2785":{"id":"/reference/cli/lep_raycluster/page-6","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-1","content":"Options"},"2786":{"id":"/reference/cli/lep_raycluster/page-7","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-create","content":"lep raycluster create"},"2787":{"id":"/reference/cli/lep_raycluster/page-8","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-2","content":"Usage"},"2788":{"id":"/reference/cli/lep_raycluster/page-9","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-2","content":"Options"},"2789":{"id":"/reference/cli/lep_raycluster/page-10","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-remove","content":"lep raycluster remove"},"2790":{"id":"/reference/cli/lep_raycluster/page-11","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-3","content":"Usage"},"2791":{"id":"/reference/cli/lep_raycluster/page-12","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-3","content":"Options"},"2792":{"id":"/reference/cli/lep_raycluster/page-13","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-get","content":"lep raycluster get"},"2793":{"id":"/reference/cli/lep_raycluster/page-14","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-4","content":"Usage"},"2794":{"id":"/reference/cli/lep_raycluster/page-15","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-4","content":"Options"},"2795":{"id":"/reference/cli/lep_raycluster/page-16","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-update","content":"lep raycluster update"},"2796":{"id":"/reference/cli/lep_raycluster/page-17","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-5","content":"Usage"},"2797":{"id":"/reference/cli/lep_raycluster/page-18","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-5","content":"Options"},"2798":{"id":"/reference/cli/lep_raycluster/page-19","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#lep-raycluster-submit","content":"lep raycluster submit"},"2799":{"id":"/reference/cli/lep_raycluster/page-20","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#usage-6","content":"Usage"},"2800":{"id":"/reference/cli/lep_raycluster/page-21","page_id":"/reference/cli/lep_raycluster/page","type":"heading","tags":[],"url":"/reference/cli/lep_raycluster/page#options-6","content":"Options"},"2801":{"id":"/reference/cli/lep_raycluster/page-22","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page","content":"Manage Ray clusters on DGX Cloud Lepton."},"2802":{"id":"/reference/cli/lep_raycluster/page-23","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage","content":"lep raycluster [OPTIONS] COMMAND [ARGS]..."},"2803":{"id":"/reference/cli/lep_raycluster/page-24","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options","content":"--help : Show this message and exit."},"2804":{"id":"/reference/cli/lep_raycluster/page-25","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"create : Creates a Ray cluster from either a spec file (spec only) or..."},"2805":{"id":"/reference/cli/lep_raycluster/page-26","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"get : Shows Ray cluster detail and optionally saves its spec JSON."},"2806":{"id":"/reference/cli/lep_raycluster/page-27","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"list : Lists all Ray clusters in the current workspace."},"2807":{"id":"/reference/cli/lep_raycluster/page-28","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"remove : Removes a Ray cluster."},"2808":{"id":"/reference/cli/lep_raycluster/page-29","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"submit : Submits a job to a Ray cluster."},"2809":{"id":"/reference/cli/lep_raycluster/page-30","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#commands","content":"update : Updates a Ray cluster worker group's min replicas."},"2810":{"id":"/reference/cli/lep_raycluster/page-31","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-list","content":"Lists all Ray clusters in the current workspace."},"2811":{"id":"/reference/cli/lep_raycluster/page-32","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-1","content":"lep raycluster list [OPTIONS]"},"2812":{"id":"/reference/cli/lep_raycluster/page-33","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-1","content":"-n,  --name TEXT : Filter rayclusters by name (case-insensitive substring). Can be specified multiple times."},"2813":{"id":"/reference/cli/lep_raycluster/page-34","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-1","content":"--help : Show this message and exit."},"2814":{"id":"/reference/cli/lep_raycluster/page-35","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-create","content":"Creates a Ray cluster from either a spec file (spec only) or CLI flags. If\nboth are provided, CLI flags override values from the file."},"2815":{"id":"/reference/cli/lep_raycluster/page-36","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-2","content":"lep raycluster create [OPTIONS]"},"2816":{"id":"/reference/cli/lep_raycluster/page-37","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-n,  --name TEXT : Name of the Ray cluster. [required]"},"2817":{"id":"/reference/cli/lep_raycluster/page-38","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-f,  --file FILE : If specified, load the Ray cluster spec from this JSON file before applying additional CLI overrides. The file should contain a LeptonRayClusterUserSpec JSON produced by lep raycluster get -p (spec only)."},"2818":{"id":"/reference/cli/lep_raycluster/page-39","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--image TEXT : Ray cluster container image. Default: ray:2.48.0-py312-gpu"},"2819":{"id":"/reference/cli/lep_raycluster/page-40","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--image-pull-secrets TEXT : Secrets to use for pulling images."},"2820":{"id":"/reference/cli/lep_raycluster/page-41","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--ray-version TEXT : Ray version to use."},"2821":{"id":"/reference/cli/lep_raycluster/page-42","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-resource-shape TEXT : Resource shape for the head node group."},"2822":{"id":"/reference/cli/lep_raycluster/page-43","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-shared-memory-size INTEGER : Shared memory size for the head node group, in MiB."},"2823":{"id":"/reference/cli/lep_raycluster/page-44","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-mount TEXT : Persistent storage to be mounted to the head group, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."},"2824":{"id":"/reference/cli/lep_raycluster/page-45","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-he,  --head-env TEXT : Environment variables for the head group, in the format NAME=VALUE."},"2825":{"id":"/reference/cli/lep_raycluster/page-46","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-hs,  --head-secret TEXT : Secrets for the head group, in the format NAME=SECRET_NAME. If secret name equals the environment variable name, you can just pass SECRET_NAME."},"2826":{"id":"/reference/cli/lep_raycluster/page-47","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-node-group TEXT : Dedicated node group(s) for the head node group (affinity). Only the first may be applied depending on backend support."},"2827":{"id":"/reference/cli/lep_raycluster/page-48","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-allowed-nodes TEXT : Comma-separated node names within the specified head dedicated node group (affinity)."},"2828":{"id":"/reference/cli/lep_raycluster/page-49","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-reservation TEXT : Reservation ID for the head node group."},"2829":{"id":"/reference/cli/lep_raycluster/page-50","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--head-allow-burst-to-other-reservation BOOLEAN : Allow the head node group to burst to other reservations."},"2830":{"id":"/reference/cli/lep_raycluster/page-51","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-group-name TEXT : Name of the worker group (if specifying via flags)."},"2831":{"id":"/reference/cli/lep_raycluster/page-52","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-resource-shape TEXT : Resource shape for the worker node group."},"2832":{"id":"/reference/cli/lep_raycluster/page-53","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-shared-memory-size INTEGER : Shared memory size for the worker node group, in MiB."},"2833":{"id":"/reference/cli/lep_raycluster/page-54","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-mount TEXT : Persistent storage to be mounted to the worker group, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."},"2834":{"id":"/reference/cli/lep_raycluster/page-55","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-we,  --worker-env TEXT : Environment variables for the worker group, in the format NAME=VALUE."},"2835":{"id":"/reference/cli/lep_raycluster/page-56","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"-ws,  --worker-secret TEXT : Secrets for the worker group, in the format NAME=SECRET_NAME. If secret name equals the environment variable name, you can just pass SECRET_NAME."},"2836":{"id":"/reference/cli/lep_raycluster/page-57","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-node-group TEXT : Dedicated node group(s) for the worker node group (affinity). Only the first may be applied depending on backend support."},"2837":{"id":"/reference/cli/lep_raycluster/page-58","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-allowed-nodes TEXT : Comma-separated node names within the specified worker dedicated node group (affinity)."},"2838":{"id":"/reference/cli/lep_raycluster/page-59","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-reservation TEXT : Reservation ID for the worker node group."},"2839":{"id":"/reference/cli/lep_raycluster/page-60","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-allow-burst-to-other-reservation BOOLEAN : Allow the worker node group to burst to other reservations."},"2840":{"id":"/reference/cli/lep_raycluster/page-61","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-min-replicas INTEGER : Minimum replicas for the worker node group. Default: 1"},"2841":{"id":"/reference/cli/lep_raycluster/page-62","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--worker-max-replicas INTEGER : Maximum replicas for the worker node group."},"2842":{"id":"/reference/cli/lep_raycluster/page-63","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--enable-autoscaler BOOLEAN : Enable the Ray autoscaler."},"2843":{"id":"/reference/cli/lep_raycluster/page-64","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--autoscaler-worker-idle-timeout INTEGER : Timeout for worker idle timeout in seconds."},"2844":{"id":"/reference/cli/lep_raycluster/page-65","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--visibility TEXT : Visibility of the Ray cluster. Can be 'public' or 'private'. If private, the cluster will only be viewable by the creator and workspace admin."},"2845":{"id":"/reference/cli/lep_raycluster/page-66","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-2","content":"--help : Show this message and exit."},"2846":{"id":"/reference/cli/lep_raycluster/page-67","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-remove","content":"Removes a Ray cluster."},"2847":{"id":"/reference/cli/lep_raycluster/page-68","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-3","content":"lep raycluster remove [OPTIONS]"},"2848":{"id":"/reference/cli/lep_raycluster/page-69","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-3","content":"-n,  --name TEXT : The raycluster name to remove. [required]"},"2849":{"id":"/reference/cli/lep_raycluster/page-70","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-3","content":"--help : Show this message and exit."},"2850":{"id":"/reference/cli/lep_raycluster/page-71","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-get","content":"Shows Ray cluster detail and optionally saves its spec JSON."},"2851":{"id":"/reference/cli/lep_raycluster/page-72","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-4","content":"lep raycluster get [OPTIONS]"},"2852":{"id":"/reference/cli/lep_raycluster/page-73","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-4","content":"-n,  --name TEXT : The raycluster name to get. [required]"},"2853":{"id":"/reference/cli/lep_raycluster/page-74","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-4","content":"-d,  --detail : Show the full raycluster detail."},"2854":{"id":"/reference/cli/lep_raycluster/page-75","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-4","content":"-p,  --path PATH : Optional local path to save the raycluster spec JSON. Directory or full filename accepted. If a directory is provided, the file will be saved as raycluster-spec-[name].json."},"2855":{"id":"/reference/cli/lep_raycluster/page-76","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-4","content":"--help : Show this message and exit."},"2856":{"id":"/reference/cli/lep_raycluster/page-77","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-update","content":"Updates a Ray cluster worker group's min replicas. The worker group name is\ninferred from the existing cluster when there is exactly one worker group."},"2857":{"id":"/reference/cli/lep_raycluster/page-78","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-5","content":"lep raycluster update [OPTIONS]"},"2858":{"id":"/reference/cli/lep_raycluster/page-79","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-5","content":"-n,  --name TEXT : The raycluster name to update. [required]"},"2859":{"id":"/reference/cli/lep_raycluster/page-80","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-5","content":"--min-replicas INTEGER : New minimum replicas for the worker group. [required]"},"2860":{"id":"/reference/cli/lep_raycluster/page-81","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-5","content":"--help : Show this message and exit."},"2861":{"id":"/reference/cli/lep_raycluster/page-82","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-submit","content":"Submits a job to a Ray cluster."},"2862":{"id":"/reference/cli/lep_raycluster/page-83","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#lep-raycluster-submit","content":"Usage: lep raycluster submit -n [cluster] -- [entrypoint command] Everything\nafter \"--\" is treated as the entrypoint command, just like native Ray."},"2863":{"id":"/reference/cli/lep_raycluster/page-84","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#usage-6","content":"lep raycluster submit [OPTIONS] [ENTRYPOINT]..."},"2864":{"id":"/reference/cli/lep_raycluster/page-85","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"-n,  --name TEXT : The raycluster name to submit a job to. [required]"},"2865":{"id":"/reference/cli/lep_raycluster/page-86","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--submission-id TEXT : Submission ID to specify for the job."},"2866":{"id":"/reference/cli/lep_raycluster/page-87","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--runtime-env FILE : Path to a YAML file containing a runtime_env definition."},"2867":{"id":"/reference/cli/lep_raycluster/page-88","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--runtime-env-json TEXT : JSON-serialized runtime_env dictionary."},"2868":{"id":"/reference/cli/lep_raycluster/page-89","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--working-dir TEXT : Directory or remote URI (.zip) for working_dir. If specified, overrides the option in --runtime-env."},"2869":{"id":"/reference/cli/lep_raycluster/page-90","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--metadata-json TEXT : JSON-serialized dictionary of metadata to attach to the job."},"2870":{"id":"/reference/cli/lep_raycluster/page-91","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--entrypoint-num-cpus FLOAT : CPU cores to reserve for entrypoint."},"2871":{"id":"/reference/cli/lep_raycluster/page-92","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--entrypoint-num-gpus FLOAT : GPUs to reserve for entrypoint."},"2872":{"id":"/reference/cli/lep_raycluster/page-93","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--entrypoint-memory INTEGER : Memory (bytes) to reserve for entrypoint."},"2873":{"id":"/reference/cli/lep_raycluster/page-94","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--entrypoint-resources TEXT : JSON-serialized dict of custom resources to reserve for entrypoint."},"2874":{"id":"/reference/cli/lep_raycluster/page-95","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--no-wait : Do not stream logs or wait for job completion."},"2875":{"id":"/reference/cli/lep_raycluster/page-96","page_id":"/reference/cli/lep_raycluster/page","tags":[],"type":"text","url":"/reference/cli/lep_raycluster/page#options-6","content":"--help : Show this message and exit."},"2876":{"id":"/reference/cli/lep_secret","page_id":"/reference/cli/lep_secret","type":"page","content":"lep secret","tags":[],"url":"/reference/cli/lep_secret"},"2877":{"id":"/reference/cli/lep_secret-0","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret","content":"Manage secrets on the DGX Cloud Lepton."},"2878":{"id":"/reference/cli/lep_secret-1","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#usage","content":"Usage"},"2879":{"id":"/reference/cli/lep_secret-2","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#options","content":"Options"},"2880":{"id":"/reference/cli/lep_secret-3","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#commands","content":"Commands"},"2881":{"id":"/reference/cli/lep_secret-4","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#lep-secret-create","content":"lep secret create"},"2882":{"id":"/reference/cli/lep_secret-5","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#usage-1","content":"Usage"},"2883":{"id":"/reference/cli/lep_secret-6","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#options-1","content":"Options"},"2884":{"id":"/reference/cli/lep_secret-7","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#lep-secret-list","content":"lep secret list"},"2885":{"id":"/reference/cli/lep_secret-8","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#usage-2","content":"Usage"},"2886":{"id":"/reference/cli/lep_secret-9","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#options-2","content":"Options"},"2887":{"id":"/reference/cli/lep_secret-10","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#lep-secret-remove","content":"lep secret remove"},"2888":{"id":"/reference/cli/lep_secret-11","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#usage-3","content":"Usage"},"2889":{"id":"/reference/cli/lep_secret-12","page_id":"/reference/cli/lep_secret","type":"heading","tags":[],"url":"/reference/cli/lep_secret#options-3","content":"Options"},"2890":{"id":"/reference/cli/lep_secret-13","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret","content":"Manage secrets on the DGX Cloud Lepton."},"2891":{"id":"/reference/cli/lep_secret-14","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#usage","content":"lep secret [OPTIONS] COMMAND [ARGS]..."},"2892":{"id":"/reference/cli/lep_secret-15","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options","content":"--help : Show this message and exit."},"2893":{"id":"/reference/cli/lep_secret-16","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#commands","content":"create : Creates secrets with the given name and value."},"2894":{"id":"/reference/cli/lep_secret-17","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#commands","content":"list : Lists all secrets in the current workspace."},"2895":{"id":"/reference/cli/lep_secret-18","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#commands","content":"remove : Removes the secret with the given name."},"2896":{"id":"/reference/cli/lep_secret-19","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#lep-secret-create","content":"Creates secrets with the given name and value. The name and value can be\nspecified multiple times to create multiple secrets, e.g.: lep secret\n  create -n SECRET1 -v VALUE1 -n SECRET2 -v VALUE2"},"2897":{"id":"/reference/cli/lep_secret-20","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#usage-1","content":"lep secret create [OPTIONS]"},"2898":{"id":"/reference/cli/lep_secret-21","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-1","content":"-n,  --name TEXT : Secret name"},"2899":{"id":"/reference/cli/lep_secret-22","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-1","content":"-v,  --value TEXT : Secret value"},"2900":{"id":"/reference/cli/lep_secret-23","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-1","content":"--public-key : Make the secret public (default is private)."},"2901":{"id":"/reference/cli/lep_secret-24","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-1","content":"--help : Show this message and exit."},"2902":{"id":"/reference/cli/lep_secret-25","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#lep-secret-list","content":"Lists all secrets in the current workspace. Note that the secret values are\nalways hidden."},"2903":{"id":"/reference/cli/lep_secret-26","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#usage-2","content":"lep secret list [OPTIONS]"},"2904":{"id":"/reference/cli/lep_secret-27","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-2","content":"--help : Show this message and exit."},"2905":{"id":"/reference/cli/lep_secret-28","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#lep-secret-remove","content":"Removes the secret with the given name."},"2906":{"id":"/reference/cli/lep_secret-29","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#usage-3","content":"lep secret remove [OPTIONS]"},"2907":{"id":"/reference/cli/lep_secret-30","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-3","content":"-n,  --name TEXT : Secret name [required]"},"2908":{"id":"/reference/cli/lep_secret-31","page_id":"/reference/cli/lep_secret","tags":[],"type":"text","url":"/reference/cli/lep_secret#options-3","content":"--help : Show this message and exit."},"2909":{"id":"/reference/cli/lep_template/page","page_id":"/reference/cli/lep_template/page","type":"page","content":"lep template","tags":[],"url":"/reference/cli/lep_template/page"},"2910":{"id":"/reference/cli/lep_template/page-0","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page","content":"Manage templates (list only)."},"2911":{"id":"/reference/cli/lep_template/page-1","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#usage","content":"Usage"},"2912":{"id":"/reference/cli/lep_template/page-2","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#options","content":"Options"},"2913":{"id":"/reference/cli/lep_template/page-3","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#commands","content":"Commands"},"2914":{"id":"/reference/cli/lep_template/page-4","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#lep-template-list","content":"lep template list"},"2915":{"id":"/reference/cli/lep_template/page-5","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#usage-1","content":"Usage"},"2916":{"id":"/reference/cli/lep_template/page-6","page_id":"/reference/cli/lep_template/page","type":"heading","tags":[],"url":"/reference/cli/lep_template/page#options-1","content":"Options"},"2917":{"id":"/reference/cli/lep_template/page-7","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page","content":"Manage templates (list only)."},"2918":{"id":"/reference/cli/lep_template/page-8","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#usage","content":"lep template [OPTIONS] COMMAND [ARGS]..."},"2919":{"id":"/reference/cli/lep_template/page-9","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#options","content":"--help : Show this message and exit."},"2920":{"id":"/reference/cli/lep_template/page-10","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#commands","content":"list : List all templates with Name/ID/Workload type."},"2921":{"id":"/reference/cli/lep_template/page-11","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#lep-template-list","content":"List all templates with Name/ID/Workload type."},"2922":{"id":"/reference/cli/lep_template/page-12","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#usage-1","content":"lep template list [OPTIONS]"},"2923":{"id":"/reference/cli/lep_template/page-13","page_id":"/reference/cli/lep_template/page","tags":[],"type":"text","url":"/reference/cli/lep_template/page#options-1","content":"--help : Show this message and exit."},"2924":{"id":"/reference/cli/lep_workspace","page_id":"/reference/cli/lep_workspace","type":"page","content":"lep workspace","tags":[],"url":"/reference/cli/lep_workspace"},"2925":{"id":"/reference/cli/lep_workspace-0","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace","content":"Manage workspace access to the DGX Cloud Lepton."},"2926":{"id":"/reference/cli/lep_workspace-1","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage","content":"Usage"},"2927":{"id":"/reference/cli/lep_workspace-2","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options","content":"Options"},"2928":{"id":"/reference/cli/lep_workspace-3","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#commands","content":"Commands"},"2929":{"id":"/reference/cli/lep_workspace-4","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-login","content":"lep workspace login"},"2930":{"id":"/reference/cli/lep_workspace-5","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-1","content":"Usage"},"2931":{"id":"/reference/cli/lep_workspace-6","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-1","content":"Options"},"2932":{"id":"/reference/cli/lep_workspace-7","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-logout","content":"lep workspace logout"},"2933":{"id":"/reference/cli/lep_workspace-8","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-2","content":"Usage"},"2934":{"id":"/reference/cli/lep_workspace-9","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-2","content":"Options"},"2935":{"id":"/reference/cli/lep_workspace-10","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-list","content":"lep workspace list"},"2936":{"id":"/reference/cli/lep_workspace-11","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-3","content":"Usage"},"2937":{"id":"/reference/cli/lep_workspace-12","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-3","content":"Options"},"2938":{"id":"/reference/cli/lep_workspace-13","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-remove","content":"lep workspace remove"},"2939":{"id":"/reference/cli/lep_workspace-14","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-4","content":"Usage"},"2940":{"id":"/reference/cli/lep_workspace-15","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-4","content":"Options"},"2941":{"id":"/reference/cli/lep_workspace-16","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-removeall","content":"lep workspace removeall"},"2942":{"id":"/reference/cli/lep_workspace-17","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-5","content":"Usage"},"2943":{"id":"/reference/cli/lep_workspace-18","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-5","content":"Options"},"2944":{"id":"/reference/cli/lep_workspace-19","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-id","content":"lep workspace id"},"2945":{"id":"/reference/cli/lep_workspace-20","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-6","content":"Usage"},"2946":{"id":"/reference/cli/lep_workspace-21","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-6","content":"Options"},"2947":{"id":"/reference/cli/lep_workspace-22","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-token","content":"lep workspace token"},"2948":{"id":"/reference/cli/lep_workspace-23","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-7","content":"Usage"},"2949":{"id":"/reference/cli/lep_workspace-24","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-7","content":"Options"},"2950":{"id":"/reference/cli/lep_workspace-25","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-url","content":"lep workspace url"},"2951":{"id":"/reference/cli/lep_workspace-26","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-8","content":"Usage"},"2952":{"id":"/reference/cli/lep_workspace-27","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-8","content":"Options"},"2953":{"id":"/reference/cli/lep_workspace-28","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#lep-workspace-status","content":"lep workspace status"},"2954":{"id":"/reference/cli/lep_workspace-29","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#usage-9","content":"Usage"},"2955":{"id":"/reference/cli/lep_workspace-30","page_id":"/reference/cli/lep_workspace","type":"heading","tags":[],"url":"/reference/cli/lep_workspace#options-9","content":"Options"},"2956":{"id":"/reference/cli/lep_workspace-31","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace","content":"Manage workspace access to the DGX Cloud Lepton."},"2957":{"id":"/reference/cli/lep_workspace-32","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage","content":"lep workspace [OPTIONS] COMMAND [ARGS]..."},"2958":{"id":"/reference/cli/lep_workspace-33","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options","content":"--help : Show this message and exit."},"2959":{"id":"/reference/cli/lep_workspace-34","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"id : Prints the id of the current workspace."},"2960":{"id":"/reference/cli/lep_workspace-35","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"list : List current workspaces and their urls on record."},"2961":{"id":"/reference/cli/lep_workspace-36","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"login : Logs in to a workspace."},"2962":{"id":"/reference/cli/lep_workspace-37","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"logout : Logout of the DGX Cloud Lepton."},"2963":{"id":"/reference/cli/lep_workspace-38","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"remove : Remove a workspace from the record."},"2964":{"id":"/reference/cli/lep_workspace-39","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"removeall : Remove all workspaces."},"2965":{"id":"/reference/cli/lep_workspace-40","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"status : Prints the status of the current workspace."},"2966":{"id":"/reference/cli/lep_workspace-41","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"token : Prints the authentication token of the current workspace."},"2967":{"id":"/reference/cli/lep_workspace-42","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#commands","content":"url : Prints the url of the current workspace."},"2968":{"id":"/reference/cli/lep_workspace-43","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-login","content":"Logs in to a workspace. This also verifies that the workspace is accessible."},"2969":{"id":"/reference/cli/lep_workspace-44","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-1","content":"lep workspace login [OPTIONS]"},"2970":{"id":"/reference/cli/lep_workspace-45","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-1","content":"-i,  --workspace-id TEXT : The workspace id to log in to."},"2971":{"id":"/reference/cli/lep_workspace-46","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-1","content":"-t,  --auth-token TEXT : Authentication token for the workspace."},"2972":{"id":"/reference/cli/lep_workspace-47","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-1","content":"--lepton-classic : Login to the classic Lepton AI workspace."},"2973":{"id":"/reference/cli/lep_workspace-48","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-1","content":"--help : Show this message and exit."},"2974":{"id":"/reference/cli/lep_workspace-49","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-logout","content":"Logout of the DGX Cloud Lepton."},"2975":{"id":"/reference/cli/lep_workspace-50","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-2","content":"lep workspace logout [OPTIONS]"},"2976":{"id":"/reference/cli/lep_workspace-51","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-2","content":"--purge : Purge the credentials of the lepton login info."},"2977":{"id":"/reference/cli/lep_workspace-52","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-2","content":"--help : Show this message and exit."},"2978":{"id":"/reference/cli/lep_workspace-53","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-list","content":"List current workspaces and their urls on record. For any workspace\ndisplayed in the list, you can log in to it by `lep workspace login -i"},"2979":{"id":"/reference/cli/lep_workspace-54","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-list","content":"[id]`."},"2980":{"id":"/reference/cli/lep_workspace-55","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-3","content":"lep workspace list [OPTIONS]"},"2981":{"id":"/reference/cli/lep_workspace-56","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-3","content":"--help : Show this message and exit."},"2982":{"id":"/reference/cli/lep_workspace-57","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-remove","content":"Remove a workspace from the record. After removal, the locally stored url\nand auth token will be deleted. If the workspace is currently logged in, you\nwill be logged out."},"2983":{"id":"/reference/cli/lep_workspace-58","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-4","content":"lep workspace remove [OPTIONS]"},"2984":{"id":"/reference/cli/lep_workspace-59","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-4","content":"-i,  --workspace-id TEXT : ID of the workspace [required]"},"2985":{"id":"/reference/cli/lep_workspace-60","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-4","content":"--help : Show this message and exit."},"2986":{"id":"/reference/cli/lep_workspace-61","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-removeall","content":"Remove all workspaces."},"2987":{"id":"/reference/cli/lep_workspace-62","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-5","content":"lep workspace removeall [OPTIONS]"},"2988":{"id":"/reference/cli/lep_workspace-63","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-5","content":"--help : Show this message and exit."},"2989":{"id":"/reference/cli/lep_workspace-64","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-id","content":"Prints the id of the current workspace. This is useful when you want to\nobtain the workspace id in the command line in e.g. a shell script, but do\nnot want to hardcode it in the source file."},"2990":{"id":"/reference/cli/lep_workspace-65","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-6","content":"lep workspace id [OPTIONS]"},"2991":{"id":"/reference/cli/lep_workspace-66","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-6","content":"--help : Show this message and exit."},"2992":{"id":"/reference/cli/lep_workspace-67","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-token","content":"Prints the authentication token of the current workspace. This is useful\nwhen you want to obtain the workspace token in the command line in e.g. a\nshell script, but do not want to hardcode it in the source file."},"2993":{"id":"/reference/cli/lep_workspace-68","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-7","content":"lep workspace token [OPTIONS]"},"2994":{"id":"/reference/cli/lep_workspace-69","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-7","content":"--help : Show this message and exit."},"2995":{"id":"/reference/cli/lep_workspace-70","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-url","content":"Prints the url of the current workspace. This is useful when you want to\nobtain the workspace url in the command line in e.g. a shell script, but do\nnot want to hardcode it in the source file."},"2996":{"id":"/reference/cli/lep_workspace-71","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-8","content":"lep workspace url [OPTIONS]"},"2997":{"id":"/reference/cli/lep_workspace-72","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-8","content":"--help : Show this message and exit."},"2998":{"id":"/reference/cli/lep_workspace-73","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#lep-workspace-status","content":"Prints the status of the current workspace."},"2999":{"id":"/reference/cli/lep_workspace-74","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#usage-9","content":"lep workspace status [OPTIONS]"},"3000":{"id":"/reference/cli/lep_workspace-75","page_id":"/reference/cli/lep_workspace","tags":[],"type":"text","url":"/reference/cli/lep_workspace#options-9","content":"--help : Show this message and exit."},"3001":{"id":"/reference/limits/workload","page_id":"/reference/limits/workload","type":"page","content":"Workload Limits","tags":[],"url":"/reference/limits/workload"},"3002":{"id":"/reference/limits/workload-0","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload","content":"Key limits for workloads on DGX Cloud Lepton."},"3003":{"id":"/reference/limits/workload-1","page_id":"/reference/limits/workload","type":"heading","tags":[],"url":"/reference/limits/workload#process-limits","content":"Process Limits"},"3004":{"id":"/reference/limits/workload-2","page_id":"/reference/limits/workload","type":"heading","tags":[],"url":"/reference/limits/workload#process-id-pid-limits","content":"Process ID (PID) Limits"},"3005":{"id":"/reference/limits/workload-3","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload","content":"A workload is a Dev Pod or a Batch Job on DGX Cloud Lepton."},"3006":{"id":"/reference/limits/workload-4","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload","content":"This page describes the limits and constraints that apply to workloads running on DGX Cloud Lepton.\nUnderstanding these limits helps you manage your workloads effectively and avoid potential issues."},"3007":{"id":"/reference/limits/workload-5","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"Each replica on DGX Cloud Lepton has a default PID limit that controls the maximum number of processes that can run within a single container."},"3008":{"id":"/reference/limits/workload-6","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"Resource"},"3009":{"id":"/reference/limits/workload-7","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"Default Limit"},"3010":{"id":"/reference/limits/workload-8","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"Description"},"3011":{"id":"/reference/limits/workload-9","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"PIDs per replica"},"3012":{"id":"/reference/limits/workload-10","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"32,768"},"3013":{"id":"/reference/limits/workload-11","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"Maximum number of processes allowed per pod"},"3014":{"id":"/reference/limits/workload-12","page_id":"/reference/limits/workload","tags":[],"type":"text","url":"/reference/limits/workload#process-id-pid-limits","content":"The PID limit helps prevent fork bombs and resource exhaustion. If your application requires more processes, consider splitting it into multiple replicas or optimizing your process usage."},"3015":{"id":"/reference/workload-identity/1st-workload-identity","page_id":"/reference/workload-identity/1st-workload-identity","type":"page","content":"Workload Overview","tags":[],"url":"/reference/workload-identity/1st-workload-identity"},"3016":{"id":"/reference/workload-identity/1st-workload-identity-0","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity","content":"Learn how workloads on DGX Cloud Lepton present themselves and how to federate them with your environment."},"3017":{"id":"/reference/workload-identity/1st-workload-identity-1","page_id":"/reference/workload-identity/1st-workload-identity","type":"heading","tags":[],"url":"/reference/workload-identity/1st-workload-identity#workload-identity","content":"Workload Identity"},"3018":{"id":"/reference/workload-identity/1st-workload-identity-2","page_id":"/reference/workload-identity/1st-workload-identity","type":"heading","tags":[],"url":"/reference/workload-identity/1st-workload-identity#token","content":"Token"},"3019":{"id":"/reference/workload-identity/1st-workload-identity-3","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#workload-identity","content":"As of the 0.42 release of DGX Cloud Lepton, all workloads (Dev Pods, Batch, Endpoints) project an identity token\ninto the workload containers. The projected token can be used to authenticate the workload to common services in major\ncloud providers (for example, AWS, GCP, Azure) through workload federation."},"3020":{"id":"/reference/workload-identity/1st-workload-identity-4","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#workload-identity","content":"The procedures for federating identity with each cloud vendor vary slightly and are described below. Most cloud vendors\nprovide Terraform or other automation methods for setting up the federation. Consult your cloud provider documentation\nfor detailed information."},"3021":{"id":"/reference/workload-identity/1st-workload-identity-5","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#workload-identity","content":"The key piece of information needed for any federation is the location of the public keys used by DGX Cloud Lepton to sign the\ntokens projected into workloads. This location is known as the OIDC Provider URL. The OIDC Provider URL can be found in\nthe DGX Cloud Lepton dashboard on the Nodes -> <NodeGroup> page."},"3022":{"id":"/reference/workload-identity/1st-workload-identity-6","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"Within the workload, the token is mapped to the file /var/run/secrets/kubernetes.io/serviceaccount/token and\n/run/secrets/kubernetes.io/serviceaccount/token. Workload applications can use this token for authentication."},"3023":{"id":"/reference/workload-identity/1st-workload-identity-7","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"An example token (decoded) looks like this:"},"3024":{"id":"/reference/workload-identity/1st-workload-identity-8","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"A couple of notable features in the token are worth pointing out:"},"3025":{"id":"/reference/workload-identity/1st-workload-identity-9","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"Currently, the only audience (aud) available is https://kubernetes.default.svc.\nAll federations should be configured to accept this audience as the allowed audience."},"3026":{"id":"/reference/workload-identity/1st-workload-identity-10","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"The Kubernetes namespace is always ws- followed by the DGX Cloud Lepton workspaceID."},"3027":{"id":"/reference/workload-identity/1st-workload-identity-11","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"The Kubernetes service account (SA) is always default."},"3028":{"id":"/reference/workload-identity/1st-workload-identity-12","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"The sub field is the identity utilized for federation."},"3029":{"id":"/reference/workload-identity/1st-workload-identity-13","page_id":"/reference/workload-identity/1st-workload-identity","tags":[],"type":"text","url":"/reference/workload-identity/1st-workload-identity#token","content":"Refer to the following sections for details on federating DGX Cloud Lepton with your cloud provider."},"3030":{"id":"/reference/workload-identity/AWS","page_id":"/reference/workload-identity/AWS","type":"page","content":"AWS Identity Federation","tags":[],"url":"/reference/workload-identity/AWS"},"3031":{"id":"/reference/workload-identity/AWS-0","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS","content":"Learn how to federate your DGX Cloud Lepton environment with Amazon Cloud."},"3032":{"id":"/reference/workload-identity/AWS-1","page_id":"/reference/workload-identity/AWS","type":"heading","tags":[],"url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"AWS - IAM Roles for Service Accounts (IRSA)"},"3033":{"id":"/reference/workload-identity/AWS-2","page_id":"/reference/workload-identity/AWS","type":"heading","tags":[],"url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"Create OIDC Provider (AWS Console)"},"3034":{"id":"/reference/workload-identity/AWS-3","page_id":"/reference/workload-identity/AWS","type":"heading","tags":[],"url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Assign IAM Roles to Kubernetes Service Accounts"},"3035":{"id":"/reference/workload-identity/AWS-4","page_id":"/reference/workload-identity/AWS","type":"heading","tags":[],"url":"/reference/workload-identity/AWS#configure-dgx-cloud-lepton-workloads-to-use-a-kubernetes-service-account","content":"Configure DGX Cloud Lepton Workloads to Use a Kubernetes Service Account"},"3036":{"id":"/reference/workload-identity/AWS-5","page_id":"/reference/workload-identity/AWS","type":"heading","tags":[],"url":"/reference/workload-identity/AWS#use-irsa-with-the-aws-sdk","content":"Use IRSA with the AWS SDK"},"3037":{"id":"/reference/workload-identity/AWS-6","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"In 2014, AWS Identity and Access Management added support for federated identities using OpenID\nConnect (OIDC). This feature allows you to authenticate AWS API calls with supported identity\nproviders and receive a valid OIDC JSON web token (JWT). You can pass this token to the AWS STS\nAssumeRoleWithWebIdentity API operation and receive IAM temporary role credentials. You can use\nthese credentials to interact with any AWS service, including Amazon S3 and DynamoDB."},"3038":{"id":"/reference/workload-identity/AWS-7","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"Enable IAM roles for service accounts by completing the following procedures:"},"3039":{"id":"/reference/workload-identity/AWS-8","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"Create an IAM OIDC provider for your cluster – You only complete this procedure once for each cluster."},"3040":{"id":"/reference/workload-identity/AWS-9","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"Assign IAM roles to Kubernetes service accounts – Complete this procedure for each unique set of permissions that you want an application to have."},"3041":{"id":"/reference/workload-identity/AWS-10","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"Configure DGX Cloud Lepton workloads to use a Kubernetes service account – Complete this procedure for each workload that needs access to AWS services."},"3042":{"id":"/reference/workload-identity/AWS-11","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"Use IRSA with the AWS SDK – Confirm that the workload uses an AWS SDK of a supported version and that the workload uses the default credential chain."},"3043":{"id":"/reference/workload-identity/AWS-12","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#aws---iam-roles-for-service-accounts-irsa","content":"This guide will take you through each of the above steps."},"3044":{"id":"/reference/workload-identity/AWS-13","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"Open the DGX Cloud Lepton console."},"3045":{"id":"/reference/workload-identity/AWS-14","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"In the top menu bar, select Nodes, and then select the name of your NodeGroup on the Nodes page."},"3046":{"id":"/reference/workload-identity/AWS-15","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"In the Details section, note the value of the OIDC provider URL."},"3047":{"id":"/reference/workload-identity/AWS-16","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"Open the IAM console at https://console.aws.amazon.com/iam/."},"3048":{"id":"/reference/workload-identity/AWS-17","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"In the left navigation pane, choose Identity Providers under Access management. If a provider is\nlisted that matches the URL for your cluster, then you already have a provider for your cluster.\nIf a provider isn't listed that matches the URL for your cluster, then you must create one."},"3049":{"id":"/reference/workload-identity/AWS-18","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"To create a provider, choose Add provider."},"3050":{"id":"/reference/workload-identity/AWS-19","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"For Provider type, select OpenID Connect."},"3051":{"id":"/reference/workload-identity/AWS-20","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"For Provider URL, enter the OIDC provider URL for your cluster."},"3052":{"id":"/reference/workload-identity/AWS-21","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"For Audience, enter https://kubernetes.default.svc."},"3053":{"id":"/reference/workload-identity/AWS-22","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"(Optional) Add any tags, for example, a tag to identify which NodeGroup this provider is for."},"3054":{"id":"/reference/workload-identity/AWS-23","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#create-oidc-provider-aws-console","content":"Choose Add provider."},"3055":{"id":"/reference/workload-identity/AWS-24","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"The first step is to create a new S3 bucket if necessary in the AWS console (or CLI).\nNote that the region depends on where you provision the S3 bucket. Be sure to select the correct region in the console\nafter logging in. In the following instructions, we will use a $BUCKET_NAME of my-s3-bucket-$ACCOUNT_ID."},"3056":{"id":"/reference/workload-identity/AWS-25","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Capture your AWS account ID to a variable:"},"3057":{"id":"/reference/workload-identity/AWS-26","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Once the S3 bucket and OIDC identity provider are created, set up Bash environment variables specific\nto the AWS-related requirements. Note that the $S3_REGION will depend on where you provisioned your bucket."},"3058":{"id":"/reference/workload-identity/AWS-27","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"DGX Cloud Lepton, by default, will prepend the namespace with ws-, so the workspace ID xyzpdq123\nwill become the Kubernetes namespace ws-xyzpdq123. Next, define some variables for the Kubernetes-related\nresources that will be used later. Specify a different $BUCKET_NAME if you used a custom name in the AWS console."},"3059":{"id":"/reference/workload-identity/AWS-28","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Now create a JSON file that defines an AWS Security Token Service (STS) role policy\nusing the OIDC URL. Note the use of Bash variable manipulation to omit the scheme from the URL.\nThen create the role using the AWS CLI:"},"3060":{"id":"/reference/workload-identity/AWS-29","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Now define and create the AWS policy for accessing the S3 bucket in another JSON file:"},"3061":{"id":"/reference/workload-identity/AWS-30","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#assign-iam-roles-to-kubernetes-service-accounts","content":"Now that you have created both a role and bucket access policy, attach the policy to that role:"},"3062":{"id":"/reference/workload-identity/AWS-31","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#configure-dgx-cloud-lepton-workloads-to-use-a-kubernetes-service-account","content":"Kubernetes has the concept of service accounts as resources, which are essentially non-human identities\nthat can interact with the API server and other resources. Every DGX Cloud Lepton workspace runs workloads as the\ndefault service account. Here we set up the workloads (container) with the three environment variables needed\nfor the AWS SDK to utilize the token and above configuration. Place the following lines in your container\nstartup (replacing the ACCOUNT_ID, ROLE_NAME, and S3_REGION appropriately):"},"3063":{"id":"/reference/workload-identity/AWS-32","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#use-irsa-with-the-aws-sdk","content":"Install the AWS CLI in your workload and run the following commands to confirm (replace BUCKET_NAME with the appropriate bucket\nname):"},"3064":{"id":"/reference/workload-identity/AWS-33","page_id":"/reference/workload-identity/AWS","tags":[],"type":"text","url":"/reference/workload-identity/AWS#use-irsa-with-the-aws-sdk","content":"See also https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts-minimum-sdk.html for\nfull details on various AWS SDKs."},"3065":{"id":"/reference/workload-identity/GCP","page_id":"/reference/workload-identity/GCP","type":"page","content":"GCP Workload Identity Federation","tags":[],"url":"/reference/workload-identity/GCP"},"3066":{"id":"/reference/workload-identity/GCP-0","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP","content":"Learn how to federate your DGX Cloud Lepton environment with Google Cloud."},"3067":{"id":"/reference/workload-identity/GCP-1","page_id":"/reference/workload-identity/GCP","type":"heading","tags":[],"url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"GCP - Workload Identity Federation via Cloud Console"},"3068":{"id":"/reference/workload-identity/GCP-2","page_id":"/reference/workload-identity/GCP","type":"heading","tags":[],"url":"/reference/workload-identity/GCP#configure-your-workload","content":"Configure Your Workload"},"3069":{"id":"/reference/workload-identity/GCP-3","page_id":"/reference/workload-identity/GCP","type":"heading","tags":[],"url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Workload Identity Federation via the gcloud CLI"},"3070":{"id":"/reference/workload-identity/GCP-4","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP","content":"Workload Identity Federation eliminates the\nmaintenance and security burden associated with service account keys. With\nWorkload Identity Federation, you can use Identity and Access Management (IAM)\nto grant external identities IAM roles, and direct access on Google Cloud\nresources. You can also grant access through service account impersonation (not\ncovered here)."},"3071":{"id":"/reference/workload-identity/GCP-5","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"To set up workload identity federation, navigate to your organization's Google\nCloud Console, then go to IAM & Admin -> Workload Identity Federation.\nClick the CREATE POOL button to proceed."},"3072":{"id":"/reference/workload-identity/GCP-6","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"In the subsequent workflow wizard, provide a name for the pool and an optional\ndescription, and select OpenID Connect (OIDC) as the provider."},"3073":{"id":"/reference/workload-identity/GCP-7","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"After selecting the provider type, enter dgx-cloud as the provider name and\npaste the Issuer URL from the DGX Cloud Lepton console into the Issuer (URL)\nfield. Under Audiences, select Allowed audiences and enter\nhttps://kubernetes.default.svc as the allowed audience."},"3074":{"id":"/reference/workload-identity/GCP-8","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"Lastly, add a mapping google.subject to assertion.sub in the\nConfigure provider attributes step."},"3075":{"id":"/reference/workload-identity/GCP-9","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"Once complete, the Workload Identity Pool will confirm that it can communicate\nwith the dgx-cloud Kubernetes cluster. You should see a green checkmark in\nthe upper left corner near the provider you created, as shown below."},"3076":{"id":"/reference/workload-identity/GCP-10","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"Next, click CONNECTED SERVICE ACCOUNTS in the upper right corner and then\nselect the DOWNLOAD CONFIG button. When the Configure your application\ndialog comes up, select the provider you created and then paste the path\n/var/run/secrets/kubernetes.io/serviceaccount/token into the OIDC ID token path\nfield, leaving the format as \"text\" and then click DOWNLOAD CONFIG to download the\nrequired configuration file."},"3077":{"id":"/reference/workload-identity/GCP-11","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"After saving the client configuration JSON file, click DISMISS. On the\nmain Identity Federation Pool page, locate and copy the value in the\nIAM Principal field. It will look similar to:"},"3078":{"id":"/reference/workload-identity/GCP-12","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"principal://iam.googleapis.com/projects/1091797687346/locations/global/workloadIdentityPools/nvidia-dgx-cloud-env/subject/SUBJECT_ATTRIBUTE_VALUE"},"3079":{"id":"/reference/workload-identity/GCP-13","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"where SUBJECT_ATTRIBUTE_VALUE represents the Kubernetes identifier\n(sub field in the token)."},"3080":{"id":"/reference/workload-identity/GCP-14","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"Since the service account is always default in DGX Cloud Lepton and the namespace is\nws-workspaceID, substitute\nsystem:serviceaccount:ws-workspaceID:default as the\nSUBJECT_ATTRIBUTE_VALUE. The overall IAM Principal will therefore be:"},"3081":{"id":"/reference/workload-identity/GCP-15","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"principal://iam.googleapis.com/projects/1068792787346/locations/global/workloadIdentityPools/nvidia-dgx-cloud-env/subject/system:serviceaccount:ws-workspaceID:default"},"3082":{"id":"/reference/workload-identity/GCP-16","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#gcp---workload-identity-federation-via-cloud-console","content":"This is the IAM Principal to use (within GCP IAM) to grant access to workloads\nin the DGX Cloud Lepton workspace, as shown below:"},"3083":{"id":"/reference/workload-identity/GCP-17","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#configure-your-workload","content":"Copy the JSON client configuration clientLibraryConfig.json file you downloaded to your\nworkload container (perhaps building the file into your container image). Set the environment\nvariable GOOGLE_APPLICATION_CREDENTIALS to point to it, log in with the credential, and set\nthe default GCP project. At this point, the GCP resources authorized through IAM will\nbe accessible using either gcloud or gsutil commands."},"3084":{"id":"/reference/workload-identity/GCP-18","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#configure-your-workload","content":"For example, add the following to your container startup:"},"3085":{"id":"/reference/workload-identity/GCP-19","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#configure-your-workload","content":"Use a command like the following to copy all objects from the bucket to a local /data\ndirectory:"},"3086":{"id":"/reference/workload-identity/GCP-20","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Once your GCS bucket is created, set up Bash environment variables for the specifics of the GCP-related variables\nrequired. Specify a different $BUCKET_NAME if you used a custom name in the console."},"3087":{"id":"/reference/workload-identity/GCP-21","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Workspaces within DGX Cloud Lepton become Kubernetes namespaces prefixed by ws-. Therefore,\nworkspaceID will become the Kubernetes namespace ws-workspaceID."},"3088":{"id":"/reference/workload-identity/GCP-22","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Next, define some variables for the Kubernetes-related resources that will be used later:"},"3089":{"id":"/reference/workload-identity/GCP-23","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Use the gcloud CLI to authenticate, then set your project for the subsequent instructions:"},"3090":{"id":"/reference/workload-identity/GCP-24","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Next, create a workload identity pool\nand an OIDC provider for that pool using the OIDC URL provided in the DGX Cloud Lepton console under NodeGroup:"},"3091":{"id":"/reference/workload-identity/GCP-25","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Now download the clientLibraryConfig.json file, which you use within your workload to configure\nthe Google Cloud tools and libraries (as shown above):"},"3092":{"id":"/reference/workload-identity/GCP-26","page_id":"/reference/workload-identity/GCP","tags":[],"type":"text","url":"/reference/workload-identity/GCP#workload-identity-federation-via-the-gcloud-cli","content":"Lastly, bind the Kubernetes service account to the bucket to allow access to the objects:"},"3093":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"page","content":"Enable JupyterLab","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab"},"3094":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-0","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","content":"Learn how to enable and access JupyterLab on your Dev Pod."},"3095":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-1","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#enabling-jupyterlab-for-default-built-in-images","content":"Enabling JupyterLab for Default Built-in Images"},"3096":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-2","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab","content":"Step 1: Enable JupyterLab"},"3097":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-3","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port","content":"Step 2: Expose JupyterLab Port"},"3098":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-4","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-3-configure-jupyterlab-workspace-root-directory-optional","content":"Step 3: Configure JupyterLab Workspace Root Directory (Optional)"},"3099":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-5","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-4-create-the-dev-pod","content":"Step 4: Create the Dev Pod"},"3100":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-6","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-5-set-new-token-for-jupyterlab","content":"Step 5: Set New Token for JupyterLab"},"3101":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-7","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-6-access-jupyterlab","content":"Step 6: Access JupyterLab"},"3102":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-8","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#enabling-jupyterlab-for-custom-images","content":"Enabling JupyterLab for Custom Images"},"3103":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-9","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab-1","content":"Step 1: Enable JupyterLab"},"3104":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-10","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#example-custom-entrypoint-run-command","content":"Example Custom Entrypoint (Run Command):"},"3105":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-11","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"Step 2: Expose JupyterLab Port"},"3106":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-12","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-3-create-the-dev-pod","content":"Step 3: Create the Dev Pod"},"3107":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-13","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-4-access-jupyterlab","content":"Step 4: Access JupyterLab"},"3108":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-14","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","content":"JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. This guide helps you set up and access JupyterLab in your Dev Pod on DGX Cloud Lepton."},"3109":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-15","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab","content":"DGX Cloud Lepton provides a simplified process for enabling JupyterLab for default container images. This requires the following two options:"},"3110":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-16","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab","content":"Use a Built-in image provided by DGX Cloud Lepton."},"3111":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-17","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab","content":"Use the Dev pod entrypoint as the Entrypoint."},"3112":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-18","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab","content":"Then, toggle the Enable JupyterLab checkbox to turn on that feature."},"3113":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-19","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port","content":"DGX Cloud Lepton does not expose any ports by default for security reasons."},"3114":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-20","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port","content":"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."},"3115":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-21","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port","content":"For built-in images configured in Step 1, select the Enable JupyterLab Proxy checkbox and the form will automatically fill in the port and protocol for you."},"3116":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-22","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port","content":"If exposing through host network, follow the screenshot to configure the container ports, but select Host Network instead of Proxy."},"3117":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-23","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-3-configure-jupyterlab-workspace-root-directory-optional","content":"If you are mounting persistent storage to the Dev Pod and using the default built-in images, you can specify the storage directory to be mounted as the workspace root directory for Jupyter. For more information on storage, refer to this guide."},"3118":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-24","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-3-configure-jupyterlab-workspace-root-directory-optional","content":"First, under Storage, specify a volume to attach to the Dev Pod, then specify the mount path for the volume. Select Mark as JupyterLab Root to use this mount path as the root workspace directory in JupyterLab."},"3119":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-25","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-4-create-the-dev-pod","content":"After these configurations, create the Dev Pod by clicking the Create Pod button."},"3120":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-26","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-5-set-new-token-for-jupyterlab","content":"Once the Dev Pod is created and ready, go to the details page and find connection information on the JupyterLab tab."},"3121":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-27","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-5-set-new-token-for-jupyterlab","content":"Before accessing your JupyterLab instance, use the following command to set a new token for JupyterLab in the Terminal tab:"},"3122":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-28","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-5-set-new-token-for-jupyterlab","content":"This command sets a new token and restarts the JupyterLab instance."},"3123":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-29","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-6-access-jupyterlab","content":"You can now access JupyterLab by clicking the generated proxy URL shown in the JupyterLab tab. Alternatively, visit the host node IP address with port 18889 if you chose to expose the port through Host Network."},"3124":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-30","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-6-access-jupyterlab","content":"Enter the token you set in the previous step to log in. You can then start using JupyterLab."},"3125":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-31","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab-1","content":"If you select a Custom image type, the Enable JupyterLab checkbox is not available. Custom images may not have JupyterLab installed, or the built-in run command may not work with the image."},"3126":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-32","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab-1","content":"In this case, you must first specify the image URL, and then select one of the following Entrypoints:"},"3127":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-33","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab-1","content":"Image default entrypoint - Select this option if the container image starts JupyterLab automatically by default. In this case, ensure that the default JupyterLab run command has authentication and security features enabled."},"3128":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-34","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-1-enable-jupyterlab-1","content":"Custom entrypoint - Select this option if you want to specify the JupyterLab install or run command. For an example custom entrypoint, refer to the screenshot and code example below."},"3129":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-35","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#example-custom-entrypoint-run-command","content":"In this example, the command assumes JupyterLab is already installed in the container image. It starts jupyter-lab and passes configuration parameters so the notebook token is set as the $ENV_SECRET environment variable, which is configured to be injected via DGX Cloud Lepton. The port is set to 8888, and the JupyterLab instance is set up to run as root and be accessed remotely."},"3130":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-36","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"DGX Cloud Lepton does not expose any ports by default for security reasons."},"3131":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-37","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."},"3132":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-38","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"If you enabled JupyterLab for a Dev Pod in Step 1, DGX Cloud Lepton starts the JupyterLab instance on a container port. The linked example uses port 18889; default JupyterLab commands typically use port 8888.\nYou will need to manually expose the port if you want to access JupyterLab from outside the container."},"3133":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-39","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"For more information on how to expose container ports, refer to this guide."},"3134":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-40","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"In the Container Ports section, add a new port with the following configurations:"},"3135":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-41","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"Protocol: TCP"},"3136":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-42","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"Port: 18889 (or 8888)"},"3137":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-43","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-2-expose-jupyterlab-port-1","content":"Expose Through: Select Host Network or Proxy. Learn more about the differences here. We will use Proxy in this example."},"3138":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-44","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-3-create-the-dev-pod","content":"After these configurations, create the Dev Pod by clicking the Create Pod button."},"3139":{"id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab-45","page_id":"/features/dev-pods/create-from-container-image/enable-jupyter-lab","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/enable-jupyter-lab#step-4-access-jupyterlab","content":"You can now access JupyterLab by clicking the generated proxy URL shown in the JupyterLab tab. Alternatively, visit the host node IP address with port 18889 (or 8888) if you chose to expose the port through Host Network."},"3140":{"id":"/features/dev-pods/create-from-container-image/ssh-access","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"page","content":"SSH Access","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access"},"3141":{"id":"/features/dev-pods/create-from-container-image/ssh-access-0","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access","content":"Learn how to configure SSH to access your Dev Pod"},"3142":{"id":"/features/dev-pods/create-from-container-image/ssh-access-1","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#prerequisites","content":"Prerequisites"},"3143":{"id":"/features/dev-pods/create-from-container-image/ssh-access-2","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#ssh-with-default-images","content":"SSH with Default Images"},"3144":{"id":"/features/dev-pods/create-from-container-image/ssh-access-3","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-built-in-image","content":"Step 1: Start the Dev Pod with Built-in Image"},"3145":{"id":"/features/dev-pods/create-from-container-image/ssh-access-4","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"Step 2: Choose or Create an SSH Public Key"},"3146":{"id":"/features/dev-pods/create-from-container-image/ssh-access-5","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"Step 3: Configure SSH Ports"},"3147":{"id":"/features/dev-pods/create-from-container-image/ssh-access-6","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#ssh-with-custom-images","content":"SSH with Custom Images"},"3148":{"id":"/features/dev-pods/create-from-container-image/ssh-access-7","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Step 1: Start the Dev Pod with Custom Image"},"3149":{"id":"/features/dev-pods/create-from-container-image/ssh-access-8","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-prepare-the-setup-script","content":"Step 2: Prepare the Setup Script"},"3150":{"id":"/features/dev-pods/create-from-container-image/ssh-access-9","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-execute-the-script-via-web-terminal","content":"Step 3: Execute the Script via Web Terminal"},"3151":{"id":"/features/dev-pods/create-from-container-image/ssh-access-10","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#step-4-connect-to-the-dev-pod-via-ssh","content":"Step 4: Connect to the Dev Pod via SSH"},"3152":{"id":"/features/dev-pods/create-from-container-image/ssh-access-11","page_id":"/features/dev-pods/create-from-container-image/ssh-access","type":"heading","tags":[],"url":"/features/dev-pods/create-from-container-image/ssh-access#next-steps","content":"Next Steps"},"3153":{"id":"/features/dev-pods/create-from-container-image/ssh-access-12","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access","content":"This guide shows how to configure SSH to access your Dev Pod."},"3154":{"id":"/features/dev-pods/create-from-container-image/ssh-access-13","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#prerequisites","content":"For SSH to work on DGX Cloud Lepton, the user and machine accessing the Dev Pod via SSH must be able to reach either the public or private IP of the node(s) on which the Dev Pods are running."},"3155":{"id":"/features/dev-pods/create-from-container-image/ssh-access-14","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#prerequisites","content":"For more information on configuring nodes, refer to the node requirements guide."},"3156":{"id":"/features/dev-pods/create-from-container-image/ssh-access-15","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#prerequisites","content":"If you want to use Dev Pods on DGX Cloud Lepton, you need to open ports from 40000 to 65535 on your network. These ports are used to pass through the SSH port on the Dev Pod. For more information, refer to this guide."},"3157":{"id":"/features/dev-pods/create-from-container-image/ssh-access-16","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#ssh-with-default-images","content":"Default images provided by DGX Cloud Lepton come with SSH service pre-configured. You only need to add your SSH public key during the creation process."},"3158":{"id":"/features/dev-pods/create-from-container-image/ssh-access-17","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-built-in-image","content":"Navigate to the Dev Pod creation page. In the Container Configuration section, select one of the default Built-in images."},"3159":{"id":"/features/dev-pods/create-from-container-image/ssh-access-18","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-built-in-image","content":"Leave the Entrypoint (Run Command) set as the default Dev Pod entrypoint."},"3160":{"id":"/features/dev-pods/create-from-container-image/ssh-access-19","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"In the SSH Public Key field, specify or create a new SSH public key if you want to access the Dev Pod via SSH."},"3161":{"id":"/features/dev-pods/create-from-container-image/ssh-access-20","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"All public keys are saved securely as a Secret in your workspace."},"3162":{"id":"/features/dev-pods/create-from-container-image/ssh-access-21","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"If you have already added an SSH public key as a Secret, you can select it from the SSH Public Key dropdown list."},"3163":{"id":"/features/dev-pods/create-from-container-image/ssh-access-22","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"To create an SSH public key, first generate an SSH key pair in your local terminal:"},"3164":{"id":"/features/dev-pods/create-from-container-image/ssh-access-23","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-choose-or-create-an-ssh-public-key","content":"After you generate a public and private key pair, click the + button, enter a name, paste the public key, and click Add SSH Public Key Secret. The key is automatically filled in after creation."},"3165":{"id":"/features/dev-pods/create-from-container-image/ssh-access-24","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"Select Enable SSH Host Network to pre-populate the container port configuration with the default SSH port 2222 and the Host Network option selected."},"3166":{"id":"/features/dev-pods/create-from-container-image/ssh-access-25","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"The Proxy option for exposing container ports does not currently support SSH. For more information, refer to this guide."},"3167":{"id":"/features/dev-pods/create-from-container-image/ssh-access-26","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"Fill in the rest of the configurations as applicable and create the Dev Pod. You will then see SSH connection information in the Connection tab."},"3168":{"id":"/features/dev-pods/create-from-container-image/ssh-access-27","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"Copy the SSH connection command and run it in your local terminal to access the Dev Pod via SSH. The private SSH key corresponding to the configured public key must be available and referenced in the SSH command via the ssh -i parameter."},"3169":{"id":"/features/dev-pods/create-from-container-image/ssh-access-28","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"The machine accessing the Dev Pod must be able to reach and communicate with either the public or private IP of the node(s) on which the Dev Pods are running."},"3170":{"id":"/features/dev-pods/create-from-container-image/ssh-access-29","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-configure-ssh-ports","content":"For more information on configuring nodes, refer to the node requirements guide."},"3171":{"id":"/features/dev-pods/create-from-container-image/ssh-access-30","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#ssh-with-custom-images","content":"If you are using a custom image, the SSH Public Key field is not present because DGX Cloud Lepton cannot determine whether SSH is pre-configured."},"3172":{"id":"/features/dev-pods/create-from-container-image/ssh-access-31","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#ssh-with-custom-images","content":"You can still use SSH to access your Dev Pod by manually setting up SSH in the container. Install and run the SSH server after starting the Dev Pod (see the example below), or install the SSH server as part of the run command (see the Install and Configure SSH and Jupyter for Custom Images example)."},"3173":{"id":"/features/dev-pods/create-from-container-image/ssh-access-32","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Navigate to the Dev Pod creation page. In the Container Configuration section, select Custom image, then enter the URL for your container image."},"3174":{"id":"/features/dev-pods/create-from-container-image/ssh-access-33","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Under Entrypoint, select one of the following options:"},"3175":{"id":"/features/dev-pods/create-from-container-image/ssh-access-34","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Image default entrypoint - Select this option to use the default entrypoint for the image."},"3176":{"id":"/features/dev-pods/create-from-container-image/ssh-access-35","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Custom entrypoint - Select this option if you want to manually specify the run command for the Dev Pod (to run a custom start-up script, for example)."},"3177":{"id":"/features/dev-pods/create-from-container-image/ssh-access-36","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Under Expose Ports, click the Add Port button."},"3178":{"id":"/features/dev-pods/create-from-container-image/ssh-access-37","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"If following the example below, specify 2222 as the port, or choose a different port if you are modifying or using your own script."},"3179":{"id":"/features/dev-pods/create-from-container-image/ssh-access-38","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Select the Host Network checkbox."},"3180":{"id":"/features/dev-pods/create-from-container-image/ssh-access-39","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"The Proxy option for exposing container ports does not currently support SSH. For more information, refer to this guide."},"3181":{"id":"/features/dev-pods/create-from-container-image/ssh-access-40","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-1-start-the-dev-pod-with-custom-image","content":"Fill in the rest of the configurations as applicable and create the Dev Pod."},"3182":{"id":"/features/dev-pods/create-from-container-image/ssh-access-41","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-2-prepare-the-setup-script","content":"Prepare your SSH public key and fill it into the script below."},"3183":{"id":"/features/dev-pods/create-from-container-image/ssh-access-42","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-execute-the-script-via-web-terminal","content":"Navigate to the pod details page and switch to the Terminal tab to open the web terminal for the pod."},"3184":{"id":"/features/dev-pods/create-from-container-image/ssh-access-43","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-3-execute-the-script-via-web-terminal","content":"Copy and paste the script into the terminal, then press Enter to execute it."},"3185":{"id":"/features/dev-pods/create-from-container-image/ssh-access-44","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-4-connect-to-the-dev-pod-via-ssh","content":"After the script finishes, connect to the Dev Pod via SSH from your local terminal using the command shown in the Connection tab."},"3186":{"id":"/features/dev-pods/create-from-container-image/ssh-access-45","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-4-connect-to-the-dev-pod-via-ssh","content":"The machine accessing the Dev Pod must be able to reach and communicate with either the public or private IP of the node(s) on which the Dev Pods are running."},"3187":{"id":"/features/dev-pods/create-from-container-image/ssh-access-46","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#step-4-connect-to-the-dev-pod-via-ssh","content":"For more information on configuring nodes, refer to the node requirements guide."},"3188":{"id":"/features/dev-pods/create-from-container-image/ssh-access-47","page_id":"/features/dev-pods/create-from-container-image/ssh-access","tags":[],"type":"text","url":"/features/dev-pods/create-from-container-image/ssh-access#next-steps","content":"Learn how to customize your container to meet your needs."},"3189":{"id":"/features/utilities/observability/audit-logs","page_id":"/features/utilities/observability/audit-logs","type":"page","content":"Audit Logs","tags":[],"url":"/features/utilities/observability/audit-logs"},"3190":{"id":"/features/utilities/observability/audit-logs-0","page_id":"/features/utilities/observability/audit-logs","tags":[],"type":"text","url":"/features/utilities/observability/audit-logs","content":"Monitor and troubleshoot your DGX Cloud Lepton applications with audit logs."},"3191":{"id":"/features/utilities/observability/audit-logs-1","page_id":"/features/utilities/observability/audit-logs","type":"heading","tags":[],"url":"/features/utilities/observability/audit-logs#what-are-audit-logs","content":"What are audit logs?"},"3192":{"id":"/features/utilities/observability/audit-logs-2","page_id":"/features/utilities/observability/audit-logs","type":"heading","tags":[],"url":"/features/utilities/observability/audit-logs#view-audit-logs","content":"View audit logs"},"3193":{"id":"/features/utilities/observability/audit-logs-3","page_id":"/features/utilities/observability/audit-logs","tags":[],"type":"text","url":"/features/utilities/observability/audit-logs","content":"DGX Cloud Lepton provides a comprehensive observability platform to help you monitor and troubleshoot your applications, including audit logs."},"3194":{"id":"/features/utilities/observability/audit-logs-4","page_id":"/features/utilities/observability/audit-logs","tags":[],"type":"text","url":"/features/utilities/observability/audit-logs#what-are-audit-logs","content":"Audit logs record actions and API calls performed by members of your workspace.\nIt helps track the changes made by members in your workspace."},"3195":{"id":"/features/utilities/observability/audit-logs-5","page_id":"/features/utilities/observability/audit-logs","tags":[],"type":"text","url":"/features/utilities/observability/audit-logs#view-audit-logs","content":"From the dashboard, select the Observability tab, then select Audit Logs."},"3196":{"id":"/features/utilities/observability/export","page_id":"/features/utilities/observability/export","type":"page","content":"Export Settings","tags":[],"url":"/features/utilities/observability/export"},"3197":{"id":"/features/utilities/observability/export-0","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export","content":"Export your logs and metrics data to your own storage."},"3198":{"id":"/features/utilities/observability/export-1","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#how-to-set-up","content":"How to Set Up"},"3199":{"id":"/features/utilities/observability/export-2","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#logs-configuration","content":"Logs Configuration"},"3200":{"id":"/features/utilities/observability/export-3","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#destination-type","content":"Destination Type"},"3201":{"id":"/features/utilities/observability/export-4","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#endpoint-url","content":"Endpoint URL"},"3202":{"id":"/features/utilities/observability/export-5","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#secret-key","content":"Secret Key"},"3203":{"id":"/features/utilities/observability/export-6","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#metrics-configuration","content":"Metrics Configuration"},"3204":{"id":"/features/utilities/observability/export-7","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#destination-type-1","content":"Destination Type"},"3205":{"id":"/features/utilities/observability/export-8","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#endpoint-url-1","content":"Endpoint URL"},"3206":{"id":"/features/utilities/observability/export-9","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#secret-key-1","content":"Secret Key"},"3207":{"id":"/features/utilities/observability/export-10","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"Enable Metrics Export When Creating Workload"},"3208":{"id":"/features/utilities/observability/export-11","page_id":"/features/utilities/observability/export","type":"heading","tags":[],"url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Labels of logs and metrics"},"3209":{"id":"/features/utilities/observability/export-12","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export","content":"DGX Cloud Lepton provides a way to export your logs and metrics data to your own storage or analytics platform."},"3210":{"id":"/features/utilities/observability/export-13","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export","content":"Only workspace admins can set up the export settings."},"3211":{"id":"/features/utilities/observability/export-14","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#how-to-set-up","content":"Login to the DGX Cloud Lepton Dashboard."},"3212":{"id":"/features/utilities/observability/export-15","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#how-to-set-up","content":"Navigate to the Observability settings page"},"3213":{"id":"/features/utilities/observability/export-16","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#how-to-set-up","content":"You can see two export configurations:"},"3214":{"id":"/features/utilities/observability/export-17","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#how-to-set-up","content":"Logs Export: Exports logs for all workloads created in this workspace. Logs are streamed in real time to the specified external destination."},"3215":{"id":"/features/utilities/observability/export-18","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#how-to-set-up","content":"Metrics Export: Collects and exports all custom metrics instrumented by developers within their applications from workloads created in this workspace."},"3216":{"id":"/features/utilities/observability/export-19","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#destination-type","content":"Currently, we only support exporting logs to Datadog."},"3217":{"id":"/features/utilities/observability/export-20","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#endpoint-url","content":"The endpoint URL is the URL of your Datadog instance. Learn more about obtaining the endpoint URL from the Datadog documentation."},"3218":{"id":"/features/utilities/observability/export-21","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#secret-key","content":"Select the secret key for your Datadog instance. You can add the secret key directly here or under Settings -> Secrets in the DGX Cloud Lepton dashboard. Refer to this guide for more details."},"3219":{"id":"/features/utilities/observability/export-22","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#secret-key","content":"For Datadog secret key, refer to the official documentation for more details."},"3220":{"id":"/features/utilities/observability/export-23","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#destination-type-1","content":"Currently, we only support exporting metrics to Datadog."},"3221":{"id":"/features/utilities/observability/export-24","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#endpoint-url-1","content":"The endpoint URL is the URL of your Datadog instance. Learn more about obtaining the endpoint URL from the Datadog documentation."},"3222":{"id":"/features/utilities/observability/export-25","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#secret-key-1","content":"Select the secret key of your Datadog instance. You can add the secret key directly here or under Settings -> Secrets in the DGX Cloud Lepton dashboard. Refer to this guide for more details."},"3223":{"id":"/features/utilities/observability/export-26","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#secret-key-1","content":"For Datadog secret key, refer to the official documentation for more details."},"3224":{"id":"/features/utilities/observability/export-27","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"To collect and export metrics from workloads, enable metrics export when creating the workload and specify the export port and path."},"3225":{"id":"/features/utilities/observability/export-28","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"You can specify the export port and path under Advanced Configuration when creating the workload."},"3226":{"id":"/features/utilities/observability/export-29","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"The DGX Cloud Lepton platform expects your workload application provide an HTTP endpoint that serves metrics in Prometheus format on the specified port and path. Most applications use the official Prometheus library."},"3227":{"id":"/features/utilities/observability/export-30","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"For Python applications, handle HTTP requests as shown in the example below. Configure the export port as 8080 and the path as /metrics."},"3228":{"id":"/features/utilities/observability/export-31","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"Example output from the endpoint includes:"},"3229":{"id":"/features/utilities/observability/export-32","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"http_response_time_seconds is a metric emitted by the above application."},"3230":{"id":"/features/utilities/observability/export-33","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#enable-metrics-export-when-creating-workload","content":"Find more details about the Prometheus Python library in the official documentation."},"3231":{"id":"/features/utilities/observability/export-34","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"This table contains DGX Cloud Lepton platform-specific labels attached to exported logs and metrics. These labels are available in the export destination (for example, Datadog) and can be used for filtering logs and metrics."},"3232":{"id":"/features/utilities/observability/export-35","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Label"},"3233":{"id":"/features/utilities/observability/export-36","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Description"},"3234":{"id":"/features/utilities/observability/export-37","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"lepton_job_name"},"3235":{"id":"/features/utilities/observability/export-38","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Job name, only available for metrics from lepton job"},"3236":{"id":"/features/utilities/observability/export-39","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"lepton_deployment_name"},"3237":{"id":"/features/utilities/observability/export-40","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Deployment name, only available for metrics from lepton endpoint and dev pod"},"3238":{"id":"/features/utilities/observability/export-41","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"lepton_replica_id"},"3239":{"id":"/features/utilities/observability/export-42","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Replica ID"},"3240":{"id":"/features/utilities/observability/export-43","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"lepton_workspace"},"3241":{"id":"/features/utilities/observability/export-44","page_id":"/features/utilities/observability/export","tags":[],"type":"text","url":"/features/utilities/observability/export#labels-of-logs-and-metrics","content":"Lepton workspace ID"},"3242":{"id":"/features/utilities/observability/logs","page_id":"/features/utilities/observability/logs","type":"page","content":"Logs","tags":[],"url":"/features/utilities/observability/logs"},"3243":{"id":"/features/utilities/observability/logs-0","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs","content":"Monitor and troubleshoot your DGX Cloud Lepton applications with real-time logs and advanced logs."},"3244":{"id":"/features/utilities/observability/logs-1","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#what-are-logs","content":"What are logs?"},"3245":{"id":"/features/utilities/observability/logs-2","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#with-real-time-logs","content":"With real-time logs:"},"3246":{"id":"/features/utilities/observability/logs-3","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#with-advanced-logs","content":"With advanced logs:"},"3247":{"id":"/features/utilities/observability/logs-4","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#real-time-logs","content":"Real-time Logs"},"3248":{"id":"/features/utilities/observability/logs-5","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#advanced-logs","content":"Advanced Logs"},"3249":{"id":"/features/utilities/observability/logs-6","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#filters","content":"Filters"},"3250":{"id":"/features/utilities/observability/logs-7","page_id":"/features/utilities/observability/logs","type":"heading","tags":[],"url":"/features/utilities/observability/logs#log-context","content":"Log Context"},"3251":{"id":"/features/utilities/observability/logs-8","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs","content":"DGX Cloud Lepton provides a comprehensive observability platform to help you monitor and troubleshoot your applications, including real-time logs and advanced logs."},"3252":{"id":"/features/utilities/observability/logs-9","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#what-are-logs","content":"Logs are generated by the replicas in jobs and pods, including all standard output and error output (print or echo)."},"3253":{"id":"/features/utilities/observability/logs-10","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-real-time-logs","content":"Logs are real-time and can be viewed as soon as the replicas in deployments, jobs, and pods are running. Real-time logs are no longer available once the workloads are deleted or terminated."},"3254":{"id":"/features/utilities/observability/logs-11","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-real-time-logs","content":"Real-time logs display only the last 10,000 rows."},"3255":{"id":"/features/utilities/observability/logs-12","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-advanced-logs","content":"Support search and filtering."},"3256":{"id":"/features/utilities/observability/logs-13","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-advanced-logs","content":"View log context."},"3257":{"id":"/features/utilities/observability/logs-14","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-advanced-logs","content":"Query at the deployment, job, pod, and workspace levels."},"3258":{"id":"/features/utilities/observability/logs-15","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#with-advanced-logs","content":"By default, if Advanced logs are enabled, generated logs are stored for 7 days. If a longer log retention period is required, contact NVIDIA support."},"3259":{"id":"/features/utilities/observability/logs-16","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#real-time-logs","content":"To view real-time logs:"},"3260":{"id":"/features/utilities/observability/logs-17","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#real-time-logs","content":"From the dashboard, navigate to the deployment, job, or pod detail page."},"3261":{"id":"/features/utilities/observability/logs-18","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#real-time-logs","content":"Select the replicas tab."},"3262":{"id":"/features/utilities/observability/logs-19","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#real-time-logs","content":"Click the Logs button to view the real-time logs."},"3263":{"id":"/features/utilities/observability/logs-20","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#real-time-logs","content":"You can also click the Live button in the top-right corner to switch to live mode."},"3264":{"id":"/features/utilities/observability/logs-21","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"Advanced logs allow you to access and query logs across multiple replicas and historical data simultaneously."},"3265":{"id":"/features/utilities/observability/logs-22","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"By default, the advanced logs feature is disabled in your workspace. You need to enable it manually in the settings page."},"3266":{"id":"/features/utilities/observability/logs-23","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"All the workloads created after enabling that feature will start collecting and storing advanced logs automatically. Logs are stored for a default of 7 days. If a longer log retention period is required, contact NVIDIA support."},"3267":{"id":"/features/utilities/observability/logs-24","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"You can also enable advanced logs for a specific deployment, job, or pod by selecting the option in the Advanced Configuration section during creation."},"3268":{"id":"/features/utilities/observability/logs-25","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"To view advanced logs:"},"3269":{"id":"/features/utilities/observability/logs-26","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"From the dashboard, select the Observability tab and select Logs."},"3270":{"id":"/features/utilities/observability/logs-27","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#advanced-logs","content":"Or, select the Logs tab from the deployment, job, or pod detail page."},"3271":{"id":"/features/utilities/observability/logs-28","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"You can use the following filters from the left sidebar to get a refined search experience."},"3272":{"id":"/features/utilities/observability/logs-29","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Filter"},"3273":{"id":"/features/utilities/observability/logs-30","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Description"},"3274":{"id":"/features/utilities/observability/logs-31","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Timeline"},"3275":{"id":"/features/utilities/observability/logs-32","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Query logs by specific time, including the maximum (7 days) or a custom time range, default is most recent 24 hours."},"3276":{"id":"/features/utilities/observability/logs-33","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Scope"},"3277":{"id":"/features/utilities/observability/logs-34","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Select or enter a deployment, job, or pod name to query the logs of all replicas within that scope."},"3278":{"id":"/features/utilities/observability/logs-35","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Replica"},"3279":{"id":"/features/utilities/observability/logs-36","page_id":"/features/utilities/observability/logs","tags":[],"type":"text","url":"/features/utilities/observability/logs#filters","content":"Select or enter a specific replica name to query the logs of that 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: If set, the endpoint can temporarily use free resources from nodes reserved by other reservations. 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Multiple environment variables can be set with multiple --env flags."],[411,"--export, environment"],[403,"--gres=gpu:X, --cpus-per-task, --mem"],[2829,"--head-allow-burst-to-other-reservation BOOLEAN : Allow the head node group to burst to other reservations."],[2827,"--head-allowed-nodes TEXT : Comma-separated node names within the specified head dedicated node group (affinity)."],[2823,"--head-mount TEXT : Persistent storage to be mounted to the head group, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."],[2826,"--head-node-group TEXT : Dedicated node group(s) for the head node group (affinity). Only the first may be applied depending on backend support."],[2828,"--head-reservation TEXT : Reservation ID for the head node group."],[2821,"--head-resource-shape TEXT : Resource shape for the head node group."],[2822,"--head-shared-memory-size INTEGER : Shared memory size for the head node group, in MiB."],[2283,"--help : Show this message and exit."],[2342,"--help : Show this message and exit."],[2346,"--help : Show this message and exit."],[2350,"--help : Show this message and exit."],[2354,"--help : Show this message and exit."],[2360,"--help : Show this message and exit."],[2365,"--help : Show this message and exit."],[2392,"--help : Show this message and exit."],[2396,"--help : Show this message and exit."],[2401,"--help : Show this message and exit."],[2405,"--help : Show this message and exit."],[2425,"--help : Show this message and exit."],[2432,"--help : Show this message and exit."],[2436,"--help : Show this message and exit."],[2440,"--help : Show this message and exit."],[2444,"--help : Show this message and exit."],[2491,"--help : Show this message and exit."],[2545,"--help : Show this message and exit."],[2554,"--help : Show this message and exit."],[2561,"--help : Show this message and exit."],[2568,"--help : Show this message and exit."],[2576,"--help : Show this message and exit."],[2584,"--help : Show this message and exit."],[2590,"--help : Show this message and exit."],[2595,"--help : Show this message and exit."],[2599,"--help : Show this message and exit."],[2603,"--help : Show this message and exit."],[2608,"--help : Show this message and exit."],[2613,"--help : Show this message and exit."],[2618,"--help : Show this message and exit."],[2629,"--help : Show this message and exit."],[2652,"--help : Show this message and exit."],[2662,"--help : Show this message and exit."],[2670,"--help : Show this message and exit."],[2684,"--help : Show this message and exit."],[2692,"--help : Show this message and exit."],[2697,"--help : Show this message and exit."],[2723,"--help : Show this message and exit."],[2755,"--help : Show this message and exit."],[2760,"--help : Show this message and exit."],[2765,"--help : Show this message and exit."],[2769,"--help : Show this message and exit."],[2773,"--help : Show this message and exit."],[2777,"--help : Show this message and exit."],[2803,"--help : Show this message and exit."],[2813,"--help : Show this message and exit."],[2845,"--help : Show this message and exit."],[2849,"--help : Show this message and exit."],[2855,"--help : Show this message and exit."],[2860,"--help : Show this message and exit."],[2875,"--help : Show this message and exit."],[2892,"--help : Show this message and exit."],[2901,"--help : Show this message and exit."],[2904,"--help : Show this message and exit."],[2908,"--help : Show this message and exit."],[2919,"--help : Show this message and exit."],[2923,"--help : Show this message and exit."],[2958,"--help : Show this message and exit."],[2973,"--help : Show this message and exit."],[2977,"--help : Show this message and exit."],[2981,"--help : Show this message and exit."],[2985,"--help : Show this message and exit."],[2988,"--help : Show this message and exit."],[2991,"--help : Show this message and exit."],[2994,"--help : Show this message and exit."],[2997,"--help : Show this message and exit."],[3000,"--help : Show this message and exit."],[2818,"--image TEXT : Ray cluster container image. Default: ray:2.48.0-py312-gpu"],[982,"--image-pull-secrets my-pull-secret: Replace with the name of the container registry authentication secret you created on DGX Cloud Lepton."],[2319,"--image-pull-secrets TEXT : Secrets to use for pulling images."],[2524,"--image-pull-secrets TEXT : Secrets to use for pulling images."],[2740,"--image-pull-secrets TEXT : Secrets to use for pulling images."],[2819,"--image-pull-secrets TEXT : Secrets to use for pulling images."],[2388,"--ingress-timeout-seconds INTEGER : Ingress request timeout in seconds (300-6000)."],[2340,"--ingress-timeout-seconds INTEGER : Ingress request timeout in seconds (300-6000). If not specified, defaults to 300."],[2316,"--initial-delay-seconds INTEGER : If specified, the endpoint will allow the specified amount of seconds for the photon to initialize before it starts the service. Usually you should not need this. If you have an endpoint that takes a long time to initialize, set it to a longer value."],[2525,"--intra-job-communication BOOLEAN : Enable intra-job communication. If --num-workers is set, this is automatically enabled."],[2374,"--ip-whitelist TEXT : IP addresses or CIDR ranges that are allowed to access the endpoint. Can be specified multiple times or as comma-separated values. Examples: --ip-whitelist 192.168.1.1,10.0.0.0/8 or --ip-whitelist 192.168.1.1 --ip-whitelist 10.0.0.0/8. Mutually exclusive with --public. This sets the IP allowlist in the deployment's authentication configuration."],[2312,"--ip-whitelist TEXT : IP addresses or CIDR ranges that are allowed to access the endpoint. Can be specified multiple times or as comma-separated values. Examples: --ip-whitelist 192.168.1.1,10.0.0.0/8 or --ip-whitelist 192.168.1.1 --ip-whitelist 10.0.0.0/8. Mutually exclusive with --public. This sets the IP allowlist in the deployment's authentication configuration. Note: --tokens are completely independent of IP access control."],[397,"--job-name"],[2972,"--lepton-classic : Login to the classic Lepton AI workspace."],[2649,"--limit INTEGER : The maximum number of result lines to return.(default: 5000) [default: 5000]"],[2341,"--load-balance [least-request|sticky-routing-default|sticky-routing-by-host-name|sticky-routing-by-resolved-ip] : Load balancing strategy: least-request | sticky-routing-default | sticky-routing-by-host-name | sticky-routing-by-resolved-ip"],[2389,"--load-balance [least-request|sticky-routing-default|sticky-routing-by-host-name|sticky-routing-by-resolved-ip] : Load balancing strategy: least-request | sticky-routing-default | sticky-routing-by-host-name | sticky-routing-by-resolved-ip"],[2519,"--max-failure-retry INTEGER : Maximum number of failures to retry per worker."],[2520,"--max-job-failure-retry INTEGER : Maximum number of failures to retry per whole job."],[2307,"--max-replicas INTEGER : (Will be deprecated) Maximum number of replicas."],[2869,"--metadata-json TEXT : JSON-serialized dictionary of metadata to attach to the job."],[2306,"--min-replicas INTEGER : (Will be deprecated soon) Minimum number of replicas."],[2859,"--min-replicas INTEGER : New minimum replicas for the worker group. [required]"],[2371,"--min-replicas INTEGER : Number of replicas to update to. Pass 0 to scale the number of replicas to zero, in which case the deployment status page will show the endpoint to be not ready until you scale it back with a positive number of replicas."],[2737,"--mount TEXT : Persistent storage to be mounted to the deployment, in the format STORAGE_PATH:MOUNT_PATH."],[2308,"--mount TEXT : Persistent storage to be mounted to the endpoint, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."],[2523,"--mount TEXT : Persistent storage to be mounted to the job, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."],[440,"--name: This is the name of the job and is analogous to the --job-name command."],[2314,"--no-traffic-timeout INTEGER : (Will be deprecated soon)If specified, the endpoint will be scaled down to 0 replicas after the specified number of seconds without traffic. Minimum is 60 seconds if set. Note that actual timeout may be up to 30 seconds longer than the specified value."],[2874,"--no-wait : Do not stream logs or wait for job completion."],[980,"--node-group my-node-group: Replace with the name of your node group to deploy the model on."],[442,"--node-group: This specifies which node group to run the job in. The list of available node groups can be found in the Nodes section of the DGX Cloud Lepton UI. This most closely resembles partitions in Slurm, but it goes further as different node groups could span different NVIDIA Cloud Providers (NCPs)."],[401,"--nodes (-N), --ntasks (-n)"],[399,"--partition, --cluster, --qos"],[2650,"--path PATH : Local directory path to save the log TXT files."],[2514,"--port TEXT : Deprecated flag, use --container-port instead."],[2329,"--privileged : Run the endpoint in privileged mode."],[2526,"--privileged : Run the job in privileged mode."],[2754,"--privileged : Run the pod in privileged mode."],[2373,"--public : Make the endpoint public (clears IP allowlist). To restrict access, use --ip-whitelist. Mutually exclusive with --ip-whitelist. Note: public can be combined with --tokens; tokens are independent and will not be removed unless explicitly updated (use --remove-tokens to clear)."],[2311,"--public : Make the endpoint public (no IP restriction). Mutually exclusive with --ip-whitelist. Can be combined with --tokens (public + tokens); '--public --tokens' is equivalent to '--tokens'. If neither --ip-whitelist nor --tokens is provided, the endpoint defaults to public access."],[2900,"--public-key : Make the secret public (default is private)."],[2318,"--public-photon : If specified, get the photon from the public photon registry. This is only supported for remote execution."],[983,"--public: This will make the endpoint available to anyone with the link. To require an access token, replace --public with --token <enter token here> and specify the token to require for incoming requests."],[2669,"--purge : Purge the credentials of the lepton login info."],[2976,"--purge : Purge the credentials of the lepton login info."],[2651,"--query TEXT : Specify the query string"],[2820,"--ray-version TEXT : Ray version to use."],[2636,"--replica TEXT : The name of the replica or a Replica object."],[2337,"--replica-spread TEXT : Controls how endpoint replicas are distributed across different nodes to improve availability, but it may lead to resource fragmentation."],[2385,"--replica-spread TEXT : Controls how endpoint replicas are distributed across different nodes to improve availability, but it may lead to resource fragmentation."],[2317,"--rerun : If specified, shutdown the endpoint of the same endpoint name and rerun it. Note that this may cause downtime of the photon if it is for production use, so use with caution. In a production environment, you should do photon create, push, and lep endpoint update instead."],[979,"--resource-shape gpu.1xh200: Replace with the name of your GPU resource if different, such as gpu.1xh100 or gpu.4xh100."],[441,"--resource-shape: This describes what resources should be allocated in the container. For multi-GPU workloads, this comes in the format gpu.Nx<gpu-type> where N is the number of GPUs to allocate in the job and <gpu-type> is the name of the GPU to run on, such as h100-80gb. For single-GPU workloads, this would be gpu.h100-80gb. The list of available resource shapes can be viewed with lep job create -h and viewing the --resource-shape option. This is analogous to the --gpus-per-node flag for Slurm."],[2511,"--run TEXT : Command string (\"run\") to substitute into the template."],[2734,"--run TEXT : Command string (\"run\") to substitute into the template."],[2866,"--runtime-env FILE : Path to a YAML file containing a runtime_env definition."],[2867,"--runtime-env-json TEXT : JSON-serialized runtime_env dictionary."],[981,"--secret NGC_API_KEY=NGC_API_KEY: Replace the second instance of NGC_API_KEY with the name of the secret that was created on DGX Cloud Lepton. For example, if your secret is named MY_NGC_KEY, the flag would be --secret NGC_API_KEY=MY_NGC_KEY."],[445,"--secret: You can specify a secret that is added to DGX Cloud Lepton to be usable in the job. Set the NAME to the name that the secret will be set to inside the container, and SECRET_NAME to the name of the secret in DGX Cloud Lepton."],[2518,"--segment-count INTEGER : Segment count for GB200 node groups. Must satisfy: 1 <= segment_count < num_workers, and num_workers % segment_count == 0. Workers within the same segment are scheduled into one NVL72 domain."],[2334,"--shared-memory-size INTEGER : Specify the shared memory size for this endpoint, in MiB."],[2535,"--shared-memory-size INTEGER : Specify the shared memory size for this job, in MiB."],[2384,"--shared-memory-size INTEGER : Update the shared memory size for this endpoint, in MiB."],[2637,"--start TEXT : The start time in ISO format.\nPlease note that all times must be in UTC.\nKeywords such as “now,” “today,” and “yesterday” will be interpreted as UTC timestamps.\nFor example, “now” corresponds to datetime.now(timezone.utc)."],[2537,"--start-at TEXT : Schedule the job to start at a specific time. If not specified, the job will start immediately.\nSupported formats: - Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS\nExample: 2024/12/25 13:10:01"],[2865,"--submission-id TEXT : Submission ID to specify for the job."],[2315,"--target-gpu-utilization INTEGER : (Will be deprecated soon)If min and max replicas are set, if the gpu utilization is higher than the target gpu utilization, autoscaler will scale up the replicas. If the gpu utilization is lower than the target gpu utilization, autoscaler will scale down the replicas. The value should be between 0 and 99."],[405,"--time"],[2375,"--tokens TEXT : Access tokens that can be used to access the endpoint. See docs for details on access control. If no tokens is specified, we will not change the tokens of the endpoint."],[2313,"--tokens TEXT : Additional tokens that can be used to access the endpoint. See docs for details on access control. These are completely independent of IP access control (--public and --ip-whitelist)."],[2527,"--ttl-seconds-after-finished INTEGER : (advanced feature) limits the lifetime of a job that has finished execution (either Completed or Failed). If not set, we will have it default to 72 hours. Ref: https://kubernetes.io/docs/concepts/workloads/controllers/job/#ttl-mechanism-for-finished-jobs"],[2321,"--visibility TEXT : Visibility of the endpoint. Can be 'public' or 'private'. If private, the endpoint will only be viewable by the creator and workspace admin."],[2376,"--visibility TEXT : Visibility of the endpoint. Can be 'public' or 'private'. If private, the endpoint will only be viewable by the creator and workspace admin."],[2534,"--visibility TEXT : Visibility of the job. Can be 'public' or 'private'. If private, the job will only be viewable by the creator and workspace admin."],[2844,"--visibility TEXT : Visibility of the Ray cluster. Can be 'public' or 'private'. If private, the cluster will only be viewable by the creator and workspace admin."],[2335,"--with-reservation TEXT : Assign the endpoint to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups)."],[2536,"--with-reservation TEXT : Assign the job to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups). If not provided, the job will be scheduled as usual."],[2752,"--with-reservation TEXT : Assign the pod to a specific reserved compute resource using a reservation ID (only applicable to dedicated node groups)."],[2839,"--worker-allow-burst-to-other-reservation BOOLEAN : Allow the worker node group to burst to other reservations."],[2837,"--worker-allowed-nodes TEXT : Comma-separated node names within the specified worker dedicated node group (affinity)."],[2830,"--worker-group-name TEXT : Name of the worker group (if specifying via flags)."],[2841,"--worker-max-replicas INTEGER : Maximum replicas for the worker node group."],[2840,"--worker-min-replicas INTEGER : Minimum replicas for the worker node group. Default: 1"],[2833,"--worker-mount TEXT : Persistent storage to be mounted to the worker group, in the format STORAGE_PATH:MOUNT_PATH or STORAGE_PATH:MOUNT_PATH:MOUNT_FROM."],[2836,"--worker-node-group TEXT : Dedicated node group(s) for the worker node group (affinity). Only the first may be applied depending on backend support."],[2838,"--worker-reservation TEXT : Reservation ID for the worker node group."],[2831,"--worker-resource-shape TEXT : Resource shape for the worker node group."],[2832,"--worker-shared-memory-size INTEGER : Shared memory size for the worker node group, in MiB."],[2868,"--working-dir TEXT : Directory or remote URI (.zip) for working_dir. If specified, overrides the option in --runtime-env."],[2323,"-ad,  --autoscale-down TEXT : Use this option if you want to have replicas but scale down after a specified time of no traffic.\nFor example, to set 2 replicas and scale down after 3600 seconds of no traffic,\nuse: --autoscale-down 2,3600s or --autoscale-down 2,3600\n(Note: Do not include spaces around the comma.)"],[2378,"-ad,  --autoscale-down TEXT : Use this option if you want to have replicas but scale down after a specified time of no traffic.\nFor example, to set 2 replicas and scale down after 3600 seconds of no traffic,\nuse: --autoscale-down 2,3600s or --autoscale-down 2,3600\n(Note: Do not include spaces around the comma.)"],[2324,"-agu,  --autoscale-gpu-util TEXT : Use this option to set a threshold for GPU utilization and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 50% threshold,\nuse: --autoscale-gpu-util 1,3,50% or --autoscale-gpu-util 1,3,50\n(Note: Do not include spaces around the comma.)"],[2379,"-agu,  --autoscale-gpu-util TEXT : Use this option to set a threshold for GPU utilization and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 50% threshold,\nuse: --autoscale-gpu-util 1,3,50% or --autoscale-gpu-util 1,3,50\n(Note: Do not include spaces around the comma.)"],[2326,"-aq,  --autoscale-qpm TEXT : Use this option to set a threshold for QPM and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 2.5 QPM,\nuse: --autoscale-qpm 1,3,2.5\n(Note: Do not include spaces around the comma.)"],[2381,"-aq,  --autoscale-qpm TEXT : Use this option to set a threshold for QPM and enable the system to scale between\na minimum and maximum number of replicas. For example,\nto scale between 1 (min_replica) and 3 (max_replica) with a 2.5 QPM,\nuse: --autoscale-qpm 1,3,2.5\n(Note: Do not include spaces around the comma.)"],[2659,"-c,  --credentials TEXT : The credentials of the lepton login info."],[2332,"-cbp,  --can-be-preempted : Allow this endpoint to be preempted by higher priority workloads."],[2532,"-cbp,  --can-be-preempted : Allow this job to be preempted by higher priority jobs (only for dedicated node groups)."],[2747,"-cbp,  --can-be-preempted : Allow this pod to be preempted by higher priority workloads."],[2333,"-cp,  --can-preempt : Allow this endpoint to preempt lower priority workloads."],[2533,"-cp,  --can-preempt : Allow this job to preempt lower priority jobs (only for dedicated node groups)."],[2748,"-cp,  --can-preempt : Allow this pod to preempt lower priority workloads."],[2633,"-d,  --deployment TEXT : The name of the deployment or a LeptonDeployment object."],[2690,"-d,  --detail : Show all the nodes"],[2764,"-d,  --detail : Show SSH/TCP/JupyterLab columns in the table."],[2359,"-d,  --detail : Show the endpoint detail"],[2853,"-d,  --detail : Show the full raycluster detail."],[2435,"-d,  --domain-name TEXT : domain-name of the ingress [required]"],[2738,"-e,  --env TEXT : Environment variables to pass to the deployment, in the format NAME=VALUE."],[2309,"-e,  --env TEXT : Environment variables to pass to the endpoint, in the format NAME=VALUE."],[2521,"-e,  --env TEXT : Environment variables to pass to the job, in the format NAME=VALUE."],[2297,"-f,  --file FILE : If provided, load the endpoint spec from this JSON file before applying CLI overrides. The file can be obtained from the dashboard's UI → CLI → 'Use spec file', or by running: lep endpoint get -i <endpoint_id> --path <download_path>."],[2735,"-f,  --file FILE : If provided, load the pod spec from this JSON file before applying CLI overrides. The file could be generated by UI → CLI → 'Use spec file', or by running: pod get -i <pod_id> --path <download_path>."],[2817,"-f,  --file FILE : If specified, load the Ray cluster spec from this JSON file before applying additional CLI overrides. The file should contain a LeptonRayClusterUserSpec JSON produced by lep raycluster get -p (spec only)."],[2509,"-f,  --file TEXT : If provided, load the job spec from this JSON file before applying CLI overrides. The file can be obtained from the dashboard's UI → CLI → 'Use spec file', or by running: lep job get -i <job_id> --path <download_path>."],[2236,"-h,  --help : Show this message and exit."],[2824,"-he,  --head-env TEXT : Environment variables for the head group, in the format NAME=VALUE."],[2825,"-hs,  --head-secret TEXT : Secrets for the head group, in the format NAME=SECRET_NAME. If secret name equals the environment variable name, you can just pass SECRET_NAME."],[2574,"-i,  --id TEXT : Job id"],[2582,"-i,  --id TEXT : The ID of the job to remove."],[2589,"-i,  --id TEXT : The job id to get events. [required]"],[2617,"-i,  --id TEXT : The job id to get events. [required]"],[2593,"-i,  --id TEXT : The job id to get log. [required]"],[2598,"-i,  --id TEXT : The job id to get replicas. [required]"],[2602,"-i,  --id TEXT : The job id to get replicas. [required]"],[2612,"-i,  --id TEXT : The job id to start. [required]"],[2607,"-i,  --id TEXT : The job id to stop. [required]"],[2369,"-i,  --id TEXT : The new photon id to update to. Use latest for the latest id."],[2299,"-i,  --photon-id TEXT : Specific version id of the photon to run. If not specified, we will run the most recent version of the photon."],[2984,"-i,  --workspace-id TEXT : ID of the workspace [required]"],[2970,"-i,  --workspace-id TEXT : The workspace id to log in to."],[2634,"-j,  --job TEXT : Specifies the job ID. To find the job ID, use the 'lep job list' command."],[2635,"-jn,  --job-name TEXT : Specifies the job name. If multiple jobs share this name, the logs for the newest job found will be retrieved by default."],[2661,"-l,  --lepton-classic : Login to the classic Lepton AI workspace."],[2328,"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."],[2383,"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."],[2528,"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."],[2744,"-lg,  --log-collection BOOLEAN : Enable or disable log collection (true/false). If not provided, the workspace setting will be used."],[2399,"-n,  --name TEXT : Endpoint name [required]"],[2345,"-n,  --name TEXT : Filter endpoints by name (case-insensitive substring). Can be specified multiple times."],[2559,"-n,  --name TEXT : Filter jobs by exact name match. Case-sensitive. Can specify multiple names. For safety, this is an exact match."],[2566,"-n,  --name TEXT : Filter jobs by exact name match. Case-sensitive. Can specify multiple names. For safety, this is an exact match."],[2812,"-n,  --name TEXT : Filter rayclusters by name (case-insensitive substring). Can be specified multiple times."],[2573,"-n,  --name TEXT : Job name"],[2508,"-n,  --name TEXT : Job name [required]"],[2296,"-n,  --name TEXT : Name of the endpoint being created. [required]"],[2439,"-n,  --name TEXT : name of the ingress [required]"],[2443,"-n,  --name TEXT : name of the ingress [required]"],[2732,"-n,  --name TEXT : Name of the pod to create. [required]"],[2816,"-n,  --name TEXT : Name of the Ray cluster. [required]"],[2758,"-n,  --name TEXT : Pod name [required]"],[2898,"-n,  --name TEXT : Secret name"],[2907,"-n,  --name TEXT : Secret name [required]"],[2363,"-n,  --name TEXT : The endpoint name to get log. [required]"],[2357,"-n,  --name TEXT : The endpoint name to get status. [required]"],[2395,"-n,  --name TEXT : The endpoint name to get status. [required]"],[2353,"-n,  --name TEXT : The endpoint name to remove. [required]"],[2349,"-n,  --name TEXT : The endpoint name to restart. [required]"],[2404,"-n,  --name TEXT : The endpoint name to stop. [required]"],[2776,"-n,  --name TEXT : The endpoint name to stop. [required]"],[2368,"-n,  --name TEXT : The endpoint name to update. [required]"],[2583,"-n,  --name TEXT : The name of the job to remove. If multiple jobs share the same name, only the newest one will be removed. Please use remove-all if you want remove multiple jobs with the same name."],[2768,"-n,  --name TEXT : The pod name to remove. [required]"],[2772,"-n,  --name TEXT : The pod name to ssh. [required]"],[2852,"-n,  --name TEXT : The raycluster name to get. [required]"],[2848,"-n,  --name TEXT : The raycluster name to remove. [required]"],[2864,"-n,  --name TEXT : The raycluster name to submit a job to. [required]"],[2858,"-n,  --name TEXT : The raycluster name to update. [required]"],[2552,"-n,  --name-or-id TEXT : Filter jobs by name or id. Case-insensitive and matches any part of the name or id. Can specify multiple names or ids. Example: 'train' will match 'training-job-123'"],[2553,"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."],[2560,"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."],[2567,"-ng,  --node-group TEXT : Filter jobs by node group. Case-insensitive and matches any part of the node group name."],[2320,"-ng,  --node-group TEXT : Node group for the endpoint. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."],[2529,"-ng,  --node-group TEXT : Node group for the job. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."],[2741,"-ng,  --node-group TEXT : Node group for the pod. If not set, use on-demand resources. You can repeat this flag multiple times to choose multiple node groups. Multiple node group option is currently not supported but coming soon for enterprise users. Only the first node group will be set if you input multiple node groups at this time."],[2691,"-ng,  --node-group TEXT : Show all the nodes in specific node groups by their IDs or names. Can use partial name/ID (e.g., 'h100' will match any name/ID containing 'h100'). Can specify multiple values."],[2696,"-ng,  --node-group TEXT : Show resource shapes for specific node groups by their IDs or names. Can use partial name/ID (e.g., 'h100' will match any name/ID containing 'h100'). Can specify multiple values."],[2330,"-ni,  --node-id TEXT : Node for the endpoint. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"],[2530,"-ni,  --node-id TEXT : Node for the job. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"],[2745,"-ni,  --node-id TEXT : Node for the pod. You can repeat this flag multiple times to choose multiple nodes. Please specify the node group when you are using this option"],[2400,"-p,  --path PATH : Optional local path to save the endpoint spec JSON. Directory or full filename accepted.\nIf a directory is provided, the file will be saved as endpoint-spec-[name].json."],[2575,"-p,  --path PATH : Optional local path to save the job spec JSON. Directory or full filename accepted.\nIf a directory is provided, the file will be saved as job-spec-[job_id].json so it can be reused with lep job create --file."],[2759,"-p,  --path PATH : Optional local path to save the pod spec JSON. Directory or full filename accepted.\nIf a directory is provided, the file will be saved as pod-spec-[name].json."],[2854,"-p,  --path PATH : Optional local path to save the raycluster spec JSON. Directory or full filename accepted. If a directory is provided, the file will be saved as raycluster-spec-[name].json."],[2763,"-p,  --pattern TEXT : Regular expression pattern to filter pod names."],[2298,"-p,  --photon TEXT : Name of the photon to run."],[2331,"-qp,  --queue-priority TEXT : Set the priority for this endpoint (dedicated node groups only)."],[2531,"-qp,  --queue-priority TEXT : Set the priority for this job (feature available only for dedicated node groups).\nCould be one of low-1, low-2, low-3, mid-4, mid-5, mid-6, high-7, high-8, high-9,Options: 1-9 or keywords: l / low (will be 1), m / mid (will be 4), h / high (will be 7).\nExamples: -qp 1, -qp 9, -qp low, -qp mid, -qp high, -qp l, -qp m, -qp h"],[2746,"-qp,  --queue-priority TEXT : Set the priority for this pod (dedicated node groups only)."],[2364,"-r,  --replica TEXT : The replica name to get log."],[2594,"-r,  --replica TEXT : The replica name to get log."],[2322,"-r,  -replicas, --replicas-static INTEGER : Use this option if you want a fixed number of replicas and want to turn off autoscaling.\nFor example, to set a fixed number of replicas to 2, you can use:\n--replicas-static 2 or\n-r 2"],[2377,"-r,  -replicas, --replicas-static INTEGER : Use this option if you want a fixed number of replicas and want to turn off autoscaling.\nFor example, to set a fixed number of replicas to 2, you can use:\n--replicas-static 2 or\n-r 2"],[2305,"-rs,  --resource-shape TEXT : Resource shape for the endpoint. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."],[2372,"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."],[2516,"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."],[2736,"-rs,  --resource-shape TEXT : Resource shape for the pod. Available types are: 'cpu.small', 'cpu.medium', 'cpu.large', 'gpu.a10', 'gpu.a10.6xlarge', 'gpu.a100-40gb', 'gpu.2xa100-40gb', 'gpu.4xa100-40gb', 'gpu.8xa100-40gb', 'gpu.a100-80gb', 'gpu.2xa100-80gb', 'gpu.4xa100-80gb', 'gpu.8xa100-80gb', 'gpu.h100-sxm', 'gpu.2xh100-sxm', 'gpu.4xh100-sxm', 'gpu.8xh100-sxm'."],[2739,"-s,  --secret TEXT : Secrets to pass to the deployment, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."],[2310,"-s,  --secret TEXT : Secrets to pass to the endpoint, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."],[2522,"-s,  --secret TEXT : Secrets to pass to the job, in the format NAME=SECRET_NAME. If secret name is also the environment variable name, you can omit it and simply pass SECRET_NAME."],[2550,"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."],[2557,"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."],[2564,"-s,  --state TEXT : Filter jobs by state. Case-insensitive and matches the beginning of the state name. Available states: Starting, Running, Failed, Completed, Stopped, Stopping, Deleting, Deleted, Restarting, Archived, Queueing, Awaiting, PendingRetry. Example: 'run' will match 'Running'. Can specify multiple states."],[2971,"-t,  --auth-token TEXT : Authentication token for the workspace."],[2358,"-t,  --show-tokens : Show tokens for the endpoint. Use with caution as this displays the tokens in plain text, and may be visible to others if you log the output."],[2510,"-t,  --template TEXT : template ID to render the job specification from."],[2733,"-t,  --template TEXT : Template ID to render the pod specification from."],[2558,"-u,  --user TEXT : Filter jobs by exact user ID match. Case-sensitive. Can specify multiple users. For safety, this is an exact match. This option is required to prevent accidental operations on other users' jobs. [required]"],[2565,"-u,  --user TEXT : Filter jobs by exact user ID match. Case-sensitive. Can specify multiple users. For safety, this is an exact match. This option is required to prevent accidental operations on other users' jobs. [required]"],[2551,"-u,  --user TEXT : Filter jobs by user. Case-insensitive and matches the beginning of the username. Can specify multiple users. Example: 'alice' will match 'alice123'"],[2660,"-u,  --workspace-url TEXT : The url of the workspace to login to."],[2899,"-v,  --value TEXT : Secret value"],[2235,"-v,  --version : Show the version and exit."],[2517,"-w,  --num-workers INTEGER : Number of workers to use for the job. For example, when you do a distributed training job of 4 replicas, use --num-workers 4."],[2834,"-we,  --worker-env TEXT : Environment variables for the worker group, in the format NAME=VALUE."],[2835,"-ws,  --worker-secret TEXT : Secrets for the worker group, in the format NAME=SECRET_NAME. If secret name equals the environment variable name, you can just pass SECRET_NAME."],[573,"\"from\": \"local:nfs\": If using remote shared storage, enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."],[588,"\"from\": \"local:nfs\": If using remote shared storage, enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."],[658,"\"from\": \"node-nfs:my-nfs\": Enter the name of the storage to mount in all jobs. This can be found in the UI while creating a job and selecting a storage option."],[571,"\"HF_TOKEN\": \"xxxxxxxxxxxxxxxxxx\": Add your Hugging Face authentication token between the quotation marks."],[586,"\"HF_TOKEN\": \"xxxxxxxxxxxxxxxxxx\": Add your Hugging Face authentication token between the quotation marks."],[587,"\"WANDB_API_KEY\": \"xxxxxxxxxxxxxxxxxx\": Add your Weights & Biases authentication token between the quotation marks."],[742,"(Experimental) Access Dev Pod with Cursor"],[512,"(Optional) Accept the Model-Specific Licenses"],[3053,"(Optional) Add any tags, for example, a tag to identify which NodeGroup this provider is for."],[613,"(Optional) Set the Access tokens to Enable public access to disable authentication for requests. This will make the endpoint available for anyone that has the URL without authentication. This should only be for temporary testing purposes."],[612,"(Optional) Set the Autoscaling option to increase the number of replicas and/or disable autoscaling to keep the instance persistent."],[1225,"(seq 1 $((LEPTON_JOB_TOTAL_WORKERS - 1)) | xargs -I {} echo ${LEPTON_JOB_WORKER_PREFIX}-{}.${LEPTON_SUBDOMAIN} | paste -sd ',' -)"],[2979,"[id]`."],[2206,"{{user.email_username}}: Username part of the workload creator's email (before @)."],[2205,"{{user.email}}: Email address of the workload creator."],[2207,"{% raw %} raw-content {% endraw %}: Content that skips template parsing."],[1208,"/opt/lepton/venv"],[1684,"✅"],[1685,"✅"],[1689,"✅ (with rules)"],[1694,"✅ (with rules)"],[1683,"✅ All reservations"],[1686,"✅ All reservations"],[1688,"✅ Own reservations"],[1691,"✅ Own reservations"],[1690,"❌"],[1693,"❌"],[1695,"❌"],[1696,"❌"],[1222,"${LEPTON_JOB_TOTAL_WORKERS}"],[1228,"${LEPTON_JOB_WORKER_INDEX}"],[1216,"${LEPTON_JOB_WORKER_PREFIX}-0.${LEPTON_SUBDOMAIN}"],[1187,"0"],[1193,"0"],[1172,"1"],[1190,"1"],[744,"1. Create a Dev Pod and Launch a Cursor Dev Tunnel"],[870,"1. Generate an SSH key on Pod 1"],[324,"10.50.0.0/16"],[326,"100.64.0.0/10"],[325,"172.20.0.0/16"],[871,"2. Apply the public key to Pods 2 through N"],[745,"2. Grant Access to the VS Code Server"],[1219,"29400"],[746,"3. Connect to the Dev Pod with Cursor"],[3012,"32,768"],[39,"A Batch Job is a one-off task that runs to completion, such as training a model or processing data.\nDGX Cloud Lepton provides a simple way to run and manage batch jobs in the cloud."],[3024,"A couple of notable features in the token are worth pointing out:"],[320,"A dedicated public IP address per machine is preferred"],[1069,"A DGX Cloud Lepton workspace with a shared storage volume"],[1288,"A DGX Cloud Lepton workspace with an available node group to create a RayCluster"],[1778,"A domain is automatically created for each endpoint by default."],[567,"A final script needs to be written to launch all of the data preparation jobs on the cluster. This uses NeMo-Run to authenticate with the DGX Cloud Lepton cluster and run distributed PyTorch jobs directly on the cluster. The jobs will be launched sequentially in the order they are called. Using the local VS Code session, save the following script locally as data-prep.py."],[838,"A fully configured SSH service on the PyTorch 25.04-py3 container"],[1674,"A higher-priority workload from the same reservation arrives."],[1035,"A Hugging Face access token."],[537,"A Hugging Face account with an API token (setup steps are provided in the following section)."],[1892,"A list of the training parameters for the job."],[631,"A minimal example to launch a NeMo Framework pre-training job on DGX Cloud Lepton"],[107,"A node group is a dedicated set of nodes of a specific GPU type. Node groups let you group multiple nodes for a given workload. There are two types of node groups:"],[855,"A Node Group with privilege mode enabled"],[1861,"A picture showing the fields filled out is below:"],[462,"A private S3 bucket with data you want to download"],[1281,"A RayCluster is a dedicated reservation of resources on DGX Cloud Lepton with one head node and at least one worker node. You can connect to the RayCluster and submit Ray jobs, which are scheduled and run directly on the cluster. You can run a single large job on the RayCluster, or multiple smaller jobs as resources allow."],[701,"A RayCluster will spin up several workers and a head node, allowing jobs to be submitted to these resources. The resources will remain allocated until this job on DGX Cloud Lepton is explicitly stopped by a user, making the resources available again. This option is best for users who plan on running multiple jobs concurrently or sequentially and do not want resources to get torn down and re-allocated after each job."],[717,"A RayJob will allocate a specific number of resources and create a one-off RayCluster where a single job is run and resources are freed up at conclusion. The job runs in the background and is not interactive. This option is best for users who plan on running a single job and want resources to be available for any job types and not limited to just Ray."],[1647,"A reservation is tied to a specific node group and workspace. Once created and approved, users included in the reservation can launch workloads—batch jobs, dev pods, or inference endpoints—directly against it. Reservations ensure predictable scheduling and isolation from other workloads."],[1720,"A resource shape specifies the combination of resources (such as GPU, CPU, memory, and storage) reserved for a workload."],[2135,"A role defines a set of permissions (Read or Read/Write) assigned to a DGX Cloud Lepton resource (such as Batch Jobs, Node Groups, and so on)."],[1283,"A running RayCluster continues to reserve the requested resources until you delete it. To use the resources for other workflows, such as standard batch jobs, first shut down the RayCluster. Instructions are provided below."],[508,"A self-contained example on how to train a Large Language Model from scratch using NeMo Framework"],[536,"A shared filesystem with read/write access that is mountable in jobs."],[646,"A shared filesystem with read/write access that is mountable in jobs."],[689,"A shared filesystem with read/write access that is mountable in jobs."],[538,"A Weights & Biases account with an API token (setup steps are provided in the following section)."],[3005,"A workload is a Dev Pod or a Batch Job on DGX Cloud Lepton."],[123,"A workspace is a virtual environment that provides a set of tools and resources for managing your infrastructure and workloads."],[257,"Accelerator Type: Defaults to the automatically detected accelerator type. You can also manually select the accelerator type from the preset options. The accelerator type name should identically match the GPU name shown when nvidia-smi is run on the GPU node."],[1998,"Accept Time: When the request was approved"],[1357,"Access Container Ports"],[1426,"Access Control"],[788,"Access Dev Pod with VS Code"],[835,"Access Interface: Connect to JupyterLab through the Pod's network interface on port 18889"],[759,"Access JupyterLab on Dev Pod via ngrok"],[25,"Access Lepton's enterprise-grade orchestration capabilities"],[2065,"Access permissions: Select Read-only."],[999,"Access to a container registry to host the custom image."],[688,"Access to a container registry to publish images."],[927,"Access to at least 1x NVIDIA A100 GPU or higher on DGX Cloud Lepton"],[1034,"Access to at least one NVIDIA A100 GPU or higher on DGX Cloud Lepton (actual GPU requirements depend on the model being deployed)."],[998,"Access to at least one NVIDIA GPU on DGX Cloud Lepton."],[957,"Access to at least one NVIDIA H100 GPU or higher on DGX Cloud Lepton."],[1957,"Access to subfolders if email username matches -> Allow users /user/<username>"],[1950,"Access to subfolders if email username matches: This automatically grants users access to all subfolders whose name matches their email username (the part before the @ symbol)."],[842,"Access to the complete PyTorch development environment"],[926,"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."],[956,"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."],[1033,"Access to your NIM Container Registry and NGC API Key. For more information, refer to the setup information."],[948,"Access Token"],[994,"Access Token"],[1029,"Access Token"],[2071,"Access Token: The personal access token you just created."],[2109,"Access Token: The personal access token you just created."],[58,"Access via Web Terminal"],[1358,"Access with IP Address"],[1359,"Access with Proxy URL"],[1789,"Accessing DGX Cloud Lepton Fine-Tuning"],[1392,"Accessing JupyterLab"],[1605,"Actions"],[120,"Activate Workspace"],[435,"Add environment variables and secrets under Environment Variables. For example, you can add HUGGING_FACE_HUB_TOKEN as a securely stored secret on DGX Cloud Lepton, which you can then reference as an environment variable within the container."],[211,"Add Machines"],[221,"Add machines via local command"],[215,"Add Machines via Local Command"],[220,"Add machines via SSH access"],[214,"Add Machines via SSH Access"],[1088,"Add responses to the Name and Resources sections at the top of the form:"],[1922,"Add Storage for Node Group"],[708,"Add your authentication key for the container registry in the Private Image Registry Auth field."],[724,"Add your authentication key for the container registry in the Private Image Registry Auth field."],[29,"Add Your Machines: Use either SSH or GPUd to add machines to your node group."],[591,"Additionally, a Python script needs to be created that converts the model to Hugging Face format once training finishes. Create a new directory named scripts using:"],[1097,"Additionally, the dataset doesn't have columns named Question and Answer like the fine-tuning tool expects, so we will use the Dataset Mapping concept to rename the columns in the datasets to something that the fine-tuning code understands. In particular, we will take the problem field as the question or input prompt, and the generated_solution field as the answer that we want the model to learn to generate for the prompt."],[304,"Additionally, the following are minimum storage requirements with three configuration options:"],[1215,"Address of the master node for distributed training"],[1224,"Addresses for worker nodes"],[1638,"Admin View of Reservations"],[1985,"Administrators can approve the request, reject it, or revoke access to a workspace."],[1655,"Admins can view all active reservation requests, node assignments, and statuses under:"],[1137,"Advanced"],[1324,"Advanced"],[1390,"Advanced"],[1805,"Advanced Configuration"],[259,"Advanced Configuration:"],[1431,"Advanced Configurations"],[3248,"Advanced Logs"],[3264,"Advanced logs allow you to access and query logs across multiple replicas and historical data simultaneously."],[434,"Advanced Settings: Configure advanced settings in the Advanced Settings section."],[114,"After adding machines to the node group, you can create workloads from the node group using available nodes.\nWhen creating a workload, select the node group in the Resource pane to view the GPU types available in that group."],[1963,"After adding volumes, you can use them in your workloads."],[151,"After authentication, validate the installation by running:"],[671,"After completing all jobs, you can remove them in the UI by clicking the Delete button next to each job in the Batch Jobs page."],[489,"After completing the configuration, click Create to submit the job. You can then view the job status on the job detail page."],[578,"After concatenation, there will be 465 large, combined files named nemotron-cc_N.jsonl where N indicates the subset of the data. Each file will be approximately 46 GB in size. All of the individual CC-MAIN*.jsonl files will be removed."],[975,"After creating a token, it will show you your credential. Copy the credential somewhere safe as it will only be shown once. Once saved, go back to your terminal and enter it when prompted. You should now be authenticated."],[575,"After creating the data preparation job, a pod for each worker and primary will be scheduled and started once resources become available on the cluster. You can monitor the process by viewing the logs in the DGX Cloud Lepton UI. The /nemo-workspace/data directory will evolve throughout the process with the following changes at the end of each stage:"],[478,"After creating the job, you will be redirected to the job overview page. Monitor the job status until the status shows \"Completed\"."],[2077,"After creating the service account, you need to create a key for it.\nNavigate to the service account page, switch to the KEYS tab, click the ADD KEY button, and select JSON as the key type."],[1114,"After creating your fine-tuning job, you will be taken back to the fine-tuning jobs list where your job will be shown at the top."],[773,"After creation and when the Dev Pod is ready, go to the details page and open the Terminal tab. Run the following command to set a new token for JupyterLab:"],[1780,"After creation, the domain appears in Pending Update status. Add the CNAME record to your DNS provider to point to the domain."],[68,"After creation, visit the details page to check the Dev Pod's status, connection information, metrics, and more."],[194,"After defining your mount points, add them to a job template with the following syntax:"],[1751,"After disabling a default shape, it appears as disabled in the Custom Shapes section. Users cannot select this shape when creating workloads unless you re‑enable it."],[576,"After downloading, there will be 23,240 compressed data shards named CC-MAIN-*.zstd where the * identifies the individual files. These files are nested within directories to indicate the shard's quality, type, whether it is human or synthetic, etc. Each file is approximately 150 MB in size."],[1600,"After enabling, an image‑acceleration daemon is deployed on each node in the group. You can view it on the node detail page. The daemon uses one CPU core and one GB of memory by default, and up to two CPU cores and 16 GB of memory."],[328,"After ensuring your machines meet these requirements, refer to this guide to add them to DGX Cloud Lepton."],[577,"After extraction, there will be 23,240 unzipped data shards named CC-MAIN-*.jsonl and all of the compressed .zstd files will be removed. Each file is approximately 350 MB in size."],[2095,"After filling in the form, click on Submit, and you will see the new Azure registry auth created successfully."],[2072,"After filling in the form, click Submit to create the new registry auth successfully."],[2085,"After filling in the form, click Submit to create the new registry auth successfully."],[2110,"After filling in the form, click Submit to create the new registry auth successfully."],[2122,"After filling in the form, click Submit to create the new registry auth successfully."],[1739,"After filling out the form, click Create to create the custom shape."],[231,"After importing the IP address and username, DGX Cloud Lepton will generate a public key for each machine and return a command to add the machines to the node group."],[973,"After installing the Python package, authenticate with your workspace by using lep login as shown below:"],[984,"After modifying and running the command above, the model deploys as an endpoint and becomes viewable in the UI."],[594,"After modifying and saving the nemotronh-pretrain.py script locally and saving the conversion script, launch the pre-training job from the terminal in your local VS Code session using the following command:"],[659,"After modifying the template script for your environment, activate the virtual environment if not already done with:"],[602,"After pre-training finishes, another task will begin to convert the final pre-trained model checkpoint to the Hugging Face format. This spins up another pod with a single GPU, which is required for conversion. The final Hugging Face model will be saved at /nemo-workspace/nemotronh_8b/huggingface. The converted Hugging Face model can be deployed with vLLM for inference."],[579,"After preprocessing, there will be 465 .bin files and 400 .idx files named nemotron-cc-N_text_document, where N corresponds to the combined data file number. Each .bin file should be approximately 40 GB in size and .idx files should be around 350 MB (sizes will vary)."],[908,"After running the example code, you should see a new project called my-awesome-project in your W&B account."],[3077,"After saving the client configuration JSON file, click DISMISS. On the\nmain Identity Federation Pool page, locate and copy the value in the\nIAM Principal field. It will look similar to:"],[1849,"After selecting the dataset, optionally add the dataset split to use for fine-tuning. Most datasets have a default split of train which is used for training, but not all datasets will have this name and it needs to be specified. For example, the nvidia/OpenMathReasoning dataset has splits of cot, tir, genselect, and additional_problems. To fine-tune against the cot split, enter cot for the split."],[1846,"After selecting the Load from storage option, the field will update with a Model file path textbox. Specify the absolute path to the directory where the model is saved in shared storage. For example, if you mounted a full NFS share as /nfs-storage in the resource configuration and your checkpoint is available at /my/custom/model inside the NFS, this path would be /nfs-storage/my/custom/model (as /nfs-storage inside the container maps to / of the NFS share). This assumes that the /nfs-storage/my/custom/model directory contains *.safetensors file(s), configs, and any other data that is required for loading the model with Hugging Face Transformers."],[3073,"After selecting the provider type, enter dgx-cloud as the provider name and\npaste the Issuer URL from the DGX Cloud Lepton console into the Issuer (URL)\nfield. Under Audiences, select Allowed audiences and enter\nhttps://kubernetes.default.svc as the allowed audience."],[1420,"After setting a new password for Jupyter, click the JupyterLab tab on the job details page to get a link to the JupyterLab session in your browser. Click the link to open JupyterLab and access the notebook in a new browser tab."],[623,"After submitting the command, it will be passed to the deployed model, which will generate a response to the prompt. Streaming is disabled for this particular request to make the results easier to read in the terminal."],[779,"After the authtoken is saved in the ngrok config, start ngrok on port 18889 using the following command:"],[1011,"After the container is built, the Gradio application can be deployed as an endpoint. Navigate to the create endpoint page on the dashboard."],[1377,"After the Dev Pod is created, view container port information on the Pod details page under the Connect tab."],[93,"After the endpoint is created, the endpoint details page shows a chat playground where you can interact with the deployed model."],[1908,"After the fine-tuning job is completed, your checkpoint will be saved in the specified shared storage location. View the logs to see where the checkpoint is located - it will be listed towards the end of the logs."],[201,"After the job has been defined in the previous step, it can be launched using the client. Since we are launching a job, we would use:"],[48,"After the job is created, you can check the status and results on the details page."],[506,"After the job is finished, the two replicas will be terminated automatically with Completed state."],[828,"After the script completes successfully, verify that SSH is properly configured:"],[3185,"After the script finishes, connect to the Dev Pod via SSH from your local terminal using the command shown in the Connection tab."],[3119,"After these configurations, create the Dev Pod by clicking the Create Pod button."],[3138,"After these configurations, create the Dev Pod by clicking the Create Pod button."],[126,"After you apply, NVIDIA will review your application and contact you."],[2058,"After you create a registry auth successfully, you can use it to access your private images for workload creation."],[3164,"After you generate a public and private key pair, click the + button, enter a name, paste the public key, and click Add SSH Public Key Secret. The key is automatically filled in after creation."],[2146,"Alerting (Configure alerts and notifications)"],[177,"All jobs require a Resource Shape to be specified. This tells the platform what resources should be allocated in the container, such as number and type of GPUs, CPU cores and memory, and local storage. To view available shapes, run lep node resource-shape in CLI version 0.26.4 or later. This returns a table of all available resource shapes in your node groups. The Shapes column shows the name of the shape. Use the desired name for the resource_shape field in the job specifications."],[1935,"All nodes with connectivity to the NFS server can read and write this data."],[318,"All outbound traffic must be allowed"],[3161,"All public keys are saved securely as a Secret in your workspace."],[3266,"All the workloads created after enabling that feature will start collecting and storing advanced logs automatically. Logs are stored for a default of 7 days. If a longer log retention period is required, contact NVIDIA support."],[917,"AlphaFold2 is a protein structure prediction model from Google DeepMind. AlphaFold2 demonstrates state-of-the-art performance at predicting protein structure from amino acid sequences."],[2538,"Alternate Format: YYYY-MM-DD HH:MM:SS\nExample: 2024-12-25 13:10:01"],[2640,"Alternate Format: YYYY-MM-DD HH:MM:SS.123456\nExample: 2024-12-25 13:10:01.123456"],[2646,"Alternate Format: YYYY-MM-DD HH:MM:SS.123456\nExample: 2024-12-25 13:10:01.123456"],[83,"An Endpoint is a running instance of an AI model that exposes an HTTP server."],[1439,"An endpoint is a running instance of an AI model that exposes an HTTP server.\nAny service can run as a dedicated endpoint. The most common use case is deploying an AI model exposed with an OpenAPI interface."],[1082,"An example of the container configuration section is shown in the image below."],[3023,"An example token (decoded) looks like this:"],[1766,"An indicator shows the latest result for each validation type: green for running, gray for completed, and red for failed."],[675,"An introduction to running NeMo-RL jobs for post-training LLMs on DGX Cloud Lepton"],[533,"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes with eight GPUs each."],[644,"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes with eight GPUs each."],[686,"An NVIDIA DGX Cloud Lepton cluster with at least 2x A100 or newer GPU nodes."],[1859,"Answer is a required field. At least one of Question or Context must be provided for input."],[1858,"Answer: This is your output field and is required. Note that it doesn't have to be a numerical/factual answer—it is simply the expected response to the prompt."],[2121,"API Key: The API key you just copied."],[81,"API Request"],[1639,"Approval Workflows"],[1976,"Approve or Reject a Request"],[1680,"Approve Reservation"],[1701,"Approve/Delete: Only Workspace Admins can approve or delete any reservation. Users may delete only those they are part of."],[1988,"Approved: The request has been granted and access is currently active"],[1347,"Archive time: How long to keep the job's logs and artifacts after completion (defaults to 3 days)."],[1415,"Archive time: How long to keep the job's logs and artifacts after the job is completed (defaults to 3 days)."],[1160,"Archive time: The time to keep the job's logs and artifacts after the job is completed. Defaults to 3 days."],[2587,"Args: id: ID of the job to clone"],[2615,"Args: id: ID of the job to get events"],[2610,"Args: id: ID of the job to start"],[2605,"Args: id: ID of the job to stop"],[2580,"Args: id: Job id to remove (exact match) name: Job name to remove\n(exact match, removes only the newest matching job)"],[2571,"Args: name: Job name to search for (exact match) id: Job id to\nsearch for (exact match) path: Optional path (file or directory) to save\nthe job spec JSON"],[2022,"As a workspace administrator, you can manage your workspace members by inviting, removing, and assigning roles to them.\nThis is an advanced feature that allows you to collaborate with your team members and control their access to the workspace."],[340,"As an example, this script implements distributed training of a convolutional neural network (CNN) on the MNIST dataset using PyTorch's DistributedDataParallel (DDP) to leverage multiple GPUs in parallel."],[360,"As an example, this script implements distributed training of a convolutional neural network (CNN) on the MNIST dataset using PyTorch's DistributedDataParallel (DDP) to leverage multiple GPUs in parallel."],[187,"As indicated in the image, lepton-shared-fs is the name of the shared storage which is mountable in containers."],[1314,"As mentioned earlier, the RayCluster holds resources until it is shut down, allowing you to submit jobs in a persistent environment. To free up resources for other tasks, or when the RayCluster is no longer needed, delete it. Click the Delete button next to the cluster as shown below."],[566,"As mentioned, this script uses the Nemotron-H tokenizer because the intent is to use this data for pre-training a Nemotron-H model. However, you can swap out the tokenizer for a different one available on Hugging Face if pre-training a different model is desired.\nFor example, you can use the Llama-3.1-8B tokenizer instead. To do this, replace the reference to nvidia/Nemotron-H-8B-Base-8K in the script with the repo ID of the Llama-3.1-8B model, meta-llama/Meta-Llama-3.1-8B.\nBe sure to accept any applicable licenses on the model repository page if you haven't already done so."],[3019,"As of the 0.42 release of DGX Cloud Lepton, all workloads (Dev Pods, Batch, Endpoints) project an identity token\ninto the workload containers. The projected token can be used to authenticate the workload to common services in major\ncloud providers (for example, AWS, GCP, Azure) through workload federation."],[208,"As part of DGX Cloud Lepton, you will receive an entitlement for the NVIDIA Enterprise Support Portal (ESP) from your NVIDIA Technical Account Manager (TAM). You will need to register for ESP access following the instructions provided by your TAM."],[1378,"As shown in the image, since we configured Proxy for port 18889, an endpoint URL was generated for it."],[1302,"As shown, there are two nodes in the cluster with a total of 80 CPU cores and 4 GPUs available for scheduling jobs."],[1071,"As stated in the paper, Qwen2.5-Math-1.5B only has a context length of 4,096 tokens which is insufficient for reasoning models which typically generate several thousands of output tokens. To support the longer reasoning traces, we need to manually update the base model to increase its output sequence length."],[1128,"As the model is training, you should find that your training loss value decays over time. In our testing, the loss was 0.33 after 100 steps, 0.31 after 200 steps, 0.30 after 300 steps, and so on."],[1865,"As training progresses, checkpoints will be periodically saved after a configurable number of steps. All checkpoints are in the *safetensors format for compatibility with Hugging Face and inference serving engines. Inside the checkpoint directory, there will be several files and sub-directories pertaining to the fine-tuning job. In particular, you will find several directories named epoch_X_step_YYYY where X is the current epoch number and YYYY is the current global step. Each of these directories contain model information from the indicated step at that time. The checkpoint can be found in the model/consolidated sub-directory within each epoch_X_step_YYYY directory. For example, if you mounted your shared storage as /nfs-storage and specified your checkpoint path as /nfs-storage/fine-tuning/my-fine-tune-model, the full checkpoint would be saved in /nfs-storage/fine-tuning/my-fine-tune-model/epoch_0_step_1000/model/consolidated. This will be the same any time the checkpoint is saved, so after another 1,000 steps, you will also have /nfs-storage/fine-tuning/my-fine-tune-model/epoch_0_step_2000/model/consolidated."],[379,"As we just want to test the job failure diagnosis feature, we can create a job with any desired GPU card and corresponding node group."],[1851,"As with loading a model from storage, loading datasets from storage requires the dataset to be saved and pre-processed on shared storage that was mounted in the Resources section above. Currently, only .jsonl datasets are supported. The dataset needs to include at least one input and an output field. This must be done prior to launching the fine-tuning job."],[497,"As you can see, there are many configurations you can fill in, like name, resource, image, etc. You can find a more detailed guide in documentation for creating a job."],[3034,"Assign IAM Roles to Kubernetes Service Accounts"],[3040,"Assign IAM roles to Kubernetes service accounts – Complete this procedure for each unique set of permissions that you want an application to have."],[2134,"Assigning Roles to Members"],[738,"Assume you have already set up a Dev Pod running with a Conda-installed image and filesystem mounted at /mnt.\nYou can create a Conda environment with PyTorch and pack it to the filesystem."],[928,"At least 1.3 TB of available SSD storage"],[929,"At least 24 CPU cores"],[1289,"At least one GPU node and one CPU resource"],[1817,"At the top of the form is an option for selecting the fine-tuning technique, either Supervised Fine-Tuning (SFT) or LoRA which is a Parameter-Efficient Fine-Tuning (PEFT) technique. SFT is selected by default."],[3189,"Audit Logs"],[2147,"Audit Logs (View and export audit logs)"],[3194,"Audit logs record actions and API calls performed by members of your workspace.\nIt helps track the changes made by members in your workspace."],[515,"Authenticate with DGX Cloud Lepton"],[634,"Authenticate with DGX Cloud Lepton"],[951,"Authenticate with the DGX Cloud Lepton CLI"],[142,"Authenticating with private container registries"],[1552,"Auto Pause Schedule"],[1553,"Auto Reboot"],[1659,"Auto-Approval: Admins can define auto-approval thresholds based on node count. Requests within the limit are automatically granted."],[2008,"Automatic Time Limit: Access is automatically limited to the duration specified in the request."],[1448,"Autoscale replicas based on GPU utilization: Specify the minimum and maximum number of replicas and the target GPU utilization percentage."],[1447,"Autoscale replicas based on traffic QPM: Specify the minimum and maximum number of replicas and the target queries per minute (QPM). You can also specify the HTTP methods and request paths to include in traffic metrics."],[1425,"Autoscaling"],[2133,"Available Resources"],[323,"Avoid using or relying on IP addresses from the following CIDR ranges:"],[3032,"AWS - IAM Roles for Service Accounts (IRSA)"],[463,"AWS access credentials (access key ID and secret access key) with permissions to read from the S3 bucket"],[3030,"AWS Identity Federation"],[2038,"Azure"],[2054,"Azure Container Registry (ACR)"],[616,"Back in the Endpoints page you will see the newly created inference endpoint. It will take some time for the model to be loaded from storage before it transitions to the Ready state. Once the deployment is ready, it can handle inference requests."],[246,"Background"],[1952,"Based on the volume directory layout shown below, we will use an example to demonstrate rule precedence."],[1164,"Basic Environment Variables"],[138,"Basic Python SDK flow"],[1290,"Basic understanding of the Ray distributed computing framework"],[34,"Batch Job"],[1133,"Batch Job Configurations"],[161,"Batch Jobs"],[2191,"Batch Jobs"],[9,"Batch Jobs: Run distributed training or batch processing jobs with high-performance interconnects and accelerated storage."],[2200,"Batch Jobs: Run training, data processing, and other batch jobs."],[1699,"Be made for all users in the workspaceOtherwise, the request will be rejected with the error:\"Reservations created by non-admin users must contain themselves or allow all users.\""],[3121,"Before accessing your JupyterLab instance, use the following command to set a new token for JupyterLab in the Terminal tab:"],[1942,"Before adding static NFS volume, please make sure NFS is already mounted on all nodes in the node group with the same mount path."],[124,"Before creating a workspace on DGX Cloud Lepton, you need a valid NVIDIA Cloud Account (NCA) and access to the DGX Cloud Lepton platform.\nAfter logging into DGX Cloud Lepton with your NCA, a workspace is generated automatically."],[2112,"Before creating the registry auth on DGX Cloud Lepton, you need to create an API key for your NVIDIA account.\nNavigate to the API Keys page, click on Generate Personal Key."],[2074,"Before creating the registry auth on DGX Cloud Lepton, you need to obtain the service account key JSON file."],[1364,"Before diving into how to configure container ports, let's understand what they are."],[2004,"Before proceeding, verify that the requested role and duration are appropriate."],[815,"Before running the script, configure the following parameters:"],[2049,"Besides public registries like Docker Hub, you can also bind your private registries to your workspace. This allows you to store and deploy your custom images in a private space."],[2123,"Besides the presets, you can also configure a custom registry for your workspace."],[1503,"Besides the registry auth key, you also need an NGC API key. Navigate to the NGC API key creation page and click Generate Personal Key."],[1588,"Best Effort: Allocate resources according to workload priority as much as possible."],[1644,"Best Practices"],[629,"Blakeman, A., Basant, A., Khattar, A., Renduchintala, A., Bercovich, A., Ficek, A., Bjorlin, A., Taghibakhshi, A., Deshmukh, A. S., Mahabaleshwarkar, A. S., Tao, A., Shors, A., Aithal, A., Poojary, A., Dattagupta, A., Buddharaju, B., Chen, B., Ginsburg, B., Wang, B., ... Chen, Z. (2025). Nemotron-H: A family of accurate and efficient hybrid Mamba-Transformer models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.03624"],[1970,"Break Glass Access for Support"],[439,"Breaking down each of the flags:"],[115,"Bring Your Own Compute"],[248,"Bring Your Own Compute (BYOC) allows you to bring your own compute resources to DGX Cloud Lepton. This is useful if you have existing resources you want to use with DGX Cloud Lepton."],[111,"Bring Your Own Compute (BYOC) allows you to bring your own compute resources to DGX Cloud Lepton. This is useful if you have existing resources you want to use with DGX Cloud Lepton.\nFollow the steps on the Create BYOC Node Group page to get started."],[21,"Bring Your Own Compute (BYOC) is a feature that enables you to use your existing hardware infrastructure with Lepton's platform. This approach offers several advantages:"],[11,"Bring Your Own Compute: Connect your own infrastructure to DGX Cloud Lepton to supercharge your cloud experience on any infrastructure."],[845,"Build Container Image"],[992,"Build Custom Dia Container"],[852,"Build Your Container Image"],[43,"By configuring the above settings, you can create a batch job that:"],[113,"By default, a new node group is empty. To add capacity, bring your own compute by clicking Add Machines under the Nodes tab."],[2183,"By default, all secrets are created as private."],[1444,"By default, DGX Cloud Lepton creates your endpoints with a single replica and automatically scales down to zero after one hour of inactivity."],[1748,"By default, DGX Cloud Lepton generates and enables default shapes for each node group, based on the accelerator type selected when creating the node group.\nIf these default shapes aren't suitable for your workload, and you want to use only custom shapes, you can disable default shapes."],[968,"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. The token appears in the UI alongside your deployed endpoint once it is ready."],[1017,"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. This will be shown in the UI alongside the deployed endpoint once ready."],[1049,"By default, endpoints require authentication with requests. This can be changed in the Access Tokens section. A random token will be generated for requests if access tokens are enabled. This will be shown in the UI alongside the deployed endpoint once ready."],[3258,"By default, if Advanced logs are enabled, generated logs are stored for 7 days. If a longer log retention period is required, contact NVIDIA support."],[3265,"By default, the advanced logs feature is disabled in your workspace. You need to enable it manually in the settings page."],[92,"By default, the endpoint is public and can be accessed by anyone with the URL. Refer to the endpoint configurations for managing endpoint access control."],[1453,"By default, the endpoint uses the Public access policy without token authentication, which means it is accessible from any IP address."],[1466,"By default, two types of probes are configured:"],[837,"By following this example, you will have:"],[108,"BYOC Node Group: Uses your own compute resources."],[250,"BYOC Node Group: Uses your own compute resources."],[265,"BYOC Requirements"],[1144,"Can be preempted by higher priority workload: Whether the job can be preempted by higher-priority workloads. Defaults to false."],[1331,"Can be preempted by higher priority workload: Whether the pod can be preempted by higher-priority workloads (defaults to false)."],[1143,"Can preempt lower priority workload: Whether the job can preempt lower-priority workloads. Defaults to false."],[1330,"Can preempt lower priority workload: Whether the pod can preempt lower-priority workloads (defaults to false)."],[3056,"Capture your AWS account ID to a variable:"],[830,"Check Authorized Keys: Verify that the authorized_keys file is present and contains your public key"],[834,"Check Logs: View JupyterLab logs at /var/log/jupyter.log"],[833,"Check Process: Verify that JupyterLab is running with pgrep jupyter-lab"],[829,"Check SSH Directory: Ensure the .ssh directory exists in the root directory"],[694,"Check the Enable privileged mode box in the Resource form."],[1005,"Check the Enable privileged mode box in the Resource form."],[1110,"Checkpoint Every Steps: Enter 500 to save a checkpoint every 500 steps. This will save a checkpoint of the current model weights to the checkpoint directory specified earlier. On eight H100 GPUs, this will save a checkpoint approximately once every 6 hours."],[1878,"Checkpoint Every Steps: This is how frequently the fine-tuning job will save a checkpoint. After every N steps, the checkpoint will be saved in the epoch_X_step_YYYY/model/consolidated directory within the checkpoint directory. Minimum: 1. Default: 1000."],[1803,"Checkpoint Output Path"],[3054,"Choose Add provider."],[1619,"Choose from two drain policies:"],[1623,"Choose from two release policies:"],[1372,"Choose how to expose the port:"],[1824,"Choose LoRA if:"],[1502,"Choose NVIDIA as the registry type and paste the registry auth key in the API Key field."],[527,"Citations"],[1063,"Citations"],[526,"Clean Up"],[640,"Clean Up"],[1742,"CLI version 0.26.4 or newer is required."],[466,"Click + New Secret and select Custom Secret."],[2141,"Click Add Permissions and select the resource you want to provide permissions for."],[1731,"Click Add Shape in the top right to open a modal where you can define the shape's name, description, GPU, CPU, and memory."],[471,"Click Add to save."],[1651,"Click Create Reservation."],[2144,"Click Create Role."],[2078,"Click CREATE to automatically download the JSON file."],[382,"Click Create to create the job.\nOnce the job is created, you can view the job in the Lepton Dashboard."],[88,"Click Create to deploy an endpoint that:"],[615,"Click Create to deploy the model."],[1255,"Click Create to launch your job."],[1270,"Click Create to launch your job."],[477,"Click CREATE to submit your job to the queue. The job will automatically start when cluster resources become available."],[2118,"Click Generate Personal Key to create the API key successfully. Copy and save the API key securely. You will not be able to access it after closing the page."],[2066,"Click Generate Token to create the new access token successfully.\nCopy and save the token securely. You will not be able to access it after closing the page."],[1598,"Click Image Acceleration under the config menu to open a configuration modal."],[49,"Click Logs under the Replicas tab to see the logs for each replica. Because the worker count is one, there will be only one replica, and the logs should display the following:"],[504,"Click on the Create button, then you can see the job status in the detail page of the job.\nThe job will proceed once two replicas are in the \"Ready\" state, which will take a few minutes."],[2102,"Click on the Generate token button to create the access token."],[1576,"Click Quota under the config menu to open the Node Group Quota page."],[1570,"Click Recovery Policy under the config menu to open a modal for configuration."],[1568,"Click Scheduling Policy under the config menu to open a modal where you can configure scheduling."],[2139,"Click the Add Role button."],[729,"Click the Create button at the bottom of the field to schedule the job. Once resources are available, DGX Cloud Lepton will pull the custom container and create the simple RayCluster and run the specified job directly."],[712,"Click the Create button at the bottom of the field to schedule the job. Once resources are available, DGX Cloud Lepton will pull the custom container and create the simple RayCluster."],[698,"Click the Create button at the bottom of the page to run the dev pod and build the image. This process can take 30 minutes or longer depending on connection speed and processing power. Progress can be monitored in the job logs in the UI."],[1009,"Click the Create button at the bottom of the page to run the dev pod and build the image. This process can take 30 minutes or longer depending on connection speed and processing power. Progress can be monitored in the job logs in the UI."],[1582,"Click the edit button in the top-right corner of the card to configure:"],[235,"Click the Generate Command button in the pop-up window."],[230,"Click the Import button to start importing."],[3262,"Click the Logs button to view the real-time logs."],[241,"Click the menu button for the node and select the Release option."],[1767,"Click the name field in the validation table row to open the validation result page, where you can view results and logs."],[1765,"Click the Run node validation dropdown and select the validation type you want to run."],[2211,"Click the Templates dropdown in the top right corner and select the target template."],[1397,"Clicking a notebook opens a read-only preview of the full notebook, providing a deeper look at what it accomplishes, as shown below."],[1303,"Clicking Dashboard opens a new tab with the Ray dashboard for the cluster, where you can view recent jobs, utilization, resource availability, and more."],[1116,"Clicking the job will open the job details page which includes more information about the job including listing the settings that were specified during creation."],[2492,"clone : Creates a copy of an existing job by its ID."],[2694,"Columns: - Node Group: name on first line, id on second line - Shapes:\none shape per block, first line is name, second line shows colored\navailability for pod/endpoint/job - Detailed: description and colored\nresource details"],[2231,"Commands"],[2250,"Commands"],[2410,"Commands"],[2449,"Commands"],[2623,"Commands"],[2675,"Commands"],[2702,"Commands"],[2782,"Commands"],[2880,"Commands"],[2913,"Commands"],[2928,"Commands"],[2098,"Complete the following fields:"],[46,"Completes after approximately 20 seconds and is archived after 3 days"],[10,"Compute: Use managed infrastructure or bring your own—and manage both with ease."],[736,"Conda is known for its ability to create isolated environments for different projects.\nWhen running jobs on DGX Cloud Lepton, you can use Conda to ensure your code runs with the exact dependencies needed, making your workloads more reliable and reproducible."],[2189,"Configuration"],[804,"Configuration Parameters"],[1353,"Configure Container Ports"],[1351,"Configure container ports for your Dev Pod"],[3035,"Configure DGX Cloud Lepton Workloads to Use a Kubernetes Service Account"],[3041,"Configure DGX Cloud Lepton workloads to use a Kubernetes service account – Complete this procedure for each workload that needs access to AWS services."],[1472,"Configure header‑based replica routing. By default, requests are load‑balanced across all replicas."],[1601,"Configure the \"Cache Threshold\" in GB. This is the upper bound of root disk space available for image caching. If not specified, up to 80% of root disk space may be used."],[467,"Configure the secret:"],[51,"Configure your Batch Job"],[72,"Configure your Dev Pod"],[3068,"Configure Your Workload"],[1060,"Configuring a Fine-Tuning Job"],[792,"Connect to the Dev Pod with VS Code"],[1276,"Connecting to the RayCluster"],[890,"Connecting to Weights & Biases"],[1213,"Construction Method"],[336,"Container"],[356,"Container"],[373,"Container"],[1136,"Container"],[1322,"Container"],[1387,"Container"],[677,"Container Building"],[414,"Container Image"],[1558,"Container Image Acceleration"],[1565,"Container Image Acceleration"],[433,"Container Image: Specify the private container image in the Container section. For this example, use the default image."],[1079,"Container Image: The default base container should be sufficient. In this example, nvcr.io/nvidia/pytorch:25.08-py3 is used."],[1365,"Container Port: The port your application listens on inside the container (for example, your web server runs on port 8080)."],[1350,"Container Ports"],[1360,"Container ports allow you to expose services running inside your Dev Pod to the outside world. By default, applications running in a container are isolated and cannot be accessed externally. Configuring container ports creates a bridge between your containerized applications and external networks."],[1341,"Container Ports: Ports to expose from the container (maximum of 3). See this guide for details."],[1149,"Container Ports: The ports that the container will listen on. In this field, you can add multiple ports, and each port can be specified with a protocol (TCP, UDP, or SCTP) and a port number."],[1294,"Container: The RayCluster runs a container on the head node and each worker. The container specified here runs on all nodes in the RayCluster.To use an official Ray image, select a tag from the Built‑in list. Tag format: ray:<ray version>-<python version>-<gpu> where <ray version> is the Ray version, <python version> is the Python version, and <gpu> indicates GPU support.To use a custom container, select Custom under Image and specify the image ID. Ensure Ray is installed in the image. Then select the corresponding Ray Version."],[1106,"Context Parallelism Size: Enter 2 to split the input sequences across two GPUs, each to reduce the GPU memory requirement for processing longer sequence lengths."],[1871,"Context Parallelism Size: This splits your context across GPUs to lower GPU memory usage. This is useful for long input and/or output sequences in your dataset, particularly those that include reasoning traces as it reduces the amount of memory required for each GPU to process the prompts. This must be a power-of-two (1, 2, 4, 8, ...). This multiplied by the Tensor Parallelism Size must be less than or equal to the total number of GPUs for your job. If you have long input or output sequences, typically 32K or greater in length, it is recommended to add context parallelism. Minimum: 1. Default: 1."],[1856,"Context: Add this field if you have required context for the prompt. An example is a paragraph with contextual information, then a Question and Answer which can be found directly in that context. If no context is included, this can be left blank."],[3184,"Copy and paste the script into the terminal, then press Enter to execute it."],[592,"Copy and save the following Python script to scripts/convert.py:"],[2103,"Copy and save the token securely. You will no longer be able to access it after closing this page."],[232,"Copy the command and run it on the machine to register the public key. DGX Cloud Lepton will use this key to handle the rest of the process."],[237,"Copy the command and run it on the machine you want to add. This command will install GPUd and use GPUd to join the node group."],[709,"Copy the following into the Run Command field. Note, if a discrete head node is desired, change the ray start --head --port=6379 --block line to be ray start --head --port=6379 --block --num-cpus 0 --num-gpus 0. This will ensure jobs don't get scheduled on the head node."],[725,"Copy the following into the Run Command field. Note, if a discrete head node is desired, change the ray start --head --port=6379 line to be ray start --head --port=6379 --num-cpus 0 --num-gpus 0. This will ensure the job doesn't run on the head node."],[3083,"Copy the JSON client configuration clientLibraryConfig.json file you downloaded to your\nworkload container (perhaps building the file into your container image). Set the environment\nvariable GOOGLE_APPLICATION_CREDENTIALS to point to it, log in with the credential, and set\nthe default GCP project. At this point, the GCP resources authorized through IAM will\nbe accessible using either gcloud or gsutil commands."],[3168,"Copy the SSH connection command and run it in your local terminal to access the Dev Pod via SSH. The private SSH key corresponding to the configured public key must be available and referenced in the SSH command via the ssh -i parameter."],[277,"CPU Requirements"],[1593,"CPU: Total CPU quota for the user (default: unlimited)."],[1584,"CPU: Total CPU quota for the workspace (default: unlimited)."],[374,"Create"],[2426,"create : Create an ingress"],[2493,"create : Creates a job."],[2724,"create : Creates a pod with the given resource shape, mount, env and..."],[2804,"create : Creates a Ray cluster from either a spec file (spec only) or..."],[2284,"create : Creates an endpoint from either a photon or container image."],[2893,"create : Creates secrets with the given name and value."],[392,"Create 10 jobs"],[103,"Create a BYOC Node Group"],[247,"Create a BYOC Node Group"],[850,"Create a Dev Pod"],[56,"Create a Dev Pod from a Container Image"],[857,"Create a dev pod with the node group you want to use.\nMake sure the dev pod is created with the privilege mode enabled in the Resource selection step.\nIt is recommended to use the whole node for the dev pod with privilege mode enabled. Eg. select the shape with 8 gpus will use the whole node. For configurations, refer to this guide."],[790,"Create a Dev Pod with VS Code as Run Command"],[1772,"Create a domain"],[883,"Create a file named /tmp/hostfile.txt on Pod 1. Each line should contain the private IP address of the nodes where these pods are running."],[511,"Create a Hugging Face Account"],[450,"Create a Job on DGX Cloud Lepton"],[735,"Create a Job with Conda environment"],[1086,"Create a new fine-tuning job by selecting the green Create Fine-tune button in the Fine-tuning job page."],[28,"Create a Node Group: Before importing machines, create a node group to organize your compute resources."],[749,"Create a pod with the following run command depending on the CPU architecture of the Dev Pod, for configurations, refer to this guide:"],[794,"Create a pod with the following run command, for configurations, refer to this guide:"],[1285,"Create a RayCluster"],[814,"Create a setup script that will install and configure SSH service and JupyterLab on your PyTorch container. Here's the complete script template:"],[2188,"Create a Template"],[513,"Create a Weights & Biases Account"],[2031,"Create Access Token"],[2042,"Create Access Token"],[78,"Create an Endpoint"],[3039,"Create an IAM OIDC provider for your cluster – You only complete this procedure once for each cluster."],[2433,"Create an ingress"],[775,"Create an ngrok account for port forwarding.\nGo to the ngrok signup page and follow the instructions to create an ngrok account."],[337,"Create and Monitor"],[357,"Create and Monitor"],[2187,"Create and use templates for dev pods and batch jobs in DGX Cloud Lepton, with customizable placeholders for flexible configuration."],[2045,"Create API Key"],[456,"Create AWS Credentials"],[2040,"Create Azure Registry Auth"],[36,"Create Batch Job"],[32,"Create BYOC Node Group"],[244,"Create BYOC Node Group"],[2048,"Create Custom Registry Auth"],[1442,"Create Dedicated LLM Endpoint"],[1424,"Create Endpoint"],[1477,"Create Endpoint"],[1497,"Create endpoint from NVIDIA NIM"],[914,"Create Endpoint from NVIDIA NIM"],[1027,"Create Endpoint from NVIDIA NIM"],[100,"Create endpoints from container image"],[1475,"Create Endpoints from Container Image"],[98,"Create endpoints from NVIDIA NIM"],[1492,"Create Endpoints from NVIDIA NIM"],[1443,"Create from Container Image"],[57,"Create from Notebooks"],[1441,"Create from NVIDIA NIM"],[332,"Create Job through Dashboard"],[352,"Create Job through Dashboard"],[99,"Create LLM endpoints"],[1516,"Create LLM Endpoints"],[1519,"Create LLM Endpoints with SGLang"],[1518,"Create LLM Endpoints with vLLM"],[2028,"Create New Registry Auth"],[2043,"Create New Registry Auth"],[2037,"Create New Registry Auth on DGX Cloud Lepton"],[3033,"Create OIDC Provider (AWS Console)"],[2032,"Create Registry Auth"],[2046,"Create Registry Auth"],[1679,"Create Reservation"],[2035,"Create Service Account for Registry Auth"],[2036,"Create Service Account Key"],[457,"Create the Batch Job"],[371,"Create the Job"],[388,"Create the Job on DGX Cloud Lepton"],[389,"Create the job using the DGX Cloud Lepton CLI"],[820,"Create the Pod: Complete the pod creation with your desired resource configuration."],[482,"Create Training Job"],[105,"Create Workloads"],[118,"Create Workspace"],[1697,"Create: Any workspace user can create a reservation, but the request must either:"],[2585,"Creates a copy of an existing job by its ID."],[2505,"Creates a job."],[2730,"Creates a pod with the given resource shape, mount, env and secret."],[2814,"Creates a Ray cluster from either a spec file (spec only) or CLI flags. If\nboth are provided, CLI flags override values from the file."],[2294,"Creates an endpoint from either a photon or container image."],[2896,"Creates secrets with the given name and value. The name and value can be\nspecified multiple times to create multiple secrets, e.g.: lep secret\n  create -n SECRET1 -v VALUE1 -n SECRET2 -v VALUE2"],[1275,"Creating a RayCluster"],[1637,"Creating a Reservation"],[2132,"Creating Custom Roles"],[2215,"Creating Tokens"],[269,"CUDA Toolkit"],[3025,"Currently, the only audience (aud) available is https://kubernetes.default.svc.\nAll federations should be configured to accept this audience as the allowed audience."],[1602,"Currently, this feature is not available if nodes have the Docker subsystem installed. We recommend not installing Docker (Lepton installs Containerd). This limitation will be removed in a future release."],[3216,"Currently, we only support exporting logs to Datadog."],[3220,"Currently, we only support exporting metrics to Datadog."],[2051,"Currently, we support the following private registry presets:"],[748,"Cursor is a powerful AI-assisted code editor built on VS Code that can connect to a Dev Pod for remote development using DGX Cloud Lepton resources. In this guide, we will show you how to connect to a Dev Pod with Cursor via Remote Tunnels."],[2047,"Custom"],[3176,"Custom entrypoint - Select this option if you want to manually specify the run command for the Dev Pod (to run a custom start-up script, for example)."],[3128,"Custom entrypoint - Select this option if you want to specify the JupyterLab install or run command. For an example custom entrypoint, refer to the screenshot and code example below."],[1339,"Custom entrypoint: Specify a custom entrypoint for the container. This overrides the default entrypoint."],[1717,"Custom Shapes"],[1724,"Custom shapes defined by the node group administrator."],[1937,"Data durability relies on the backup and reliability features provided by the underlying NFS server."],[309,"Data volume: Minimum 640GB per GPU (e.g., using LVM)"],[303,"Data: At least 20 TB of NVMe SSD (can be multiple disks)."],[1801,"Dataset Mapping"],[1798,"Datasets"],[1848,"Datasets can be loaded directly from Hugging Face hub by clicking the Load from Hugging Face option and specifying the repo ID of a dataset, such as nvidia/OpenMathReasoning. When the job launches, this will download the requested dataset into local container storage and will be pre-processed before fine-tuning begins. You can also select a dataset from the drop-down list which contains several of the most popular datasets. Note that some of these datasets might not be a good target for fine-tuning, so only select datasets that you have inspected previously."],[1763,"DCGM Diagnostics"],[1716,"Default GPU Shapes"],[1723,"Default GPU shapes provided by Lepton."],[3157,"Default images provided by DGX Cloud Lepton come with SSH service pre-configured. You only need to add your SSH public key during the creation process."],[3009,"Default Limit"],[184,"Define the job by passing the job spec and giving it a name"],[140,"Define the task to perform"],[155,"Define the task to perform"],[2427,"delete : Delete an ingress by name"],[2441,"Delete a ingress by name"],[1681,"Delete Reservation"],[1640,"Deleting Reservations"],[1663,"Deletion can be performed by:"],[589,"Depending on how many resources you have available, you can also change the number of nodes used for pre-training by modifying this line:"],[911,"Deploy AlphaFold2 NIM"],[942,"Deploy GPT-OSS-120B"],[952,"Deploy the Endpoint via CLI"],[523,"Deploy the Model for Inference"],[1809,"Deploying the Model for Inference"],[3237,"Deployment name, only available for metrics from lepton endpoint and dev pod"],[90,"Deploys the nvidia/Nemotron-Research-Reasoning-Qwen-1.5B model with vLLM"],[1168,"Description"],[3010,"Description"],[3233,"Description"],[3273,"Description"],[1738,"Description (Optional): An optional field to provide additional details or context about the shape."],[3200,"Destination Type"],[3204,"Destination Type"],[1793,"Determining GPU Resources"],[54,"Dev Pod"],[1319,"Dev Pod Configurations"],[1337,"Dev Pod entrypoint: Automatically applies a run command based on the selected image."],[162,"Dev Pods"],[2190,"Dev Pods"],[2199,"Dev Pods: Interactive coding with SSH, Jupyter, or your favorite tool."],[8,"Dev Pods: Run interactive development sessions—including SSH, Jupyter notebooks, and VS Code—with managed GPUs in the cloud."],[396,"DGX Cloud Lepton"],[1559,"DGX Cloud Lepton allows you to configure the node group, including the following settings:"],[20,"DGX Cloud Lepton allows you to seamlessly integrate your own compute infrastructure, providing enterprise-grade workload management capabilities."],[127,"DGX Cloud Lepton can integrate with an external SSO/IdP to centralize user authentication and manage access to NVIDIA cloud services. To get started, refer to NGC External SSO. Contact your NVIDIA TAM if you have questions."],[1362,"DGX Cloud Lepton does not expose any ports by default for security reasons."],[3113,"DGX Cloud Lepton does not expose any ports by default for security reasons."],[3130,"DGX Cloud Lepton does not expose any ports by default for security reasons."],[1812,"DGX Cloud Lepton Fine-Tuning is designed to support a wide range of models including those built on some of the most popular architectures, such as Gemma 3, Llama, Mixtral/Mistral, Qwen3, Nemotron, and more. Base models can either be loaded directly from Hugging Face Hub, or loaded from mounted storage on DGX Cloud Lepton."],[5,"DGX Cloud Lepton is a fully managed AI platform designed for developing, training, and deploying AI models.\nIt offers production-grade performance, cost efficiency, comprehensive ML tooling, and flexible GPU options backed by enterprise SLAs."],[1317,"DGX Cloud Lepton is currently in early access support of Slurm clusters. Slurm clusters are provisioned by request."],[84,"DGX Cloud Lepton lets you deploy AI models as endpoints, making them accessible via high-performance, scalable REST APIs."],[376,"DGX Cloud Lepton provides a built-in feature to automatically diagnose job failures.\nThis feature is designed to help users quickly identify and resolve issues in batch jobs, ensuring optimal performance and reliability."],[1450,"DGX Cloud Lepton provides a built‑in access control system for your endpoints. You can create an endpoint with one of the following access control policies:"],[3193,"DGX Cloud Lepton provides a comprehensive observability platform to help you monitor and troubleshoot your applications, including audit logs."],[3251,"DGX Cloud Lepton provides a comprehensive observability platform to help you monitor and troubleshoot your applications, including real-time logs and advanced logs."],[1811,"DGX Cloud Lepton provides a no-code interface for fine-tuning large language models (LLMs). Powered by NVIDIA NeMo Automodel, users can fine-tune most LLMs available on Hugging Face Hub with support for dozens of the most popular model architectures. Fine-tuning on DGX Cloud Lepton provides an easy way to experiment with tailoring the latest open source models to your unique needs. The tool runs directly on your hardware connected to DGX Cloud Lepton, providing a single-pane-of-glass for training, tuning, and deploying models in your own private workspace."],[1725,"DGX Cloud Lepton provides a set of default GPU shapes, designed for general-purpose workloads and focused on GPU allocation."],[218,"DGX Cloud Lepton provides a simple way to manage all your BYOC nodes in the node group page."],[1757,"DGX Cloud Lepton provides a simple way to run validations to check node group health, including:"],[3109,"DGX Cloud Lepton provides a simplified process for enabling JupyterLab for default container images. This requires the following two options:"],[61,"DGX Cloud Lepton provides a solution called Dev Pods—lightweight, container-based AI development environments."],[1929,"DGX Cloud Lepton provides a storage solution that allows you to store and access your data in a secure and scalable way."],[3209,"DGX Cloud Lepton provides a way to export your logs and metrics data to your own storage or analytics platform."],[769,"DGX Cloud Lepton provides an easy way to automatically install and launch JupyterLab in your Dev Pod container. This requires the following two options:"],[1626,"DGX Cloud Lepton provides health checks to monitor every node across the following dimensions:"],[128,"DGX Cloud Lepton provides many features to help you manage your workspace. Learn more:"],[1614,"DGX Cloud Lepton provides node-level operations to help you manage nodes more effectively."],[1234,"DGX Cloud Lepton provides two templates to help you get started quickly:"],[339,"DGX Cloud Lepton supports MPI for distributed training. Here is an example of running a distributed MPI job with two workers on DGX Cloud Lepton."],[1280,"DGX Cloud Lepton supports the creation of RayClusters to manage the scheduling and scaling of machine learning workloads using the open source Ray framework."],[146,"DGX Cloud Lepton supports the REST API protocol and includes a Python SDK for interacting with workspaces. Common tasks include monitoring and launching batch jobs and endpoints. This document provides an overview of how to interact with the Python SDK for DGX Cloud Lepton."],[1722,"DGX Cloud Lepton supports two types of resource shapes:"],[1671,"DGX Cloud Lepton supports workload preemption to manage contention:"],[238,"DGX Cloud Lepton will automatically validate the nodes after adding them and display their status in the node list."],[298,"DGX Cloud Lepton will install GPUd, containerd, kubelet, and tailscale packages during the BYOC installation process."],[3058,"DGX Cloud Lepton, by default, will prepend the namespace with ws-, so the workspace ID xyzpdq123\nwill become the Kubernetes namespace ws-xyzpdq123. Next, define some variables for the Kubernetes-related\nresources that will be used later. Specify a different $BUCKET_NAME if you used a custom name in the AWS console."],[1001,"Dia includes a Dockerfile with all dependencies for the demo application. The container must first be built and pushed to a container registry so it can be deployed as an endpoint on DGX Cloud Lepton. The container can be built directly on DGX Cloud Lepton using a dev pod. The full process for building a container using a dev pod can be found here."],[996,"Dia is a lightweight open source text-to-speech (TTS) model developed by Nari Labs which generates realistic audio with fine-grained control over emotion and tone. Their GitHub repository includes a sample frontend application built in Gradio to make it easy to interact with the models. This guide shows how to build their custom Docker container on DGX Cloud Lepton, deploy the Gradio application as an endpoint, and interact with the model via the application."],[989,"Dia Text-to-Speech Example"],[375,"Diagnose Job Failure"],[1719,"Disable or Enable Default Shapes"],[1159,"Disable retry when program error occurs: If enabled, the job will not be retried if a program error is detected in the logs."],[2,"Discover the NVIDIA DGX Cloud Lepton AI platform for development, with endpoints, dev pods, batch jobs, and enterprise solutions."],[329,"Distributed Training with MPI"],[349,"Distributed Training with PyTorch"],[319,"DNS server must be properly configured"],[2030,"Docker Hub"],[2052,"Docker Hub"],[854,"Docker image is widely used as environment control. In this guide, we will show you how to build a container image in a Dev Pod."],[1770,"Domains"],[2148,"Domains (Configure ingress rules and networking)"],[1777,"Domains manage traffic routing to your endpoints. When you create an endpoint, a default domain is automatically created. You can also create domains manually to route traffic to multiple endpoints based on assigned weights, or to shadow a percentage of traffic to a different deployment for testing."],[1609,"Drain"],[1621,"Drain: Evicts all pods from the node and terminates any active workloads."],[1996,"Duration: How long the access will be active once granted"],[1986,"Each access request can be in one of the following states:"],[1632,"Each health check card includes a trigger button with a play icon. Hover over the icon to see a tooltip showing when the check last ran."],[1940,"Each node group has its own Node Local Volume list and Static NFS Volume list. Add storage for each node group separately by clicking the button in the top right corner of the section."],[1401,"Each notebook may have different requirements. Refer to the Requirements section at the top of the notebook preview for the number of GPUs and any additional requirements."],[1866,"Each of these consolidated directories contain the full *safetensors files and associated configs so they can be deployed for inference. If you choose to deploy your model or publish it on Hugging Face, you must specify the consolidated directory."],[176,"Each of these resources has a specific template they expect for the API request. For example, the Batch Jobs API expects a job to have a leptonai.api.v1.types.job.LeptonJob type for submission. Similarly, Endpoints (also known as \"Deployments\" in the SDK), expect a leptonai.api.v1.types.deployment.LeptonDeployment object for submission, as do Dev Pods which are handled similarly to Endpoints by the backend. The list of API specs can be found here. Open the file for the specific task you need and review its specification."],[3007,"Each replica on DGX Cloud Lepton has a default PID limit that controls the maximum number of processes that can run within a single container."],[317,"ECC (Error-Correcting Code) support is recommended."],[1070,"Eight (8) GPUs with 80+ GB of GPU memory"],[3038,"Enable IAM roles for service accounts by completing the following procedures:"],[3093,"Enable JupyterLab"],[74,"Enable JupyterLab in a Dev Pod"],[1343,"Enable JupyterLab Proxy: Whether to enable JupyterLab Proxy port configuration (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."],[1408,"Enable JupyterLab: Defaults to True to enable a direct connection to a JupyterLab session inside the container."],[1334,"Enable JupyterLab: Whether to enable JupyterLab in the pod (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."],[3207,"Enable Metrics Export When Creating Workload"],[1704,"Enable preemption thoughtfully to maintain fairness and responsiveness."],[1342,"Enable SSH Host Network: Whether to enable SSH Host Network port configuration (defaults to false). Available for default images with Dev Pod entrypoint. See this guide for details."],[3102,"Enabling JupyterLab for Custom Images"],[3095,"Enabling JupyterLab for Default Built-in Images"],[76,"Endpoint"],[2237,"endpoint : Manage endpoints (formerly called deployments) on the DGX..."],[97,"Endpoint configurations"],[1422,"Endpoint Configurations"],[3201,"Endpoint URL"],[3205,"Endpoint URL"],[163,"Endpoints"],[2149,"Endpoints & Dev Pods (Access and manage endpoints and devpods)"],[969,"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."],[1018,"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."],[1050,"Endpoints can also be made publicly available without authentication. Please note that anyone with the endpoint URL can access the model and send requests without authentication if made public."],[7,"Endpoints: Deploy AI models as endpoints, with high performance and scalability."],[1410,"Ensure that Expose JupyterLab Proxy is selected. Otherwise, you won't be able to access JupyterLab."],[703,"Enter a job name like nemo-rl-cluster."],[719,"Enter a job name like nemo-rl-job."],[607,"Enter a name for the endpoint, such as nemotronh-8b-base."],[2140,"Enter a name for the role."],[64,"Enter a name, select a node group and machine type in the Resource section, then click Create. The Dev Pod starts shortly."],[1003,"Enter a pod name, such as dia-builder."],[692,"Enter a pod name, such as nemo-rl-builder."],[1457,"Enter one IP address or CIDR range per line, or separate multiple entries with commas."],[1016,"Enter python app.py for the Run Command field."],[707,"Enter the custom image that was pushed in the previous section, for example nvcr.io/my-private-registry/nemo-rl."],[723,"Enter the custom image that was pushed in the previous section, for example nvcr.io/my-private-registry/nemo-rl."],[1008,"Enter the following as the entrypoint command. Replace docker tag dia-gpu <registry tag> with the location of your registry to push to. For example, if you will push the container to a private registry on nvcr.io named my-private-registry, the command will be docker tag dia-gpu nvcr.io/my-private-registry/dia-gpu. Copy the same tag for the docker push command beneath it as well, such as docker push nvcr.io/my-private-registry/dia-gpu."],[697,"Enter the following as the entrypoint command. Replace docker tag nemo_rl <registry tag> with the location of your registry to push to. For example, if you will push the container to a private registry on nvcr.io named my-private-registry, the command will be docker tag nemo_rl nvcr.io/my-private-registry/nemo-rl. Copy the same tag for the docker push command beneath it as well, such as docker push nvcr.io/my-private-registry/nemo-rl."],[1292,"Enter the following details on the form:"],[705,"Enter the number of GPUs for each node in the Resource section. For example, x8 will use eight GPUs per worker."],[721,"Enter the number of GPUs for each node in the Resource section. For example, x8 will use eight GPUs per worker."],[706,"Enter the number of workers for the job. By default, this example will allow jobs to be scheduled on the head node in addition to the workers. If the head node should not run jobs, add an extra worker (that is, if 4 workers for running jobs and a standalone head node are desired, enter 5 for the worker count in the UI)."],[722,"Enter the number of workers for the job. By default, this example will allow jobs to be scheduled on the head node in addition to the workers. If the head node should not run jobs, add an extra worker (that is, if 4 workers for running jobs and a standalone head node are desired, enter 5 for the worker count in the UI)."],[3124,"Enter the token you set in the previous step to log in. You can then start using JupyterLab."],[1098,"Enter this information under Training Dataset, and select Load from Hugging Face. Next, enter nvidia/OpenMathReasoning for the dataset, cot for the dataset split, problem for the Question field, and generated_solution for the Answer field as shown in the image below."],[1336,"Entrypoint (Run Command): Entrypoint of the Dev Pod container. Choose from:"],[1406,"Entrypoint: Specifies the script to run at container launch. The Starter Kits are pre-configured with an entrypoint to launch Jupyter. Use the default Dev Pod entrypoint option here."],[1199,"env-job-98bw"],[1205,"env-job-98bw"],[1184,"env-job-98bw-0-2nm7s"],[1196,"env-job-98bw-job-svc"],[1167,"Environment Variable Name"],[1211,"Environment Variable Name"],[412,"Environment Variables and Secrets"],[1388,"Environment Variables and Secrets"],[1429,"Environment Variables and Secrets"],[1458,"Environment variables are key‑value pairs passed to the deployment.\nAll variables are injected into the deployment container and available at runtime."],[1162,"Environment Variables Reference"],[1151,"Environment Variables: Environment variables are key-value pairs that are passed to the job. They are automatically set as environment variables in the job container, so the runtime can refer to them as needed. Refer to this guide for more details."],[1349,"Environment Variables: Key–value pairs passed to the pod container for use at runtime."],[1104,"Epochs: Enter 3 for doing three full passes of the training dataset. While the paper calls for six epochs, that included the other two dataset splits which we did not include in this example. Three epochs will be enough to yield the same chain-of-thought results as the paper."],[1868,"Epochs: This is the number of full passes through the training dataset to complete. This must be a positive integer. Typical ranges are from 1-10. Note that if the Max Steps setting takes precedence over Epochs if the max steps has been reached prior to the desired number of epochs. Minimum: 1. Default: 1."],[1759,"Ethernet Ping"],[164,"Events"],[2285,"events : Lists events of the endpoint"],[2494,"events : Prints the events of a job by its ID."],[1129,"Every 500 steps, a checkpoint will be generated and saved in the specified directory. To deploy the checkpoint for inference, look for the model/consolidated sub-directory inside the checkpoint directory as this can be deployed directly as an Endpoint on DGX Cloud Lepton."],[387,"Example"],[3104,"Example Custom Entrypoint (Run Command):"],[2193,"Example for placeholders"],[145,"Example job submission via SDK"],[3228,"Example output from the endpoint includes:"],[636,"Example Script Template"],[894,"Example W&B Job"],[2750,"Examples:\n8080:tcp:proxy -> proxy only\n8080:udp:hostmap -> host mapping only\n8080:tcp:proxy:hostmap -> both strategies (note: only first proxy will take effect)"],[822,"Execute the Script: Copy the configured script from Step 1, paste it into the terminal, and press Enter."],[144,"Execute the task"],[156,"Execute the task"],[572,"executor = lepton_executor(nodes=8, devices=1): The example runs on eight pods with one process per node. If more nodes/processes are required, specify the amount here."],[264,"Expected GPUd Check: Configurations for the expected GPUd check. You can set expected values so that GPUd can help check if the node group is healthy, including InfiniBand Count, Minimum InfiniBand Bandwidth, and GPU Count."],[2064,"Expiration date: Default is None, which means the token will never expire."],[1999,"Expiration Time: When the access will automatically expire"],[2100,"Expiration: Recommended to set as No expiration."],[2115,"Expiration: The expiration date for the new API key."],[2222,"Expiration: The expiration time of the token. Default to 1 day."],[1989,"Expired: The granted access has passed its expiration time"],[2150,"Export Observability (Export observability data and metrics)"],[3196,"Export Settings"],[3197,"Export your logs and metrics data to your own storage."],[424,"Exports all current environment variables to the container"],[1409,"Expose JupyterLab Proxy: Ensure this button is checked for a direct URL to the JupyterLab session which can be accessed externally."],[3137,"Expose Through: Select Host Network or Proxy. Learn more about the differences here. We will use Proxy in this example."],[121,"External SSO Integration"],[12,"Features: Built-in storage, observability, and many configurable options."],[1654,"Figure 1. User view of submitting a new reservation request."],[1657,"Figure 2. Admin dashboard showing list of reservations with status and actions."],[1660,"Figure 3. Admin configuration screen showing auto-approval threshold settings."],[1669,"Figure 4. Selecting a reservation when launching a workload and choosing the option to burst into other reservations if available."],[1241,"Fill in the MPI Command, job name, and other configurations."],[3181,"Fill in the rest of the configurations as applicable and create the Dev Pod."],[3167,"Fill in the rest of the configurations as applicable and create the Dev Pod. You will then see SSH connection information in the Connection tab."],[1259,"Fill in the Torchrun Command, job name, and other configurations."],[3272,"Filter"],[3249,"Filters"],[1062,"Final Thoughts"],[667,"Finally, update the script to run on 2 nodes with eight GPUs per node. Edit the train.py script and change this line:"],[3230,"Find more details about the Prometheus Python library in the official documentation."],[181,"Finds the ID of the specified node group - Update the listed node group for your specific needs"],[1786,"Fine-Tuning"],[1054,"Fine-Tuning an LLM for Math Reasoning"],[1847,"Fine-tuning an LLM requires a dataset for the model to learn from. Datasets must have at least one input and one output field. As with models, datasets can either be loaded directly from Hugging Face or from shared storage mounted in the container."],[1066,"Fine-tuning is the process of taking a base model and adapting it to learn more about a domain or respond in a specific manner to prompts. A common example is to take an open source model and fine-tune it to improve its accuracy in math benchmarks which is what this guide showcases."],[1762,"FIO Test"],[898,"First, create a W&B access token by navigating to https://wandb.ai and click Sign Up in the top right to create a free account if not done already. Once logged in, go to https://wandb.ai/settings and go to the bottom to create a new API key. This API key needs to be specified for jobs that use W&B."],[147,"First, install the Python SDK and authenticate with your workspace. Install the SDK with:"],[647,"First, install the required Python dependencies to launch the script. Open a terminal on your computer and navigate to your preferred directory to save the launching scripts. Then, run the following commands:"],[1419,"First, set a password for accessing Jupyter. Click the Terminal button on the job details page and run the following command, as shown below:"],[3118,"First, under Storage, specify a volume to attach to the Dev Pod, then specify the mount path for the volume. Select Mark as JupyterLab Root to use this mount path as the root workspace directory in JupyterLab."],[496,"First, you need to create a job in our platform.\nHead over to the Create Job page."],[2097,"First, you need to create an access token with the appropriate scopes.\nNavigate to your GitHub tokens page, click on the Generate new token button."],[14,"Follow quick start guides for Workspace, Node Group, Endpoint, Dev Pod, and Batch Job—or explore the examples."],[606,"Follow these steps in the new form that opens:"],[178,"For a batch job, you need a LeptonJob object with a LeptonJobUserSpec. Review the LeptonJobUserSpec in the Python script for the list of settings which are required for launching a job. The following is a quick example of defining a batch job spec (this expands upon the previous code which instantiated the client):"],[2198,"For a template, you need to specify a workload type, which can be one of the following:"],[2506,"For advanced uses, check\nhttps://kubernetes.io/docs/concepts/workloads/controllers/job/."],[60,"For AI application developers, having a convenient development environment is essential for building and testing applications."],[673,"For an in-depth guide on training and fine-tuning LLMs with NeMo Framework, refer to our comprehensive guide."],[751,"For ARM-based systems:"],[3052,"For Audience, enter https://kubernetes.default.svc."],[358,"For batch jobs, some frameworks like PyTorch, TensorFlow, and Horovod support distributed training. DGX Cloud Lepton also supports distributed training for these frameworks."],[301,"For best performance and stability, we recommend the following storage configuration (local to each GPU node):"],[1941,"For both types of storage, specify the volume name and the mount path (on the node) when adding storage."],[3115,"For built-in images configured in Step 1, select the Enable JupyterLab Proxy checkbox and the form will automatically fill in the port and protocol for you."],[290,"For CUDA toolkit, we recommend version 12.4.1 or later with NVCC installed."],[3219,"For Datadog secret key, refer to the official documentation for more details."],[3223,"For Datadog secret key, refer to the official documentation for more details."],[1726,"For default GPU shapes, the upper bound for other resource dimensions (such as vCPU and memory) equals total available resources divided by the number of GPUs.\nFor example, on a node with 8 GPUs and 128 vCPUs, the default 1‑GPU shape is limited to 16 vCPUs (128 vCPUs divided by 8 GPUs)."],[1370,"For each port, specify the port number to expose. The port number must be between 1 and 65535."],[1369,"For each port, specify the protocol type: TCP, UDP, or SCTP."],[1483,"For Endpoint name, enter container-image-endpoint or any name you prefer."],[1507,"For Endpoint name, enter nim-endpoint or any name you prefer."],[1538,"For Endpoint name, enter sglang-endpoint or any name you prefer."],[1525,"For Endpoint name, enter vllm-endpoint or any name you prefer."],[1498,"For enhanced performance and seamless compatibility, NVIDIA‑optimized models from the NIM container registry are available on DGX Cloud Lepton."],[2180,"For every secret, you can set the visibility to Public or Private."],[3084,"For example, add the following to your container startup:"],[1581,"For example, if you are in a workspace named \"test-workspace\", you'll see a quota card named \"test-workspace\" in the workspace quota section. By default, the quota isn't configured, which means the total resource usage in the node group isn't limited. The priority policy is set to \"Best Effort\"."],[1947,"For example, if you specify /data/test as the path prefix, then the permission will only apply to the path /data/test and all subfolders under /data/test."],[1210,"For example, if you use torch.distributed.launch, the required environment variables can be set up as:"],[1721,"For example, the shape 8xH100 GPUs, 128 CPUs, 1 TB Memory reserves 8 H100 GPUs, 128 CPU cores, and 1 TB of memory."],[2059,"For example, to use a Dev Pod with a Docker Hub registry, navigate to the pod creation page, and in the Container configuration section, select custom image.\nInput the image address in the Image field, and select the registry auth you created in the Private image registry auth field."],[1964,"For example, when you are creating a Dev Pod, you can specify the node group as the one you've added the storage to."],[1375,"For example, you can configure port 2222 with protocol TCP and expose it through Host Network, and port 18889 with protocol TCP and expose it through Proxy."],[95,"For example, you can use the following command to list the available models in the endpoint."],[1541,"For Image configuration, leave defaults as is. To add arguments, expand the command-line arguments section and add your own. SGLang arguments are listed here."],[1528,"For Image configuration, leave defaults as is. To add arguments, expand the command-line arguments section and add your own. vLLM arguments are listed here."],[1484,"For Image configuration, select the image, command, and port:"],[1526,"For Model, click Load from Hugging Face and search by keyword. For example, meta-llama/Llama-3.1-8B-Instruct. If the model is gated, provide a Hugging Face token. Create a token in your Hugging Face account and save it as a secret in your workspace."],[1539,"For Model, click Load from Hugging Face and search by keyword. For example, meta-llama/Llama-3.1-8B-Instruct. If the model is gated, provide a Hugging Face token. Create a token in your Hugging Face account and save it as a secret in your workspace."],[922,"For more information about AlphaFold2, see the AlphaFold2 paper in Nature. If you use this NIM or AlphaFold2, make sure to cite the paper:"],[50,"For more information about batch jobs, refer to the following:"],[96,"For more information about endpoints, refer to the following:"],[3155,"For more information on configuring nodes, refer to the node requirements guide."],[3170,"For more information on configuring nodes, refer to the node requirements guide."],[3187,"For more information on configuring nodes, refer to the node requirements guide."],[3133,"For more information on how to expose container ports, refer to this guide."],[1023,"For more information on the Dia model, please refer to their GitHub repo."],[626,"For more information on the OpenAI API, reference the official documentation."],[731,"For more information on using NeMo-RL, refer to their GitHub page and associated documentation."],[67,"For more information on using Starter Kits, refer to the Starter Kits on Dev Pods guide."],[322,"For multi-node training workloads, follow your cloud provider's guidelines to set up RDMA for east/west traffic."],[1509,"For NIM configuration:"],[1514,"For NIM engine‑related configurations, refer to this guide. Configure the NIM engine by setting the relevant environment variables."],[939,"For other endpoint configurations, refer to the Endpoints documentation."],[967,"For other endpoint configurations, refer to the Endpoints documentation."],[1048,"For other endpoint configurations, refer to the Endpoints documentation."],[1513,"For other endpoint‑related configurations, refer to this guide."],[1379,"For ports exposed through Host Port, access them using the IP address of the host node."],[3050,"For Provider type, select OpenID Connect."],[3051,"For Provider URL, enter the OIDC provider URL for your cluster."],[3227,"For Python applications, handle HTTP requests as shown in the example below. Configure the export port as 8080 and the path as /metrics."],[2579,"For removing multiple jobs with the same name, use 'lep job remove-all'\ninstead."],[1508,"For Resource, choose an appropriate resource based on the model size."],[1527,"For Resource, choose an appropriate resource based on the model size."],[1540,"For Resource, choose an appropriate resource based on the model size."],[3154,"For SSH to work on DGX Cloud Lepton, the user and machine accessing the Dev Pod via SSH must be able to reach either the public or private IP of the node(s) on which the Dev Pods are running."],[1014,"For the Image Configuration section, specify the name of your <registry tag> that was used during the container build for the Image field. This is the location that the custom container was pushed to. If the registry requires additional authentication for pulling images, add your registry authentication key in the Private Image Registry Auth field."],[1094,"For the Model field, select Load from storage and enter the path to where the modified model was saved during the preparation step earlier. For example, if your model was saved to /storage/models per the STORAGE_DIR variable above, enter /storage/models/qwen2.5-math-1.5b as that is the absolute path to where the model is saved. This must start with the storage mount path (ie. /storage) and end with the downloaded model path name (ie. qwen2.5-math-1.5b) as in the image below."],[1099,"For the validation dataset, we could use a subset of the OpenMathReasoning dataset, but given this has millions of samples, even a small percentage of the dataset will result in a lot of validation data to process, significantly increasing the total training time. Instead, we will use a math benchmark which is significantly smaller but also more indicative of how the model is learning compared to real-world benchmarks. Specifically, we will use the AIME24 dataset which contains 30 math problems with reasoning traces and answers as output."],[1676,"For training workloads, checkpointing is the user's responsibility. If a job is preempted and not checkpointed properly, progress may be lost."],[750,"For x86-based systems:"],[909,"For your own W&B experiments, adding the API key will automate the login process so your own code should run automatically connected to your account."],[1617,"Force a reboot of a specific node."],[1608,"Force Reboot"],[1645,"Frequently Asked Questions"],[1967,"From path: Specifies the directory path within the volume. For example, to mount the root of the volume, set the \"From path\" to /."],[3260,"From the dashboard, navigate to the deployment, job, or pod detail page."],[3269,"From the dashboard, select the Observability tab and select Logs."],[3195,"From the dashboard, select the Observability tab, then select Audit Logs."],[192,"from: This is the name of the storage to mount. When using shared storage, you must first specify the storage type followed by a colon and your storage name. The storage type is very commonly node-nfs. Following the example above, this should be node-nfs:lepton-shared-fs."],[2639,"Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS.123456\nExample: 2024/12/25 13:10:01.123456"],[2645,"Full Date and Time:\nFormat: YYYY/MM/DD HH:MM:SS.123456\nExample: 2024/12/25 13:10:01.123456"],[2033,"GCP"],[3067,"GCP - Workload Identity Federation via Cloud Console"],[3065,"GCP Workload Identity Federation"],[1628,"General hardware: CPU, memory, disk, network, PCI, and more."],[2428,"get : Get an ingress by name and print it in json"],[2495,"get : Gets detailed information about jobs."],[2630,"get : Retrieve and display logs from deployments, jobs, or replicas"],[2725,"get : Show pod detail and optionally save its spec JSON."],[2286,"get : Shows Endpoint detail and optionally saves its spec JSON."],[2805,"get : Shows Ray cluster detail and optionally saves its spec JSON."],[2437,"Get a ingress by name and print it in json"],[2034,"Get Service Account Key JSON File"],[2227,"Get Started"],[182,"Get the list of the node IDs for all nodes in your node group - this specifies which nodes the job can be scheduled on"],[2569,"Gets detailed information about jobs."],[2591,"Gets the log of a job. If replica is not specified, the first replica is\nselected. Otherwise, the log of the specified replica is shown. To get the\nlist of replicas, use lep job replicas."],[2361,"Gets the log of an endpoint. If replica is not specified, the first\nreplica is selected. Otherwise, the log of the specified replica is shown.\nTo get the list of replicas, use lep endpoint status."],[2355,"Gets the status of an endpoint."],[18,"Getting Started with BYOC"],[2041,"GitHub"],[2055,"GitHub Container Registry (GHCR)"],[2108,"GitHub Username: Your GitHub username."],[468,"Give it a descriptive name like AWS_SECRET_ACCESS_KEY."],[1828,"Give the fine-tuning job a name, such as my-finetuning-job. This is how the job will be referenced in the fine-tuning jobs list in the UI."],[125,"Given that DGX Cloud Lepton is in early access, your workspace must be activated after it is created.\nA banner will appear indicating your workspace is not yet activated. Follow the instructions to apply for access on the NVIDIA Developer website."],[562,"Given that the Nemotron-CC dataset contains several thousands of files, it is helpful to concatenate them into fewer, larger files. Processing a smaller number of large files is much faster than handling a large number of small files, which eliminates potential data bottlenecks during the pre-training stage."],[297,"glibc version ≥ GLIBC_2.32"],[1107,"Global Batch Size: Enter 1024 to process 1,024 sequences per training pass across the cluster. The paper used a batch size of 256 but also packed their sequences by a factor of 4x, effectively yielding a batch size of 1024."],[1874,"Global Batch Size: This is the number of sequences to process during each step of the training process. The global batch size is divided by the number of GPUs so each GPU processes Batch / num GPU sequences during a training step. Higher batch sizes may result in greater training throughput at the cost of additional GPU memory. This must be a positive integer and is typically a power-of-two (1, 2, 4, 8, ...). This must be greater than or equal to the number of GPUs in the job. Minimum: 1. Default: 16."],[777,"Go back to the Dev Pod Terminal tab. In the web terminal, run the following command to install ngrok:"],[937,"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /nim/.cache. Then go to Storage and click + Mount Storage. Select your desired volume, set the from path to /nim/.cache, and set the to path to /nim/.cache."],[1046,"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /opt/nim/.cache. Then go to Storage and click + Mount Storage. Select your desired volume, set the from path to /opt/nim/.cache or another location for storing cache on storage, and set the to path to /opt/nim/.cache."],[966,"Go to Environment Variables and Secrets and click Add Variable. Set the variable name to NIM_CACHE_PATH and the variable value to /opt/nim/.cache. Then go to Storages and click + Mount Storage. Select your desired volume, set the from path to /opt/nim/.cache or another location for storing the cache on your volume, and set the to path to /opt/nim/.cache."],[1239,"Go to the Batch Jobs tab and click Create Job."],[1257,"Go to the Batch Jobs tab and click Create Job."],[1480,"Go to the Endpoints page and click Create Endpoint."],[1522,"Go to the Endpoints page and click Create Endpoint."],[1535,"Go to the Endpoints page and click Create Endpoint."],[797,"Go to the GitHub login page and use the code generated in the logs to grant access to the VS Code server."],[755,"Go to the GitHub login page, and use the code generated in the logs to grant access to the VS Code server."],[776,"Go to the ngrok authtoken page. Click on the Copy button to copy the authtoken and save it for later use."],[856,"Go to the node group page, and make sure the privilege mode is enabled for the node group you want to use. You can enable the privilege mode by clicking the \"Config\" button and select the \"Privilege Mode\" checkbox. Make sure it's enabled for the node group you want to use."],[2053,"Google Container Registry (GCR)"],[944,"GPT-OSS-120B Deployment Guide"],[964,"GPT-OSS-120B is a large model that can take a long time to download if not already available in storage. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 99999 seconds or higher as necessary."],[1761,"GPU Burn"],[1737,"GPU Type: The specific type of GPU assigned to the shape. Available types are determined by the node group."],[1627,"GPU: GPU temperature, utilization, power usage, and more."],[1592,"GPU: Total GPU quota for the user (default: unlimited)."],[1583,"GPU: Total GPU quota for the workspace (default: unlimited)."],[1620,"Graceful Drain (recommended): Migrates current workloads to other nodes, then terminates all workloads on the node."],[1624,"Graceful Release (recommended): Migrates workloads to an idle node, then terminates all remaining workloads and releases the node."],[791,"Grant access to the VS Code server"],[1574,"Hardware issues can cause nodes to become unhealthy. When Auto Reboot is enabled, an unhealthy and drained node (no workloads) will be rebooted automatically to attempt recovery."],[275,"Hardware Requirements"],[1296,"Head Node: Select the Node Group and resource shape for the head node. The head node can be CPU‑only (scheduling only) or include GPU resources to run workloads. Specify any storage to mount on the head node using + Mount Storage."],[342,"Head over to the Batch Jobs page, and follow the steps below to create a job."],[362,"Head over to the Batch Jobs page, and follow the steps below to create a job."],[2210,"Head to the Create From Container Image page for dev pods."],[1437,"Header-based Replica Routing"],[1612,"Health Check"],[1434,"Health Check Initial Delay (seconds)"],[165,"Health Checks"],[1631,"Health checks run periodically, but you can also manually trigger a health check for a specific node."],[1229,"Here is an example of a short script that sets up the environment variables for a job:"],[359,"Here is an example of running a distributed PyTorch job with two workers on DGX Cloud Lepton."],[737,"Here is an example of running a job with Conda environment management on DGX Cloud Lepton."],[1373,"Host Network: A random port between 40000 and 65535 is added to the Dev Pod and mapped to the specified container port. This Host Network port is accessible on the host node IP."],[262,"Host Network: Whether to enable host network for the node group. Defaults to disabled. Enable host network to allow sharing the node's network namespace, which is useful for workloads requiring direct access to the host's network interfaces."],[1366,"Host Port: The port on the host machine that maps to your container port (for example, external users connect to port 45123 on the host, which forwards to port 8080 in the container)."],[1247,"HOST_ADDRS: Comma-separated list of all node hostnames"],[1248,"HOST_IPS: Comma-separated list of all node IP addresses"],[1252,"HOSTFILE: Path to the file containing all node IPs (default: \"/tmp/hostfile.txt\")"],[3281,"Hover over the line whose context you want to view, then click the show context button to view the log context.\nIn the log context dialog, the current line will be highlighted along with its surrounding context."],[207,"How to request support for DGX Cloud Lepton"],[3198,"How to Set Up"],[1272,"How to set up a RayCluster on DGX Cloud Lepton for submitting Ray jobs on compute resources."],[1316,"How to set up a Slurm cluster on DGX Cloud Lepton for running Slurm-based workloads."],[1728,"However, bursting introduces a risk of out‑of‑memory (OOM) events or GPU resource contention if a CPU‑only/memory shape is co‑located with a GPU workload, as both contend for the same limited resources."],[3229,"http_response_time_seconds is a metric emitted by the above application."],[540,"Hugging Face contains resources for many of the most popular language models and datasets in the community. You can leverage these resources while training the model to minimize deployment steps and maintain consistency with community model assumptions."],[1913,"I see AssertionError: Expected at least two columns in my logs"],[1915,"I see CUDA OOM errors in my logs"],[1758,"IB Ping"],[2959,"id : Prints the id of the current workspace."],[1902,"Ideally, you want your train_loss and val_loss to decrease over time like in the example above. This indicates the model is learning the target dataset."],[1177,"Identifier for the current workspace"],[2014,"Identify the request you want to revoke access for and click Revoke."],[974,"If a browser tab does not open automatically, navigate to the API tokens page. Click + Create Token in the top right corner, specify a name for your access token, and select the expiration of the token."],[1580,"If a node group is shared by multiple workspaces, you may want to limit total resource usage for the current workspace."],[1648,"If a reservation has available capacity, eligible workloads are placed there. Otherwise, DGX Cloud Lepton falls back to scheduling on unreserved nodes in the node group."],[1673,"If enabled, a running job may be preempted when:"],[3116,"If exposing through host network, follow the screenshot to configure the container ports, but select Host Network instead of Proxy."],[3178,"If following the example below, specify 2222 as the port, or choose a different port if you are modifying or using your own script."],[757,"If not already available, install version 1.0 of Cursor on your local machine."],[292,"If NVIDIA driver and NVIDIA Fabric Manager are already installed on your machine, DGX Cloud Lepton will not install them again."],[185,"If shared storage is available in your node group, you can mount it in a job by pointing to the storage address and indicating which directory to mount from storage and where to mount it in the container."],[1838,"If storage is not added, any generated checkpoints will be lost after the job completes."],[2688,"If the --detail or -d option is provided, additional information about each\nnode will be displayed."],[901,"If the above command returns nothing, W&B is not installed already."],[2325,"If the GPU utilization is higher than the target GPU utilization,\nthe autoscaler will scale up the replicas.\nIf the GPU utilization is lower than the target GPU utilization,\nthe autoscaler will scale down the replicas.\nThe threshold value should be between 0 and 99."],[2380,"If the GPU utilization is higher than the target GPU utilization,\nthe autoscaler will scale up the replicas.\nIf the GPU utilization is lower than the target GPU utilization,\nthe autoscaler will scale down the replicas.\nThe threshold value should be between 0 and 99."],[239,"If the machine is added successfully, the node status will show as Healthy."],[1885,"If the project, run, and API key for W&B are set in this section, your fine-tuning job will automatically be synced with W&B for further viewing."],[1901,"If you added your W&B credentials, you can view the training metrics at wandb.ai as in the image below:"],[3117,"If you are mounting persistent storage to the Dev Pod and using the default built-in images, you can specify the storage directory to be mounted as the workspace root directory for Jupyter. For more information on storage, refer to this guide."],[3171,"If you are using a custom image, the SSH Public Key field is not present because DGX Cloud Lepton cannot determine whether SSH is pre-configured."],[196,"If you are using a private container registry that requires authentication, you need to specify the private container secret in the job definition."],[296,"If you are using a system with NVSwitch, you must have NVIDIA Fabric Manager installed with the same version as the driver."],[1371,"If you are using built-in images and Dev Pod Entrypoint, port 2222 in the container will be used for SSH.\nAdditionally, if you also select Enable JupyterLab, port 18889 in the container will be used for JupyterLab."],[1363,"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."],[3114,"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."],[3131,"If you choose to expose container ports, ensure proper authentication and security measures are in place. You are responsible for the security of your exposed services."],[1380,"If you configured a port to be exposed through Proxy, access it with the generated public URL."],[832,"If you configured JupyterLab:"],[768,"If you do not wish to expose JupyterLab via the Proxy or Host Network options, an example is provided below which installs and uses ngrok in the Dev Pod to access JupyterLab."],[542,"If you don't have a Hugging Face account already, create one by going to https://huggingface.co/join and signing up with your corporate email account."],[3132,"If you enabled JupyterLab for a Dev Pod in Step 1, DGX Cloud Lepton starts the JupyterLab instance on a container port. The linked example uses port 18889; default JupyterLab commands typically use port 8888.\nYou will need to manually expose the port if you want to access JupyterLab from outside the container."],[195,"If you have a single dictionary and not a list, this would be:"],[1862,"If you have a unique validation dataset, this process can be repeated specifically for inputting validation data. Refer to the steps above on filling out this information if needed."],[3162,"If you have already added an SSH public key as a Secret, you can select it from the SSH Public Key dropdown list."],[200,"If you have an existing node reservation for scheduling jobs on dedicated resources, you can add that to your request using the node_reservation field as follows:"],[2169,"If you have the admin role, click the dropdown menu in the Role column for a member and select a role."],[2138,"If you have the admin role, you can create a new role."],[299,"If you have your own version of these packages already installed on the system, the BYOC installation process can fail."],[1131,"If you made it this far, congratulations! You have now fine-tuned an open source model to add math reasoning capabilities which make it more accurate in math-related benchmarks. From here, you can deploy the model as an Endpoint on DGX Cloud Lepton to test it out and send requests, or you could further fine-tune it with additional dataset splits and upload the final result to Hugging Face."],[672,"If you made it this far, congratulations! Your DGX Cloud Lepton workspace should now be ready for distributed training jobs."],[1864,"If you mounted your storage as /nfs-storage, the checkpoint path should begin with /nfs-storage/."],[1781,"If you recently added a domain or updated the DNS record, it may take a few minutes for the status to change from PENDING to ACTIVE."],[1906,"If you run ls in that directory you will find a list of sub-directories named epoch_X_step_YYYY where X is the epoch number and YYYY is the global step number. Each of these sub-directories contains the checkpoints at the indicated step count."],[3125,"If you select a Custom image type, the Enable JupyterLab checkbox is not available. Custom images may not have JupyterLab installed, or the built-in run command may not work with the image."],[1951,"If you select the Permitting selected members to specific path policy, you can select the workspace members you want to grant access to."],[291,"If you select the Standard boot option for your machine (on Azure, this is called Security Option), DGX Cloud Lepton can automatically install the 550.144.03 NVIDIA driver with NVIDIA Fabric Manager for you."],[1880,"If you selected LoRA for your fine-tuning technique, you will see two additional settings at the top of the box:"],[941,"If you set your endpoint to be publicly accessible, you do not need to set an endpoint token."],[987,"If you set your endpoint to be publicly accessible, you do not need to set the authorization token."],[1052,"If you set your endpoint to be publicly accessible, you do not need to set the authorization token."],[781,"If you want to run ngrok continuously in the background, follow these steps:"],[321,"If you want to use Dev Pod on DGX Cloud Lepton, you need to open ports 40000 to 65535 for your machines."],[3156,"If you want to use Dev Pods on DGX Cloud Lepton, you need to open ports from 40000 to 65535 on your network. These ports are used to pass through the SSH port on the Dev Pod. For more information, refer to this guide."],[293,"If you want to use the Trusted Launch boot option, you need to install the NVIDIA driver and NVIDIA Fabric Manager manually."],[1884,"If you would like to log results with W&B, specify your desired Project name and the name of the Run for the fine-tuning job to be visible under in W&B. Next, add your W&B API key by either selecting it from the drop-down list or clicking the green + Add key button and following the prompts to add your W&B API key."],[800,"If you'd like to connect to the Dev Pod with VS Code installed on your local machine, you can use Connect to Tunnel function in VS Code with the same authentication. Eg. if you've granted access to the VS Code server with github, you can use the same github account to connect to the Dev Pod with VS Code installed on your local machine."],[544,"If you're using gated models that require users to accept their license, such as the official Llama-3.1-8B model, navigate to their specific model card on Hugging Face, read the privacy policy, and click the Agree and access repository button while logged in to Hugging Face. Now you can download resources from the gated repository using your personal access token."],[1855,"If your dataset already has an input column named question and an output column named answer, you can copy those fields for the columns. If not, specify the column name in the dataset that maps to each field. For example:"],[2023,"If your Enterprise SSO has been integrated, DGX Cloud Lepton members still need to be individually added to DGX Cloud Lepton. See the Workspace section for more information on SSO integrations."],[1729,"If your workload is sensitive to these issues, we recommend defining custom GPU shapes with CPU/memory limits that ensure the combined allocation between CPU‑only and GPU shapes does not exceed total system resources."],[3127,"Image default entrypoint - Select this option if the container image starts JupyterLab automatically by default. In this case, ensure that the default JupyterLab run command has authentication and security features enabled."],[3175,"Image default entrypoint - Select this option to use the default entrypoint for the image."],[1338,"Image default entrypoint: Uses the image's default run command."],[932,"Image: Choose Custom image and enter nim/deepmind/alphafold2:2.1.0."],[487,"Image: Choose the custom image and enter nvcr.io/nvidia/pytorch:24.11-py3"],[1485,"Image: Choose the image to use. For example: nvcr.io/nvidia/pytorch:24.11-py3."],[1146,"Image: The container image that will be used to create the job. You can choose from the default image lists or use your own custom image."],[1332,"Image: The container image used to create the pod. Choose from default images or use your own custom image."],[1405,"Image: This field should be pre-populated with the container image the notebook was designed for. While you can try a different container, it is not recommended, as it might not work with the notebook."],[501,"Image: We will use nvcr.io/nvidia/pytorch:24.11-py3 as the image for the job. This image is built with the latest NVIDIA container toolkit and PyTorch 24.11. Choose custom image and enter the image name."],[903,"Import the wandb module"],[33,"Import Your Machines"],[180,"Imports all required modules"],[3037,"In 2014, AWS Identity and Access Management added support for federated identities using OpenID\nConnect (OIDC). This feature allows you to authenticate AWS API calls with supported identity\nproviders and receive a valid OIDC JSON web token (JWT). You can pass this token to the AWS STS\nAssumeRoleWithWebIdentity API operation and receive IAM temporary role credentials. You can use\nthese credentials to interact with any AWS service, including Amazon S3 and DynamoDB."],[1886,"In addition to W&B integration, you can also choose to add labels to your job or change the visibility as needed."],[1367,"In DGX Cloud Lepton, you must configure container ports to expose them as host ports externally."],[1119,"In order, this prints out:"],[1820,"In summary, choose SFT if:"],[710,"In the Advanced Configuration section, add a Hugging Face token by clicking Add secret in the Environment Variables field. Either select your Hugging Face token if already added, or add one by clicking CREATE NEW SECRET and select the Hugging Face option and fill out the requested information."],[727,"In the Advanced Configuration section, add a Hugging Face token by clicking Add secret in the Environment Variables field. Either select your Hugging Face token if already added, or add one by clicking CREATE NEW SECRET and select the Hugging Face option and fill out the requested information."],[711,"In the Advanced Configuration section, add storage with the + Mount Storage button and select the volume for your node group's storage and enter the storage path as well as the mount path inside containers."],[728,"In the Advanced Configuration section, add storage with the + Mount Storage button and select the volume for your node group's storage and enter the storage path as well as the mount path inside containers."],[1860,"In the case of the nvidia/OpenMathReasoning dataset, there isn't a question or answer column present and they need to be overridden. This dataset contains several columns, but the two important ones for fine-tuning are problem and generated_solution. problem is the input which maps to the Question field, and generated_solution is the response which maps to the Answer field."],[3134,"In the Container Ports section, add a new port with the following configurations:"],[380,"In the container section, use the default image and paste the following command to run the job:"],[42,"In the Container section, use the default image. In the Run Command field, specify the following command to run in the container:"],[346,"In the container section, use the image (nvcr.io/nvidia/pytorch:25.08-py3) and paste the following command as the MPI Command to run the job:"],[366,"In the container section, use the image (nvcr.io/nvidia/pytorch:25.08-py3) and paste the following command as the Torchrun Command to run the job:"],[1074,"In the creation form that opens up, enter the following details:"],[2081,"In the creation form, you need to fill in the following fields:"],[2113,"In the creation form, you need to fill in the following fields:"],[1750,"In the Default Shapes section, view default shapes for the node group and disable or enable them by clicking Disable or Enable for each row."],[3046,"In the Details section, note the value of the OIDC provider URL."],[1888,"In the job tile you can view various information including:"],[3048,"In the left navigation pane, choose Identity Providers under Access management. If a provider is\nlisted that matches the URL for your cluster, then you already have a provider for your cluster.\nIf a provider isn't listed that matches the URL for your cluster, then you must create one."],[961,"In the NIM Configuration section, select Built-in under image and enter/select nim/openai/gpt-oss-120b:latest from the list. Additionally, specify the registry authentication and your NGC API key in the following fields:"],[931,"In the NIM Configuration section, specify the container image, registry auth key, and NGC API key."],[1041,"In the NIM Configuration section, specify the registry authentication and your NGC API key."],[1946,"In the Path field, specify the path prefix you want to grant access to. This Path is relative to the volume itself. The permission will only apply to the specified path prefix."],[344,"In the resource section, first select which node group you want to use."],[364,"In the resource section, first select which node group you want to use."],[41,"In the Resource section, select the node group and your desired resource shape.\nFor example, select H100-80GB-HBM3 x 1 from node group h100sxm-0, and leave the worker and priority settings at their default values."],[611,"In the Run Command section, add --trust-remote-code to the end of the command. By default, Hugging Face throws an error when models that aren't hosted on its servers are run so the user is aware that the model could be malicious. Since you built this model and trust it, you can authorize its usage for vLLM. The command will end up looking like vllm serve /nemo-workspace/nemotronh_8b/huggingface --port 8080 --trust-remote-code."],[726,"In the script, change the uv run python... command to the specific task you want to run with NeMo-RL including any modifications to the parameters. The task specified here will be run directly on the RayCluster that is created as part of this Batch Job."],[2076,"In the second step, you need to grant registry auth role to the service account. In this case, you need to grant Artifact Registry Reader role so that the service account can access the Artifact Registry and pull images."],[1504,"In the Service Included field, select Public API Endpoints."],[3160,"In the SSH Public Key field, specify or create a new SSH public key if you want to access the Dev Pod via SSH."],[476,"In the Storage section, click + Mount Storage. Select the storage volume where you want to download the data. Input your desired from path and /dataset as the mount path."],[1965,"In the Storage section, click the Mount storage button to select the volume you want to mount."],[614,"In the Storage section, verify the correct volume and mount path is selected."],[3072,"In the subsequent workflow wizard, provide a name for the pool and an optional\ndescription, and select OpenID Connect (OIDC) as the provider."],[3045,"In the top menu bar, select Nodes, and then select the name of your NodeGroup on the Nodes page."],[3126,"In this case, you must first specify the image URL, and then select one of the following Entrypoints:"],[1918,"In this case, you will need to do some pre-processing of the dataset on shared storage and split the input and output into different columns. One example is creating a Python script which reads in the dataset, iterates over each line, saves the input and output as different keys/values in a dictionary, and writes the data to a file."],[1243,"In this documentation, we use the term 'primary' instead of 'master' to align with modern terminology. Please note that the UI, commands, and environment variables may still reference 'master'.\nThe MPI template generates a bash script that sets up the MPI runtime environment. The following environment variables are automatically available in your job:"],[1261,"In this documentation, we use the term 'primary' instead of 'master' to align with modern terminology. Please note that the UI, commands, and environment variables may still reference 'master'.\nThe Torchrun template generates a bash script that predefines environment variables for your job:"],[3129,"In this example, the command assumes JupyterLab is already installed in the container image. It starts jupyter-lab and passes configuration parameters so the notebook token is set as the $ENV_SECRET environment variable, which is configured to be injected via DGX Cloud Lepton. The port is set to 8888, and the JupyterLab instance is set up to run as root and be accessed remotely."],[498,"In this example, we will use the following configurations:"],[1520,"In this guide, we'll show you how to create a dedicated endpoint from LLMs with inference engine vLLM and SGLang to serve models from Hugging Face."],[1829,"In this section, specify the Node Group and the resources to allocate for the job."],[549,"In VS Code, open a terminal window by clicking the Terminal > New Terminal button in the menu. Next, create a Python virtual environment and install the dependencies required for running NeMo 2.0 and Lepton using the following commands in the new terminal:"],[1698,"Include themselves in the reservation, or"],[1192,"Index of the current worker within the job"],[258,"InfiniBand: Whether to enable InfiniBand. Defaults to disabled."],[166,"Ingress"],[510,"Initial Setup"],[633,"Initial Setup"],[139,"Initialize a client"],[154,"Initialize a client"],[904,"Initialize the wandb project with wandb.init and specify hyperparameters and the project name"],[158,"Initializing the client is straightforward—simply import the Lepton API module and instantiate the client:"],[228,"Input the IP address and username in the following format:"],[826,"Install and configure JupyterLab (if token provided)"],[801,"Install and Configure SSH and Jupyter for Custom Images"],[824,"Install and configure SSH service on the PyTorch container"],[950,"Install DGX Cloud Lepton Python package"],[851,"Install Docker and Required Plugins"],[3063,"Install the AWS CLI in your workload and run the following commands to confirm (replace BUCKET_NAME with the appropriate bucket\nname):"],[136,"Installation and authentication"],[895,"Integration with NVIDIA NeMo Framework"],[1,"Introduction"],[1056,"Introduction"],[15,"Introduction to BYOC"],[282,"IP Address Restrictions"],[1428,"IP Allowlist"],[1452,"IP allowlist: Allow access to your endpoint only from valid IP addresses or CIDR ranges."],[2238,"job : Manages Lepton Jobs."],[417,"Job array, dependencies"],[260,"Job Default Envs: Environment variables to be set for all jobs in the node group. You can add multiple environment variables, including variable env and secret env."],[369,"Job Failure Diagnosis"],[398,"Job Name"],[3235,"Job name, only available for metrics from lepton job"],[1253,"Job Name: Enter a descriptive name like mpi-job or your preferred identifier."],[1268,"Job Name: Enter a descriptive name like torchrun-job or your preferred identifier."],[1075,"Job Name: Enter a name for the job, such as qwen-modification."],[474,"Job Name: Set a descriptive name like s3-data-download.\nResource: Select the desired node group and resource shape. You can use a CPU-only resource for this workload.\nImage: Use the built-in image.\nRun Command: Copy the following script and replace your-s3-bucket-name-here with your actual S3 bucket name:"],[485,"Job name: Set it to nanogpt-training."],[678,"Job Setup"],[52,"Job templates"],[406,"Job Timeout"],[2151,"Jobs (Access and manage all batch jobs)"],[436,"Jobs can also be submitted using the Lepton CLI. Install the latest version of the CLI with the following command:"],[416,"Jobs List and Stop Job"],[2084,"Json Key: The JSON key file for the service account. You can upload the file by clicking the Upload Service Account Key button."],[923,"Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2"],[817,"JUPKEY: Your JupyterLab token (optional, leave empty to skip JupyterLab installation)"],[3108,"JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. This guide helps you set up and access JupyterLab in your Dev Pod on DGX Cloud Lepton."],[1421,"JupyterLab opens in a new browser tab running on the requested resources inside the Dev Pod. Enter the password you specified above to authenticate, then click the notebook in the left panel to open it and begin running it."],[840,"JupyterLab running on port 18889 (optional)"],[2219,"Keep your tokens secure and do not share them with others."],[2007,"Key Details Regarding Approved Access:"],[3,"Key Features and Benefits"],[3002,"Key limits for workloads on DGX Cloud Lepton."],[2114,"Key Name: The name for the new API key."],[839,"Key-based SSH authentication"],[3062,"Kubernetes has the concept of service accounts as resources, which are essentially non-human identities\nthat can interact with the API server and other resources. Every DGX Cloud Lepton workspace runs workloads as the\ndefault service account. Here we set up the workloads (container) with the three environment variables needed\nfor the AWS SDK to utilize the token and above configuration. Place the following lines in your container\nstartup (replacing the ACCOUNT_ID, ROLE_NAME, and S3_REGION appropriately):"],[3232,"Label"],[3208,"Labels of logs and metrics"],[1065,"Large Language Models (LLMs) have continued to demonstrate strong capabilities in a wide range of domains from math to science to creative writing and everything in between. LLMs are typically trained on a very large corpus of diverse text and gain general understanding on a broad list of topics. While these LLMs are valuable out-of-the-box for many use-cases, they usually require focused training on specific domains where accuracy is key. This is where fine-tuning comes in."],[1835,"Larger models: Additional resources will be required"],[314,"Larger storage space is recommended for training clusters."],[3074,"Lastly, add a mapping google.subject to assertion.sub in the\nConfigure provider attributes step."],[3092,"Lastly, bind the Kubernetes service account to the bucket to allow access to the objects:"],[1850,"Lastly, the Dataset Mapping needs to be specified. See below for more information on these fields."],[1853,"Lastly, the Dataset Mapping needs to be specified. See below for more information on these fields."],[1112,"Lastly, the fine-tuning feature supports Weights and Biases (W&B) for tracking training metrics. If you would like to track your fine-tuning job with W&B, open the Advanced Configuration tab and enter your W&B project and job name that you would like the fine-tuning to be tracked as, and enter your W&B API token in the WandB API Key field."],[1103,"Lastly, we need to enter the hyperparameters for the job. Following the guidance of the paper, we can set the following parameters while keeping the other ones default."],[518,"Launch Data Preparation"],[635,"Launch Pre-training Script"],[521,"Launch the Pre-Training Job"],[1277,"Launching a Ray Job"],[1806,"Launching the Job"],[1391,"Launching the Starter Kit"],[35,"Learn about Batch Jobs on DGX Cloud Lepton."],[2214,"Learn about Lepton's authentication tokens, including User and Workspace tokens, and how to use them securely with the API, CLI, and SDKs."],[266,"Learn about the requirements for bringing your own compute to DGX Cloud Lepton."],[1715,"Learn about the resource shapes on DGX Cloud Lepton."],[1971,"Learn how NVIDIA support team can request temporary access to your workspace for troubleshooting, and how workspace administrators can manage these access requests."],[789,"Learn how to access a Dev Pod with VS Code."],[760,"Learn how to access JupyterLab on your Dev Pod via ngrok."],[212,"Learn how to add your machines to DGX Cloud Lepton with BYOC."],[16,"Learn how to bring your own compute to DGX Cloud Lepton."],[846,"Learn how to build container image in Dev Pod."],[802,"Learn how to configure SSH service and JupyterLab for nvcr.io/nvidia/pytorch:25.04-py3 image in Dev Pod"],[3141,"Learn how to configure SSH to access your Dev Pod"],[1548,"Learn how to configure the node group on DGX Cloud Lepton."],[1134,"Learn how to configure your batch job in DGX Cloud Lepton."],[1320,"Learn how to configure your dev pod in Lepton."],[743,"Learn how to connect Cursor to a dev pod."],[2027,"Learn how to connect private image registries to your DGX Cloud Lepton workspace, enabling secure deployment of custom container images for your applications."],[1423,"Learn how to create and manage dedicated endpoints in Lepton for AI model deployment, including LLM, custom container image, and NVIDIA NIM, with various configuration options."],[102,"Learn how to create and manage node groups on DGX Cloud Lepton."],[2131,"Learn how to create and manage roles and permissions for workspace members in DGX Cloud Lepton, including built-in roles and custom role creation."],[55,"Learn how to create and use a development pod on DGX Cloud Lepton."],[77,"Learn how to create and use endpoints on DGX Cloud Lepton for AI model deployment."],[245,"Learn how to create BYOC node groups in DGX Cloud Lepton."],[1493,"Learn how to create dedicated endpoints from NVIDIA NIM."],[1476,"Learn how to create endpoints from Docker images."],[3188,"Learn how to customize your container to meet your needs."],[990,"Learn how to deploy a Gradio app for interacting with the open source Dia TTS model."],[1025,"Learn how to deploy a multi-LLM compatible NIM on DGX Cloud Lepton."],[943,"Learn how to deploy GPT-OSS-120B for inference on DGX Cloud Lepton."],[1517,"Learn how to deploy LLM endpoints on DGX Cloud Lepton."],[370,"Learn how to diagnose job failures in DGX Cloud Lepton."],[454,"Learn how to download data from S3 using s5cmd in a DGX Cloud Lepton batch job."],[3094,"Learn how to enable and access JupyterLab on your Dev Pod."],[3031,"Learn how to federate your DGX Cloud Lepton environment with Amazon Cloud."],[3066,"Learn how to federate your DGX Cloud Lepton environment with Google Cloud."],[1604,"Learn how to operate nodes in a node group on DGX Cloud Lepton."],[1382,"Learn how to run a Starter Kit for a reproducible AI workflow in a Dev Pod."],[330,"Learn how to run distributed training with MPI in DGX Cloud Lepton."],[350,"Learn how to run distributed training with PyTorch in DGX Cloud Lepton."],[733,"Learn how to run jobs with Conda environment in DGX Cloud Lepton."],[867,"Learn how to run NCCL test on DGX Cloud Lepton."],[1753,"Learn how to run node validations on DGX Cloud Lepton."],[912,"Learn how to run the AlphaFold2 NIM on DGX Cloud Lepton."],[481,"Learn how to train a NanoGPT model with distributed training on DGX Cloud Lepton."],[1771,"Learn how to use domains on DGX Cloud Lepton."],[1635,"Learn how to use reservations on DGX Cloud Lepton."],[1920,"Learn how to use storage on DGX Cloud Lepton."],[3016,"Learn how workloads on DGX Cloud Lepton present themselves and how to federate them with your environment."],[117,"Learn what a workspace is and how to create and manage a workspace on DGX Cloud Lepton."],[1108,"Learning Rate: Enter 0.0003 which is the max learning rate used in the paper."],[1876,"Learning Rate: This is the learning rate used by the optimizer. It must be a positive float. Values typically range from 1e-7 to 1e-3 with most jobs being between 1e-5 and 5e-4. There isn't a perfect science for the optimal learning rate, so experimenting with different values could yield improved accuracy. Minimum: 0. Maximum: 1. Default: 0.0001."],[1489,"Leave other configurations at their defaults, or refer to endpoint configurations for details."],[1529,"Leave other configurations at their defaults, or refer to endpoint configurations for details."],[1542,"Leave other configurations at their defaults, or refer to endpoint configurations for details."],[3159,"Leave the Entrypoint (Run Command) set as the default Dev Pod entrypoint."],[2234,"lep [OPTIONS] COMMAND [ARGS]..."],[2246,"lep endpoint"],[2282,"lep endpoint [OPTIONS] COMMAND [ARGS]..."],[2251,"lep endpoint create"],[2295,"lep endpoint create [OPTIONS]"],[2272,"lep endpoint events"],[2394,"lep endpoint events [OPTIONS]"],[2275,"lep endpoint get"],[2398,"lep endpoint get [OPTIONS]"],[2254,"lep endpoint list"],[2344,"lep endpoint list [OPTIONS]"],[2266,"lep endpoint log"],[2362,"lep endpoint log [OPTIONS]"],[2260,"lep endpoint remove"],[2352,"lep endpoint remove [OPTIONS]"],[2257,"lep endpoint restart"],[2348,"lep endpoint restart [OPTIONS]"],[2263,"lep endpoint status"],[2356,"lep endpoint status [OPTIONS]"],[2278,"lep endpoint stop"],[2403,"lep endpoint stop [OPTIONS]"],[2269,"lep endpoint update"],[2367,"lep endpoint update [OPTIONS]"],[2406,"lep ingress"],[2424,"lep ingress [OPTIONS] COMMAND [ARGS]..."],[2414,"lep ingress create"],[2434,"lep ingress create [OPTIONS]"],[2420,"lep ingress delete"],[2442,"lep ingress delete [OPTIONS]"],[2417,"lep ingress get"],[2438,"lep ingress get [OPTIONS]"],[2411,"lep ingress list"],[2431,"lep ingress list [OPTIONS]"],[2232,"Lep is the main entry point for the DGX Cloud Lepton command-line interface.\nIt provides a set of commands to create and develop photons locally and\ndeploy them to DGX Cloud Lepton. It also provides a set of commands to\nmanage resources in the cloud, such as workspaces, deployments, secrets, and\nstorage. To install it, run:"],[2445,"lep job"],[2490,"lep job [OPTIONS] COMMAND [ARGS]..."],[2468,"lep job clone"],[2588,"lep job clone [OPTIONS]"],[2450,"lep job create"],[2507,"lep job create [OPTIONS]"],[2486,"lep job events"],[2616,"lep job events [OPTIONS]"],[2462,"lep job get"],[2572,"lep job get [OPTIONS]"],[2453,"lep job list"],[2549,"lep job list [OPTIONS]"],[2471,"lep job log"],[2592,"lep job log [OPTIONS]"],[2477,"lep job nodes"],[2601,"lep job nodes [OPTIONS]"],[2465,"lep job remove"],[2581,"lep job remove [OPTIONS]"],[2456,"lep job remove-all"],[2556,"lep job remove-all [OPTIONS]"],[2474,"lep job replicas"],[2597,"lep job replicas [OPTIONS]"],[2483,"lep job start"],[2611,"lep job start [OPTIONS]"],[2480,"lep job stop"],[2606,"lep job stop [OPTIONS]"],[2459,"lep job stop-all"],[2563,"lep job stop-all [OPTIONS]"],[2619,"lep log"],[2628,"lep log [OPTIONS] COMMAND [ARGS]..."],[2624,"lep log get"],[2632,"lep log get [OPTIONS]"],[2653,"lep login"],[2658,"lep login [OPTIONS]"],[2663,"lep logout"],[2668,"lep logout [OPTIONS]"],[2671,"lep node"],[2683,"lep node [OPTIONS] COMMAND [ARGS]..."],[2676,"lep node list"],[2689,"lep node list [OPTIONS]"],[2679,"lep node resource-shape"],[2695,"lep node resource-shape [OPTIONS]"],[2698,"lep pod"],[2722,"lep pod [OPTIONS] COMMAND [ARGS]..."],[2703,"lep pod create"],[2731,"lep pod create [OPTIONS]"],[2706,"lep pod get"],[2757,"lep pod get [OPTIONS]"],[2709,"lep pod list"],[2762,"lep pod list [OPTIONS]"],[2712,"lep pod remove"],[2767,"lep pod remove [OPTIONS]"],[2715,"lep pod ssh"],[2771,"lep pod ssh [OPTIONS]"],[2718,"lep pod stop"],[2775,"lep pod stop [OPTIONS]"],[2778,"lep raycluster"],[2802,"lep raycluster [OPTIONS] COMMAND [ARGS]..."],[2786,"lep raycluster create"],[2815,"lep raycluster create [OPTIONS]"],[2792,"lep raycluster get"],[2851,"lep raycluster get [OPTIONS]"],[2783,"lep raycluster list"],[2811,"lep raycluster list [OPTIONS]"],[2789,"lep raycluster remove"],[2847,"lep raycluster remove [OPTIONS]"],[2798,"lep raycluster submit"],[2863,"lep raycluster submit [OPTIONS] [ENTRYPOINT]..."],[2795,"lep raycluster update"],[2857,"lep raycluster update [OPTIONS]"],[2876,"lep secret"],[2891,"lep secret [OPTIONS] COMMAND [ARGS]..."],[2881,"lep secret create"],[2897,"lep secret create [OPTIONS]"],[2884,"lep secret list"],[2903,"lep secret list [OPTIONS]"],[2887,"lep secret remove"],[2906,"lep secret remove [OPTIONS]"],[2909,"lep template"],[2918,"lep template [OPTIONS] COMMAND [ARGS]..."],[2914,"lep template list"],[2922,"lep template list [OPTIONS]"],[2924,"lep workspace"],[2957,"lep workspace [OPTIONS] COMMAND [ARGS]..."],[2944,"lep workspace id"],[2990,"lep workspace id [OPTIONS]"],[2935,"lep workspace list"],[2980,"lep workspace list [OPTIONS]"],[2929,"lep workspace login"],[2969,"lep workspace login [OPTIONS]"],[2932,"lep workspace logout"],[2975,"lep workspace logout [OPTIONS]"],[2938,"lep workspace remove"],[2983,"lep workspace remove [OPTIONS]"],[2941,"lep workspace removeall"],[2987,"lep workspace removeall [OPTIONS]"],[2953,"lep workspace status"],[2999,"lep workspace status [OPTIONS]"],[2947,"lep workspace token"],[2993,"lep workspace token [OPTIONS]"],[2950,"lep workspace url"],[2996,"lep workspace url [OPTIONS]"],[452,"Lepton CLI Reference"],[2228,"Lepton Command-Line Interface (CLI) reference"],[109,"Lepton Managed Node Group: All resources are managed by DGX Cloud Lepton."],[251,"Lepton Managed Node Group: All resources are managed by DGX Cloud Lepton."],[110,"Lepton Managed Node Groups will be created by your Technical Account Manager(TAM) for you to use."],[3241,"Lepton workspace ID"],[3236,"lepton_deployment_name"],[1185,"LEPTON_JOB_FAILURE_COUNT"],[3234,"lepton_job_name"],[1203,"LEPTON_JOB_NAME"],[1153,"LEPTON_JOB_NAME: The name of the job"],[1197,"LEPTON_JOB_SERVICE_PREFIX"],[1188,"LEPTON_JOB_TOTAL_WORKERS"],[1173,"LEPTON_JOB_WORKER_HOSTNAME_PREFIX"],[1191,"LEPTON_JOB_WORKER_INDEX"],[1200,"LEPTON_JOB_WORKER_PREFIX"],[3238,"lepton_replica_id"],[1170,"LEPTON_RESOURCE_ACCELERATOR_NUM"],[1179,"LEPTON_RESOURCE_ACCELERATOR_TYPE"],[1154,"LEPTON_RESOURCE_ACCELERATOR_TYPE: The resource accelerator type of the job"],[1194,"LEPTON_SUBDOMAIN"],[1206,"LEPTON_VIRTUAL_ENV"],[1182,"LEPTON_WORKER_ID"],[3240,"lepton_workspace"],[1176,"LEPTON_WORKSPACE_ID"],[377,"Let's walk through an example of how to use this feature to diagnose a job failure."],[22,"Leverage your existing hardware investments"],[2429,"list : List all ingress"],[2920,"list : List all templates with Name/ID/Workload type."],[2960,"list : List current workspaces and their urls on record."],[2287,"list : Lists all endpoints in the current workspace."],[2496,"list : Lists all jobs in the current workspace."],[2685,"list : Lists all node groups in the current workspace."],[2726,"list : Lists all pods in the current workspace."],[2806,"list : Lists all Ray clusters in the current workspace."],[2894,"list : Lists all secrets in the current workspace."],[2430,"List all ingress"],[2921,"List all templates with Name/ID/Workload type."],[2978,"List current workspaces and their urls on record. For any workspace\ndisplayed in the list, you can log in to it by `lep workspace login -i"],[390,"List Jobs"],[2693,"List resource shapes per node group."],[1718,"List Shapes from CLI"],[2343,"Lists all endpoints in the current workspace."],[2546,"Lists all jobs in the current workspace."],[2687,"Lists all node groups in the current workspace."],[2761,"Lists all pods in the current workspace."],[2810,"Lists all Ray clusters in the current workspace."],[2902,"Lists all secrets in the current workspace. Note that the secret values are\nalways hidden."],[2393,"Lists events of the endpoint"],[1468,"Liveness probe: Initial delay of 600 seconds (10 minutes); checks every 5 seconds. One success marks the container as healthy; 12 consecutive failures mark it as unhealthy. Ensures the service remains healthy during operation."],[988,"LLMs follow the OpenAI API standard. For more information, refer to the official documentation."],[1053,"LLMs follow the OpenAI API standard. For more information, refer to the official documentation."],[1796,"Load from Hugging Face"],[1799,"Load from Hugging Face"],[1797,"Load from Storage"],[1800,"Load from Storage"],[1875,"Local Batch Size: This is the amount of pipelining to perform on each GPU. Increasing the local batch size can improve throughput at the cost of additional GPU memory. This must be a positive integer and is most commonly 1 but additional throughput could be gained with 2 or 4 here as well. Minimum: 1. Default: 1."],[2497,"log : Gets the log of a job."],[2288,"log : Gets the log of an endpoint."],[2239,"log : Manage and retrieve the logs history of specific jobs,..."],[1438,"Log Collection"],[1345,"Log Collection: Whether to collect logs from the container (follows the workspace-level setting by default)."],[1413,"Log Collection: Whether to collect logs from the container. Follows the workspace-level setting by default."],[1150,"Log Collection: Whether to collect the logs from the container. Follows the workspace-level setting by default."],[3250,"Log Context"],[2240,"login : Login to the DGX Cloud Lepton."],[2961,"login : Logs in to a workspace."],[3211,"Login to the DGX Cloud Lepton Dashboard."],[2657,"Login to the DGX Cloud Lepton. This will open a browser window to the Lepton\nlogin page if credentials are not given. You will be redirected to a page with\nthe credentials string. Copy the string and paste it into the terminal, and\nyou will be logged in."],[2654,"Login to the DGX Cloud Lepton. This will open a browser window to the Lepton login page if credentials are not given. You will be redirected to a page with the credentials string. Copy the string and paste it into the terminal, and you will be logged in."],[2241,"logout : Logout of the DGX Cloud Lepton."],[2962,"logout : Logout of the DGX Cloud Lepton."],[2664,"Logout of the DGX Cloud Lepton."],[2667,"Logout of the DGX Cloud Lepton."],[2974,"Logout of the DGX Cloud Lepton."],[167,"Logs"],[3242,"Logs"],[2152,"Logs (View and access system and application logs)"],[3252,"Logs are generated by the replicas in jobs and pods, including all standard output and error output (print or echo)."],[3253,"Logs are real-time and can be viewed as soon as the replicas in deployments, jobs, and pods are running. Real-time logs are no longer available once the workloads are deleted or terminated."],[3199,"Logs Configuration"],[3214,"Logs Export: Exports logs for all workloads created in this workspace. Logs are streamed in real time to the specified external destination."],[2968,"Logs in to a workspace. This also verifies that the workspace is accessible."],[1882,"LoRA Alpha: This is the alpha parameter for scaling the adaptation strength. Higher values give stronger weight to the adapter layers. Typical values are powers-of-two (1, 2, 4, 8, ...). This must be a positive integer. Minimum: 1. Default: 32."],[1881,"LoRA Rank: This is the dimension to use for LoRA while fine-tuning and corresponds to the number of parameters in the adapter layers. Larger numbers tend to yield better results. Typical values are powers-of-two (1, 2, 4, 8, ...). This must be a positive integer. Minimum: 1. Default: 16."],[1833,"LoRA: Single 80+ GB GPU"],[1819,"Low-Rank Adaptation (LoRA) is a PEFT technique that freezes the base model weights and adds additional low-rank adapter modules, making it more computationally efficient and requiring fewer resources. This creates a small model adapter file that instructs a deployed model how to respond with the new weights. LoRA is best suited for targeted use cases and requires fewer examples to learn the expected output structure."],[23,"Maintain control over your physical infrastructure"],[849,"Make sure privilege mode is enabled"],[595,"Make sure your Python virtual environment is activated before running this command."],[2627,"Manage and retrieve the logs history of specific jobs, deployments and\nreplicas."],[2620,"Manage and retrieve the logs history of specific jobs, deployments and replicas."],[1773,"Manage domain routing rules"],[2247,"Manage endpoints (formerly called deployments) on the DGX Cloud Lepton."],[2281,"Manage endpoints (formerly called deployments) on the DGX Cloud Lepton."],[2407,"Manage ingress on the DGX Cloud Lepton."],[2423,"Manage ingress on the DGX Cloud Lepton."],[2672,"Manage nodes on the DGX Cloud Lepton."],[2682,"Manage nodes on the DGX Cloud Lepton."],[2779,"Manage Ray clusters on DGX Cloud Lepton."],[2801,"Manage Ray clusters on DGX Cloud Lepton."],[2877,"Manage secrets on the DGX Cloud Lepton."],[2890,"Manage secrets on the DGX Cloud Lepton."],[2910,"Manage templates (list only)."],[2917,"Manage templates (list only)."],[1945,"Manage the permission for paths within a volume by clicking on the Permission button in the volume row."],[2925,"Manage workspace access to the DGX Cloud Lepton."],[2956,"Manage workspace access to the DGX Cloud Lepton."],[2020,"Manage workspace collaboration in DGX Cloud Lepton by inviting team members, assigning roles, and controlling access permissions for your projects."],[30,"Manage Your Nodes: After importing, manage your BYOC nodes through the node group management interface."],[2446,"Manages Lepton Jobs."],[2489,"Manages Lepton Jobs."],[2699,"Manages pods on the DGX Cloud Lepton."],[2721,"Manages pods on the DGX Cloud Lepton."],[1973,"Managing Access Requests"],[2021,"Managing Members"],[2173,"Managing Secrets"],[1658,"Manual Approval: Admins can review and approve/deny requests."],[1214,"MASTER_ADDR"],[1244,"MASTER_ADDR: Hostname of the primary node (first node)"],[1245,"MASTER_IP: IP address of the primary node"],[1263,"MASTER_IP: IP address of the primary node"],[1217,"MASTER_PORT"],[1158,"Max job failure retry: Maximum number of failure restarts of the entire job."],[1595,"Max Priority: Maximum priority for the user."],[1586,"Max Priority: Maximum priority for the workspace."],[1157,"Max replica failure retry: Maximum number of times to retry a failed replica. Defaults to zero."],[1869,"Max Steps: This is the total number of backward passes to complete as the model learns. Once the Max Steps value is reached, fine-tuning will complete. This must be a positive integer. This can range from 10s to millions of steps depending on your dataset and other settings. If not specified, fine-tuning will run until the Epochs value is reached. Minimum: 1. Default: Infinite."],[3013,"Maximum number of processes allowed per pod"],[1212,"Meaning"],[129,"Members"],[1926,"Members"],[2019,"Members"],[2153,"Members (Manage and invite workspace members)"],[278,"Memory"],[1734,"Memory: The amount of memory reserved for the shape, measured in MB."],[1594,"Memory: Total memory quota for the user in GB (default: unlimited)."],[1585,"Memory: Total memory quota for the workspace in GB (default: unlimited)."],[3203,"Metrics Configuration"],[3215,"Metrics Export: Collects and exports all custom metrics instrumented by developers within their applications from workloads created in this workspace."],[384,"Migrate Slurm Scripts to DGX Cloud Lepton Jobs"],[385,"Migrate Slurm scripts to DGX Cloud Lepton jobs, including job creation, resource allocation, and environment configuration."],[1109,"Min Learning Rate: Enter 0.0000003 which is the minimum learning rate used in the paper that the optimizer will decay to."],[1877,"Min Learning Rate: This is the minimum learning rate used by the optimizer. It will decay to the minimum as the training progresses. This must be a positive float. Common practice has the minimum learning rate being 10x smaller than the maximum learning rate. Minimum: 0. Maximum: 1. Default: 0.000001."],[316,"Minimum 256GB RAM per GPU"],[306,"Minimum 640GB storage per GPU on the OS disk"],[1795,"Model"],[1788,"Model Architecture and Fine-Tuning Technique Support"],[1059,"Model Preparation"],[3190,"Monitor and troubleshoot your DGX Cloud Lepton applications with audit logs."],[3243,"Monitor and troubleshoot your DGX Cloud Lepton applications with real-time logs and advanced logs."],[458,"Monitor Progress"],[2018,"Monitor Support Personnel Activity: All actions performed by support personnel within a workspace are also audited."],[1776,"Monitor traffic"],[168,"Monitoring"],[682,"Monitoring"],[1061,"Monitoring Fine-Tuning"],[1807,"Monitoring the Job"],[189,"More information on each value:"],[1132,"Moshkov, I., Hanley, D., Sorokin, I., Toshniwal, S., Henkel, C., Schifferer, B., Du, W., Gitman, I. (2025). AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.16891"],[160,"Most tasks available to users on DGX Cloud Lepton are supported via the SDK. The following API resources are accessible:"],[1968,"Mount path: Specifies the path where the directory will be mounted in the container. For example, to mount the volume's root directory to /mnt in the container, set the \"Mount path\" to /mnt."],[1462,"Mount storage for the deployment container. Refer to this guide for details."],[1344,"Mount storage for the pod container. See this guide for details."],[191,"mount_path: This is the directory the path directory from storage will be mounted inside the container. Putting /my-mount will mount the path directory from storage at /my-mount inside the container. Note that this value cannot be /."],[141,"Mounting shared storage"],[453,"Moving Data from S3 Using s5cmd"],[663,"Moving on, scale the job up to eight GPUs for a single node. Edit the train.py script and change this line:"],[338,"MPI (Message Passing Interface) is a standard for parallel computing. It is a message-passing library that allows for the communication and coordination of processes in a distributed environment."],[1242,"MPI Command: The template provides a default command. You can customize it using the predefined environment variables provided by the MPI template."],[1232,"MPI Template"],[1235,"MPI: For distributed computing with MPI"],[1031,"Multi-LLM compatible NIM is a single container that supports a broad range of models for flexible deployment. The multi-LLM NIM container is recommended for models that NVIDIA has not yet created a specific container for. Models can be loaded from local storage, Hugging Face, or NGC. Refer to the official documentation for more information about NVIDIA NIM for LLMs."],[1024,"Multi-LLM NIM Deployment"],[2548,"Multiple filters can be combined. For example: lep job list -s queue -u\nalice -n train -ng h100"],[1836,"Multiple nodes may be necessary, especially for SFT on larger models. To increase the number of nodes, update the Worker option to the desired amount."],[1917,"My dataset combines the input and output into a single column"],[1791,"Name"],[1204,"Name assigned to the job"],[2126,"Name: A name for your registry."],[1089,"Name: Give the fine-tuning job a name, such as qwen25-open-math-reasoning."],[499,"Name: nccl-benchmark or any name you want."],[428,"Name: test-job as the job name configured in the Slurm script."],[1732,"Name: The identifier for the shape, used to distinguish it when selecting or configuring resources."],[2082,"Name: The name for the new registry, to help you manage registries on DGX Cloud Lepton."],[480,"NanoGPT Training"],[1907,"Navigate into the latest sub-directory which represents the highest step count. This is your most recent checkpoint. The final consolidated checkpoint will be located in the model/consolidated directory inside of the latest epoch_X_step_YYYY sub-directory."],[818,"Navigate to Dev Pod Creation: Go to the Dev Pod creation page in your DGX Cloud Lepton dashboard."],[378,"Navigate to the Batch Jobs page to create the job with the following configuration:"],[427,"Navigate to the Create a Job page, and configure the following fields:"],[930,"Navigate to the create endpoint page on the dashboard."],[1036,"Navigate to the create endpoint page on the dashboard."],[1506,"Navigate to the Create Endpoint page on the dashboard."],[473,"Navigate to the create job page to configure your data download job."],[484,"Navigate to the create job page where you can see the job configuration form."],[40,"Navigate to the create job page."],[85,"Navigate to the create LLM endpoint page."],[63,"Navigate to the create pod page on the dashboard, where you can configure and launch a Dev Pod using a custom container image."],[2197,"Navigate to the Create Templates page on your workspace, where you can see a detailed form to create a template."],[234,"Navigate to the detailed page of the node group you want to add machines to, click the Add Machines button in the Nodes tab, and select Add via Local Command."],[226,"Navigate to the detailed page of the node group you want to add machines to, click the Add Machines button in the Nodes tab, and select Add via SSH Access."],[3173,"Navigate to the Dev Pod creation page. In the Container Configuration section, select Custom image, then enter the URL for your container image."],[3158,"Navigate to the Dev Pod creation page. In the Container Configuration section, select one of the default Built-in images."],[2167,"Navigate to the Members tab on the left side of the workspace settings page."],[2024,"Navigate to the Members tab on the left-hand side of the workspace settings page.\nYou will see a list of all members in the current workspace. If you have an admin role, you can invite, remove, and assign roles to members from this page.\nOnce a member is invited, they will receive an email notification with an invitation link to join the workspace."],[253,"Navigate to the Node Groups page, click Create Node Group, and select BYOC."],[3212,"Navigate to the Observability settings page"],[2062,"Navigate to the personal access tokens page and click Generate New Token.\nIn the creation form, you need to fill in the following fields:"],[3183,"Navigate to the pod details page and switch to the Terminal tab to open the web terminal for the pod."],[2050,"Navigate to the registries tab in the settings page of your workspace, then click the New registry auth button in the top right corner. You can choose a registry source from the list."],[2090,"Navigate to the registry settings page, click on New registry auth, and select Azure as the source."],[2124,"Navigate to the registry settings page, click on New registry auth, and select Custom as the source."],[2068,"Navigate to the registry settings page, click on New registry auth, and select Docker Hub as the source."],[2080,"Navigate to the registry settings page, click on New registry auth, and select GCP as the source."],[2106,"Navigate to the registry settings page, click on New registry auth, and select GitHub as the source."],[2120,"Navigate to the registry settings page, click on New registry auth, and select NVIDIA as the source.\nYou need to fill in the following fields:"],[2136,"Navigate to the Roles tab on the left side of the workspace settings page."],[2178,"Navigate to the Secrets tab on the left-hand side of the workspace settings page.\nYou will see a list of all the secrets you have created. You can add, edit, or delete secrets from this page."],[2075,"Navigate to the service accounts page in the GCP console, select the target project, then click the Create service account button."],[465,"Navigate to the Settings page in your workspace and click Secrets on the left side."],[1939,"Navigate to the storage page on the DGX Cloud Lepton dashboard. You can view the storage list grouped by node groups."],[2003,"Navigate to the Temporary Access tab within Settings."],[2013,"Navigate to the Temporary Access tab within Settings."],[1650,"Navigate to Workspace > Nodes > Config > Reservations."],[2087,"Navigate to your Azure portal and locate your Azure Container Registry page.\nIf there are no registries in your Azure account yet, you need to create one first."],[1730,"Navigate to your node group details page, click the Config dropdown, and select Shapes. You'll be taken to a new page to define custom shapes."],[1749,"Navigate to your node group details page, click the Config dropdown, and select Shapes. You'll be taken to a page where you can disable or enable default shapes."],[491,"NCCL Performance Benchmark Job"],[1760,"NCCL Test"],[877,"NCCL test is a tool to verify bandwidth among nodes that have GPUs with InfiniBand (IB) or RoCE connectivity."],[879,"NCCL Test requires 2 or more Dev Pods, each utilizing a full node. In this example, we will create 2 Dev Pods, each uses 8x H100 GPUs."],[153,"Nearly all workflows using the Python SDK follow the same basic flow:"],[1101,"Neither the datasets nor the model are gated on Hugging Face, but if any of them were, you would need to add your Hugging Face Token in the indicated field and accept any licenses on the model or dataset's page on Hugging Face."],[529,"NeMo 2.0 now uses a Pythonic API that allows it to be integrated with IDEs such as Visual Studio Code (VS Code) and supports type checking."],[581,"NeMo Framework contains many predefined configurations for various models, including the Nemotron-H models. This section will demonstrate how to initiate training a Nemotron-H 8B model on DGX Cloud Lepton using the preprocessed Nemotron-CC dataset."],[507,"NeMo Framework End-to-End Workflow Example"],[597,"NeMo Framework is fully integrated with Weights & Biases and logs multiple metrics that can be viewable on their website. If the W&B key was provided in the command, a new W&B project will automatically be created and metrics will be uploaded there. Viewing logs on W&B is recommended as the best path to monitor training progress."],[552,"NeMo Framework on DGX Cloud Lepton leverages the Lepton Python SDK to upload data to the cluster and schedule jobs. To use the Python SDK, you need to authenticate with the cluster using the Lepton CLI tool installed in the previous step. In your active virtual environment, run:"],[630,"NeMo Framework Quick Start Guide"],[555,"NeMo Framework supports processing custom text-based datasets for pre-training new models. The data preprocessor requires datasets to be cleansed, excluding any sensitive or improperly formatted data that is unsuitable for use during pre-training. Each file in the dataset must be in .json or, ideally, .jsonl format. Datasets can be downloaded from external sources or uploaded directly to the remote filesystem."],[674,"NeMo-RL Quick Start"],[531,"Nemotron-H is a hybrid SSM that leverages the Mamba architecture for faster training and inference speeds. Recent studies have proven this hybrid to be very robust, with models yielding slightly higher accuracy and much quicker training times and faster inference throughput compared to pure transformer-based equivalents, as found by Blakeman et al. (2025). With the rise of reasoning models, there is an increasing shift toward inference performance to meet the large output token demand. The hybrid SSM architecture of Nemotron-H is an excellent candidate for reasoning models given the high inference throughput."],[279,"Network Configuration"],[280,"Network Requirements"],[4,"Next Steps"],[19,"Next Steps"],[38,"Next Steps"],[59,"Next Steps"],[82,"Next Steps"],[106,"Next Steps"],[122,"Next Steps"],[641,"Next Steps"],[683,"Next Steps"],[3152,"Next Steps"],[1095,"Next we need to specify the Training Dataset which is where we input the OpenMathReasoning dataset listed in the paper."],[148,"Next, authenticate with your workspace:"],[3076,"Next, click CONNECTED SERVICE ACCOUNTS in the upper right corner and then\nselect the DOWNLOAD CONFIG button. When the Configure your application\ndialog comes up, select the provider you created and then paste the path\n/var/run/secrets/kubernetes.io/serviceaccount/token into the OIDC ID token path\nfield, leaving the format as \"text\" and then click DOWNLOAD CONFIG to download the\nrequired configuration file."],[3090,"Next, create a workload identity pool\nand an OIDC provider for that pool using the OIDC URL provided in the DGX Cloud Lepton console under NodeGroup:"],[3088,"Next, define some variables for the Kubernetes-related resources that will be used later:"],[619,"Next, enter a prompt, such as Write me a short story about a baby dragon that learns to fly and hit the Send button to initiate the request. The request will be sent to the deployed model and will stream the response once ready. The form will stream the generated response from the model."],[1102,"Next, enter the location for the Checkpoint Output Path. This is where the checkpoints will be saved. This must start with the storage mount path, such as /storage. Enter a location such as /storage/my-checkpoints/qwen2.5-math-1.5b."],[660,"Next, launch the pre-training job with:"],[1496,"NGC API Key"],[934,"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."],[963,"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."],[1043,"NGC API key: Select one of the existing NGC API keys you saved in your workspace as a secret or create a new one."],[1251,"NGPUS: Number of GPUs on the current node"],[1249,"NNODES: Total workers for the current job"],[2011,"No Addition to Lepton Workspace or NCA: The support specialist will only be able to access the Workspace; they will not be added to your Lepton Workspace or NCA."],[1787,"No-code interface for fine-tuning Large Language Models"],[2242,"node : Manage nodes on the DGX Cloud Lepton."],[101,"Node Group"],[408,"Node Group / Node ID"],[400,"Node Group and Queue Priority"],[1547,"Node Group Configuration"],[254,"Node Group Name: The name of the node group."],[1890,"Node group the job is running on."],[1326,"Node Group: First, select one or more node groups to determine which resources the pod can use."],[1139,"Node Group: Select one or more node groups to determine what resources this job can use."],[1402,"Node Group: Select the desired node group to run the Dev Pod in."],[1076,"Node Group: Select the node group to run the job in. This must be the same as the node group you will use for fine-tuning as it must share storage."],[1090,"Node Group: Select the same node group that was used for the model preparation in the previous section."],[169,"Node Groups"],[2154,"Node Groups (Configure and manage node groups)"],[249,"Node groups let you group multiple nodes for a given workload. There are two types of node groups:"],[6,"Node Groups: Create node groups to manage compute resources and run workloads."],[1932,"Node Local Volume data is not replicated across different nodes."],[1931,"Node Local Volume: Node Local Volume persists data across workload lifecycles on the same node. It stores data directly on the node where your workload runs."],[1603,"Node Operations"],[2155,"Node Reservations (Reserve and manage compute nodes)"],[1752,"Node Validations"],[570,"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."],[585,"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."],[657,"node_group=\"xxxxx\": Replace xxxxx with the node group to run in. The list of available node groups can be found in the Nodes tab in the UI."],[1226,"NODE_RANK"],[1250,"NODE_RANK: Rank of the current node among all workers"],[2498,"nodes : Prints the nodes id of a job."],[1142,"Nodes: Defaults to no specific nodes, but you can specify the nodes you want to launch the job on."],[1329,"Nodes: No specific nodes are selected by default, but you can target particular nodes to launch the pod on."],[418,"Not Supported Yet"],[1842,"Note that if you are trying to access a gated model which requires approval, you must add your Hugging Face token in the job submission form. More on this below."],[199,"Note that image_pull_secrets expects a list of strings, allowing multiple secrets to be added."],[2099,"Note: A note for you to identify the token usage."],[2541,"Note: By default, all times are interpreted in your local timezone. To specify UTC time, prefix your input with 'UTC:' or 'utc:'. For example:"],[313,"Note: DGX Cloud Lepton will automatically combine all data disks into a single volume."],[2751,"Notice: Exposing container ports may increase your service's security risk. Please implement appropriate authentication and security controls; you are solely responsible for the security of any services exposed."],[2638,"now\nExample: now (indicates the current time in UTC)"],[2644,"now\nExample: now (indicates the current time in UTC)"],[3059,"Now create a JSON file that defines an AWS Security Token Service (STS) role policy\nusing the OIDC URL. Note the use of Bash variable manipulation to omit the scheme from the URL.\nThen create the role using the AWS CLI:"],[3060,"Now define and create the AWS policy for accessing the S3 bucket in another JSON file:"],[3091,"Now download the clientLibraryConfig.json file, which you use within your workload to configure\nthe Google Cloud tools and libraries (as shown above):"],[3061,"Now that you have created both a role and bucket access policy, attach the policy to that role:"],[603,"Now that you have finished pre-training a base model and converted it to the Hugging Face format, you can deploy it for inference as a NIM and send requests to the deployed model to do quick human evaluations."],[2105,"Now that you have the personal access token from GitHub, you can create a new registry auth."],[2079,"Now that you have the service account key JSON file, you can create a new registry auth for GCP on DGX Cloud Lepton."],[583,"Now the training job can be defined. The following script is used to launch pre-training of a Nemotron-H 8B model for one trillion tokens using the Nemotron-CC dataset that was prepared. Save the script to nemotronh-pretrain.py locally using your VS Code session. Note that, as with data preparation earlier, several lines will need to be modified to reflect your cluster. These lines are as follows:"],[787,"Now you can access JupyterLab via the public URL from ngrok and the token you set in step 2."],[862,"Now you can build your container image in the dev pod. First, create a Dockerfile with your desired configuration. Here's a simple sample Dockerfile:"],[863,"Now you can build your container image using the following command:"],[347,"Now you can click the Create button to create and run the job. After that, you can check the job logs or details to monitor the job."],[367,"Now you can click the Create button to create and run the job. After that, you can check the job logs or details to monitor the job."],[785,"Now you can detach from the tmux session by pressing Ctrl+b and then d."],[2201,"Now you can follow the page instructions to fill in the configuration for your template based on the selected workload type."],[652,"Now you can launch a pre-training job with NeMo Framework using synthetic data. The system will generate fake data during pre-training. While the resulting trained model won't be meaningful, this process helps validate the entire software stack and assess performance."],[426,"Now, convert this to a batch job on DGX Cloud Lepton."],[1733,"Number of CPU: The total number of CPU cores allocated to the shape."],[1186,"Number of failed job attempts"],[1736,"Number of GPU: The number of GPUs allocated to the shape. Set to \"None\" to create a CPU‑only shape."],[1171,"Number of hardware accelerators allocated"],[2044,"NVIDIA"],[119,"NVIDIA Cloud Account"],[2056,"NVIDIA Container Registry (NCR)"],[270,"NVIDIA Driver and NVIDIA Fabric Manager"],[910,"NVIDIA NeMo Framework supports W&B natively. To use W&B with NeMo Framework, set your W&B key as an environment variable named WANDB_API_KEY. Refer to the documentation on integrating W&B for your specific NeMo Framework job."],[684,"NVIDIA NeMo-RL is a scalable library for post-training models and supports various techniques such as GRPO, DPO, and SFT. NeMo-RL leverages Ray for scheduling and management of resources. This guide showcases how to create a lightweight RayCluster on DGX Cloud Lepton and launch jobs with NeMo-RL."],[300,"NVIDIA recommends removing these packages from the system prior to adding the system to DGX Cloud Lepton."],[1495,"NVIDIA Registry"],[1181,"NVIDIA-A100-80GB"],[2039,"Obtain Registry Credentials"],[227,"Obtain the IP address and username of the machine you want to add."],[881,"On each of Pod 2 to Pod N, prepare the .ssh directory and add the generated public key from Pod 1 to authorized_keys:"],[880,"On Pod 1, generate an SSH key pair and add the public key to the list of authorized keys:"],[884,"On Pod 2 to Pod N, run the following command:"],[554,"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."],[651,"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."],[1745,"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back in your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."],[150,"On the credentials page, create an authentication token by following the prompts. The page will display a secret token which is used for authentication. Copy the workspace ID and token shown in the second field and paste it back into your terminal. The format should look like xxxxxx:**************************. You should now be authenticated with DGX Cloud Lepton. You only need to authenticate once locally as long as your credentials remain valid."],[2184,"On the dashboard, you can add secrets as environment variables to your deployments, jobs, and pods.\nConfigure them under advanced settings when creating or editing a deployment, job, or pod."],[2166,"Once a role is created, it can be assigned to users."],[580,"Once all files have been preprocessed, it is time to begin pre-training the model."],[565,"Once all of the files have been concatenated, it is time to preprocess the dataset. The preprocessing phase tokenizes each dataset file using the Nemotron-H 8B Base tokenizer, which is downloaded from Hugging Face, and creates .bin and .idx files for each concatenated file. As with the other scripts, this one divides the work amongst all available workers to speed up preprocessing. Using the local VS Code session, save the following script in the local directory as data_prep/preprocess.py."],[1887,"Once all settings in the form have been configured, click the green Create button at the bottom of the page. This will schedule a fine-tuning job on the cluster and run it once resources are ready. You will be taken back to the Fine-tune job page and should see the job listed at the top of the queue per the image above."],[574,"Once all the scripts are saved in the specified location, it is time to launch the preprocessing job. NeMo-Run will launch the job automatically on the cluster, so starting data preparation is as simple as running a Python command. Launch data preparation with the following command in the terminal of your local VS Code session:"],[3075,"Once complete, the Workload Identity Pool will confirm that it can communicate\nwith the dgx-cloud Kubernetes cluster. You should see a green checkmark in\nthe upper left corner near the provider you created, as shown below."],[836,"Once connected via SSH, you can verify the PyTorch environment:"],[1491,"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."],[1531,"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."],[1544,"Once created, the endpoint appears on the Endpoints page. View logs for each replica by clicking the Logs button in the Replica section."],[550,"Once dependencies are installed, you can define the data preparation and training scripts using VS Code."],[1743,"Once installed, run:"],[437,"Once installed, the CLI can be invoked with lep. To see the list of available commands, run:"],[209,"Once registered, you can file support cases through ESP by clicking the Support Portal link in the Support dropdown menu in the top right of the DGX Cloud Lepton dashboard."],[865,"Once the container image is built, you can push it to a remote repository for further use with docker image push command."],[1899,"Once the container is ready, the job will be Running and the fine-tuning process begins. After fine-tuning finishes, it will transition to Completed and the resources are freed for other jobs."],[1118,"Once the dataset has been prepared, training will begin. You will know it has started when you see lines similar to the following:"],[699,"Once the dev pod completes, the image should be available at the container registry you specified at the end of the entrypoint command."],[1010,"Once the dev pod completes, the image should be available at the container registry you specified at the end of the entrypoint command."],[1417,"Once the Dev Pod creation form is complete, click the Create button at the bottom of the page. This queues a Dev Pod with the requested resources in the specified node group. When resources become available, the container launches and the JupyterLab session begins."],[3120,"Once the Dev Pod is created and ready, go to the details page and find connection information on the JupyterLab tab."],[859,"Once the dev pod is created, you can connect to the dev pod via web terminal. Then copy the following script to the terminal and run it."],[1298,"Once the form is complete, click the Create button at the bottom of the page. You are returned to the list of RayClusters running in the workspace. When resources are available, the specified containers launch on each resource and the RayCluster starts. Depending on container size and requested resources, it can take a while for the RayCluster to reach the Ready state. When ready, the list looks similar to the following:"],[1020,"Once the form is complete, click the Create button to deploy the endpoint."],[1083,"Once the form is complete, click the green Create button at the bottom of the page to queue the job once resources are available."],[1113,"Once the form is fully completed, click the green Create button at the bottom of the page to schedule the fine-tuning job."],[741,"Once the job is created, you can view the job in the dashboard."],[112,"Once the node group is available, you can view its details on the node group list page."],[796,"Once the pod is created, you'll need to  grant access to the VS Code server. Go to the Dev Pod details page, click on the Logs tab, and find the following line:"],[754,"Once the pod is created, you'll need to grant access to the VS Code server which is running the Cursor dev tunnel. Go to the Dev Pod details page, click on the Logs tab, and find the following line:"],[713,"Once the RayCluster is running and all pods are ready, select the job in the Batch Jobs queue. In the Replicas tab at the bottom of the screen, select the Terminal button for the first replica in the list. This will open a terminal session directly in the container for the head node where you can launch commands."],[3057,"Once the S3 bucket and OIDC identity provider are created, set up Bash environment variables specific\nto the AWS-related requirements. Note that the $S3_REGION will depend on where you provisioned your bucket."],[205,"Once the script completes, the launched job should be viewable in the UI."],[860,"Once the script is executed, you can verify the GPU setup by running the following command."],[479,"Once the status shows \"Completed\", click on the job name and go to the LOGS tab to verify the data transfer was successful. The logs should show the s5cmd output indicating successful data transfer from your S3 bucket to the mounted storage."],[798,"Once you have granted access to the VS Code server, you will be able to see the link to the VS Code server in logs."],[756,"Once you have granted access to the VS Code server, you will be able to see the name of the Cursor dev tunnel in the logs."],[543,"Once your account is set up, go to https://huggingface.co/settings/tokens while logged in to create a personal access token. Create a new token with Read access and give it a memorable name. Save the generated token in a safe place, as it won't be viewable again for security reasons."],[940,"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"],[985,"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"],[1051,"Once your endpoint is ready, you can use the playground to send requests directly from the UI, or you can send requests to the endpoint URL by running a command like the following:"],[3086,"Once your GCS bucket is created, set up Bash environment variables for the specifics of the GCP-related variables\nrequired. Specify a different $BUCKET_NAME if you used a custom name in the console."],[1843,"Only models in *safetensors format are currently supported."],[223,"Only workspace admins can add machines to the node group."],[3210,"Only workspace admins can set up the export settings."],[971,"Open a terminal and install Python 3.10 or newer if not already available. Follow the official Python documentation for instructions on installing a compatible version for your device. Then, install the DGX Cloud Lepton Python package in your terminal with:"],[758,"Open Cursor locally and ensure the remote tunnels extension is installed. Run the Remote-Tunnels: Connect to Tunnel... command in the command pallete in Cursor and authenticate with the same account you granted access to the dev tunnel for. If you granted access to the tunnel using GitHub, use the same GitHub account to authenticate inside Cursor. Once authenticated, select the dev tunnel listed in the Dev Pod logs to connect to the remote resources."],[2088,"Open the Access keys tab under Settings and toggle the Admin user switch to on.\nCopy the password and save it securely."],[3044,"Open the DGX Cloud Lepton console."],[3047,"Open the IAM console at https://console.aws.amazon.com/iam/."],[799,"Open the link generated in the logs in your browser, you will be able to see the VS Code running in your browser."],[821,"Open the Web Terminal: Navigate to your Pod details page and switch to the \"Terminal\" tab."],[425,"Opens an interactive bash shell inside the container"],[268,"Operating System"],[946,"Option 1: Create Endpoint from NVIDIA NIM via UI"],[949,"Option 2: Create Endpoint from NVIDIA NIM via CLI"],[2230,"Options"],[2249,"Options"],[2253,"Options"],[2256,"Options"],[2259,"Options"],[2262,"Options"],[2265,"Options"],[2268,"Options"],[2271,"Options"],[2274,"Options"],[2277,"Options"],[2280,"Options"],[2409,"Options"],[2413,"Options"],[2416,"Options"],[2419,"Options"],[2422,"Options"],[2448,"Options"],[2452,"Options"],[2455,"Options"],[2458,"Options"],[2461,"Options"],[2464,"Options"],[2467,"Options"],[2470,"Options"],[2473,"Options"],[2476,"Options"],[2479,"Options"],[2482,"Options"],[2485,"Options"],[2488,"Options"],[2622,"Options"],[2626,"Options"],[2656,"Options"],[2666,"Options"],[2674,"Options"],[2678,"Options"],[2681,"Options"],[2701,"Options"],[2705,"Options"],[2708,"Options"],[2711,"Options"],[2714,"Options"],[2717,"Options"],[2720,"Options"],[2781,"Options"],[2785,"Options"],[2788,"Options"],[2791,"Options"],[2794,"Options"],[2797,"Options"],[2800,"Options"],[2879,"Options"],[2883,"Options"],[2886,"Options"],[2889,"Options"],[2912,"Options"],[2916,"Options"],[2927,"Options"],[2931,"Options"],[2934,"Options"],[2937,"Options"],[2940,"Options"],[2943,"Options"],[2946,"Options"],[2949,"Options"],[2952,"Options"],[2955,"Options"],[3270,"Or, select the Logs tab from the deployment, job, or pod detail page."],[307,"OS Disk + Data Volume Setup"],[310,"OS Disk + Multiple Data Disks Setup"],[308,"OS disk: Minimum 128GB"],[311,"OS disk: Minimum 128GB"],[1269,"Other Configurations: Use the default settings or customize as needed. Refer to Batch Job configurations for detailed options."],[1254,"Other Configurations: Use the default settings or customize as needed. See Batch Job configurations for detailed options."],[1630,"Others: Tailscale, kubelet, etc."],[1636,"Overview"],[289,"Package auto-updates must be disabled."],[1356,"Pass Through"],[2094,"Password: Your Registry password just copied."],[2129,"Password: Your Registry password."],[1924,"Path"],[1207,"Path to the Python virtual environment"],[190,"path: This is the directory from your shared storage to be mounted. Putting / will mount the root directory of the shared storage inside your container."],[1606,"Pause Schedule"],[1615,"Pause scheduling on a specific node.\nOnce paused, no new workloads are scheduled to the node."],[1987,"Pending: The request has been submitted and is awaiting admin approval"],[1936,"Performance is governed by network bandwidth, latency, and the capacity of the NFS server."],[283,"Performance Verification"],[1927,"Permission Example"],[1982,"Permission Levels:"],[1923,"Permission management"],[1943,"Permissions allow administrators to configure volume sub-path access for users. The permission system verifies that users requesting jobs with storage are authorized to mount the specified path before job execution. Once a job starts, the mount remains unaffected by permission changes."],[2009,"Permissions: Approved access confers either viewer or user permissions as requested."],[1958,"Permitting selected members to specific path -> Allow admin1 and admin2 to /"],[1954,"Permitting selected members to specific path -> Allow all-users to /Common"],[1956,"Permitting selected members to specific path -> Allow user1 and user2 to /Data/dataset2"],[1955,"Permitting selected members to specific path -> Allow user1 to /Data/dataset1"],[1949,"Permitting selected members to specific path: This will only grant access to the selected members for the path you specified in the Path field."],[1262,"PET_MASTER_ADDR: Hostname of the primary node (first node)"],[1265,"PET_NNODES: Total number of workers for the current job"],[1266,"PET_NODE_RANK: Rank of the current node among all workers"],[1267,"PET_NPROC_PER_NODE: Number of GPUs on the current node\nEnvironment variables prefixed with PET_ are automatically parsed as corresponding torchrun parameters. For example, PET_MASTER_ADDR becomes --master_addr. You can override these by explicitly setting parameters in your torchrun command."],[3011,"PIDs per replica"],[2233,"pip install -U leptonai"],[2192,"Placeholders"],[80,"Playground"],[2243,"pod : Manages pods on the DGX Cloud Lepton."],[1385,"Pod Name"],[1925,"Policy"],[1355,"Port"],[1218,"Port for master node communication"],[3136,"Port: 18889 (or 8888)"],[639,"Pre-train on Two Nodes"],[519,"Pre-Train the Model"],[637,"Pre-train Using a Single GPU"],[638,"Pre-train Using Eight GPUs"],[582,"Pre-training is the most compute-intensive phase of the LLM training process as the model is typically trained for hundreds of billions to several trillions of tokens while it learns the vocabulary and word pairings of the underlying dataset. Depending on the size of the dataset and model, as well as the amount of compute resources available to train the model, this process can take anywhere from several days to a few months to finish. Therefore, it is strongly recommended to leverage as much of your available compute power as possible for pre-training the model."],[53,"Predefined environment variables"],[920,"Predict a multiple sequence alignment (MSA) for a given sequence against a series of protein sequence databases. The NIM includes the GPU-accelerated MMSeqs2 package for Multiple Sequence Alignment."],[921,"Predict a protein structure given a pre-computed MSA of the sequence against protein sequence databases."],[919,"Predict protein structure given a protein sequence."],[1642,"Preemption Behavior"],[1672,"Preemption occurs only within the same reservation or for higher-priority workloads targeting overlapping resources."],[2338,"Preferred (p): Attempts to spread replicas across different nodes when possible.\nRequired (r): Enforces strict replica spreading where each replica must be scheduled on a different node. Replicas cannot start if there are not enough nodes."],[2386,"Preferred (p): Attempts to spread replicas across different nodes when possible.\nRequired (r): Enforces strict replica spreading where each replica must be scheduled on a different node. Replicas cannot start if there are not enough nodes."],[1201,"Prefix used for naming services"],[1198,"Prefix used for naming services related to the job"],[1174,"Prefix used for naming worker hostnames"],[734,"Prepare the Conda environment"],[516,"Prepare the Data"],[331,"Prepare the Python script for distributed training"],[351,"Prepare the Python script for distributed training"],[3182,"Prepare your SSH public key and fill it into the script below."],[213,"Prerequisites"],[455,"Prerequisites"],[847,"Prerequisites"],[913,"Prerequisites"],[945,"Prerequisites"],[991,"Prerequisites"],[1026,"Prerequisites"],[1273,"Prerequisites"],[1494,"Prerequisites"],[3142,"Prerequisites"],[1633,"Press the button to trigger the health check."],[3081,"principal://iam.googleapis.com/projects/1068792787346/locations/global/workloadIdentityPools/nvidia-dgx-cloud-env/subject/system:serviceaccount:ws-workspaceID:default"],[3078,"principal://iam.googleapis.com/projects/1091797687346/locations/global/workloadIdentityPools/nvidia-dgx-cloud-env/subject/SUBJECT_ATTRIBUTE_VALUE"],[2992,"Prints the authentication token of the current workspace. This is useful\nwhen you want to obtain the workspace token in the command line in e.g. a\nshell script, but do not want to hardcode it in the source file."],[2614,"Prints the events of a job by its ID."],[2989,"Prints the id of the current workspace. This is useful when you want to\nobtain the workspace id in the command line in e.g. a shell script, but do\nnot want to hardcode it in the source file."],[2600,"Prints the nodes id of a job."],[2596,"Prints the replicas id of a job."],[2998,"Prints the status of the current workspace."],[2995,"Prints the url of the current workspace. This is useful when you want to\nobtain the workspace url in the command line in e.g. a shell script, but do\nnot want to hardcode it in the source file."],[1587,"Priority Policy: Priority policy for the workspace."],[1140,"Priority: Set the priority of the job. Defaults to Medium (4). If the specified node group has limited resources, you can set the priority accordingly to get higher-priority resource allocation."],[1327,"Priority: Set the priority of the pod (defaults to Medium (4)). If the specified node group has limited resources, you can raise the priority to get higher priority allocation."],[1403,"Priority: Set the priority of the pod (defaults to Medium (4)). If the specified node group has limited resources, you can set the priority accordingly to get higher priority resource allocation."],[1335,"Private image registry auth (optional): If you are using a private image, specify image registry credentials."],[1147,"Private image registry auth (optional): If you are using a private image, specify the image registry authentication."],[1488,"Private image registry auth (optional): If you use a private registry, add registry credentials on the Registries page."],[1295,"Private Image Registry Authentication: Optionally add a private registry key for authenticating with a container registry."],[261,"Private Net Interface: A list of network interfaces that are used for host LAN network traffic."],[2156,"Private Registry Auth (Configure private container registry authentication)"],[2182,"Private: The secret is only visible to you, the user who created it."],[1464,"Private: Visible only to the creator and administrators."],[1000,"Privileged mode enabled in the DGX Cloud Lepton workspace to build custom containers."],[687,"Privileged mode enabled on the DGX Cloud Lepton workspace for container building."],[263,"Privileged Mode: Whether to enable privileged mode for the node group. Defaults to disabled. Privileged mode allows the node group to run privileged pods. This is useful for workloads that require access to the host's resources, such as the host's filesystem."],[1911,"Problem"],[3004,"Process ID (PID) Limits"],[3003,"Process Limits"],[1178,"prod01awsuswest"],[841,"Proper environment variable exposure for SSH sessions"],[1622,"Protect a node from being released. When enabled, the node cannot be released through the dashboard or API."],[1354,"Protocol"],[3135,"Protocol: TCP"],[1652,"Provide the reservation name, select users (either specific users or all users in the workspace), and enter the desired number of nodes."],[1374,"Proxy: A public URL is generated for the port, accessible on the public network. Only one port can be exposed through Proxy at a time."],[1427,"Public Access"],[1451,"Public access: Allow access to your endpoint from any IP address with an optional endpoint token authentication."],[2181,"Public: The secret is visible to all users in the workspace."],[1463,"Public: Visible to all team members in your workspace."],[1436,"Pulling Metrics from Replicas"],[853,"Push to Remote Repository"],[535,"Python 3.10 or newer with PIP installed on a local machine."],[645,"Python 3.10 or newer with PIP installed on a local machine."],[893,"Python Package Installation"],[134,"Python SDK Reference"],[1707,"Q: Can I create a reservation on partially used nodes?A: No. A node must be fully idle—no CPU or GPU workload—before it can be added to a reservation."],[1711,"Q: Can I include all users in the workspace in a reservation?A: Yes. You can select either specific users or all users in the workspace when creating a reservation."],[1710,"Q: Can I increase the size of an existing reservation?A: Not at this time. To change node count, delete and re-create the reservation."],[1706,"Q: Can reservations span different GPU types (e.g., H100 + B200)?A: No. Reservations inherit the homogeneity constraint of the node group they are created in."],[1705,"Q: Can users outside a reservation see or use it?A: No. Only admins and users within the reservation can view or launch workloads against it."],[1709,"Q: Do reservations expire?A: No. Reservations persist until explicitly deleted by an admin or a user who is part of the reservation."],[1712,"Q: How do I know if my workload is scheduled from a reservation?A: The workload detail view in the dashboard shows the reservation ID used for scheduling, if applicable."],[1713,"Q: What does preemption look like in practice?A: If a job enables \"Allow burst to other reservation nodes\", it can use the free resources of nodes reserved by another reservation. Once a reservation-bound job is created, it will likely evict the former job. The evicted job may restart if checkpointing is supported."],[1708,"Q: What happens if I request more nodes than are available?A: The system will reject the request with an error message. No partial reservation is created."],[3257,"Query at the deployment, job, pod, and workspace levels."],[3275,"Query logs by specific time, including the maximum (7 days) or a custom time range, default is most recent 24 hours."],[1857,"Question: This is your input field or prompt. Note that it doesn't have to be a question—it is simply the input which maps to the Answer field below."],[170,"Queue"],[1554,"Quota"],[1563,"Quota"],[1227,"Rank of the current worker node in the distributed setup"],[1293,"Ray Cluster Name: Specify a name for the RayCluster, such as demo-ray-cluster."],[2157,"Ray Clusters (Access for Ray clusters management)"],[679,"RayCluster"],[1271,"RayCluster"],[1282,"RayCluster supports heterogeneous compute types, allowing you to run a head node on a CPU‑only instance and workers on GPU nodes. Additionally, the head node can include GPUs and optionally run jobs as well."],[681,"RayJob"],[281,"RDMA Configuration"],[171,"Readiness"],[1467,"Readiness probe: Initial delay of 5 seconds; checks every 5 seconds. One success marks the container as ready; 10 consecutive failures mark it as not ready. Ensures the service is ready to accept traffic."],[3247,"Real-time Logs"],[3254,"Real-time logs display only the last 10,000 rows."],[2001,"Reason: The explanation provided by the support team member"],[1551,"Recovery Policy"],[1562,"Recovery Policy"],[1571,"Recovery policy controls the following options:"],[924,"Refer to NVIDIA NIM for AlphaFold2 documentation for more information."],[71,"Refer to the following guides to learn more about Dev Pods:"],[3029,"Refer to the following sections for details on federating DGX Cloud Lepton with your cloud provider."],[1567,"Refer to the Storage guide for details."],[1501,"Refer to this guide for details. Once the registry auth key is created, add a private registry via Settings > Registries > New Registry Auth."],[1165,"Reference for torch.distributed.launch"],[393,"References"],[1854,"Regardless of how the dataset is loaded, the columns in the dataset need to be mapped to the expected fields for the code to understand. This is referred to as Dataset Mapping. The fine-tuning code will adjust each prompt in the dataset to the following format with the {context} field being optional:"],[730,"Regardless of the method used for launching the job, you can view the logs from the training process in the Logs tab on the job page in the UI. Additionally, you can see resource utilization in the Metrics tab for the job."],[132,"Registry"],[2026,"Registry"],[962,"Registry Auth: Select one of the existing registry auth configurations you created in your workspace or create a new one with access to NGC."],[1042,"Registry Auth: Select one of the existing registry auth configurations you created in your workspace or create a new one with access to NGC."],[933,"Registry auth: Select one of the existing registry auth configurations you created in your workspace or create a new one."],[2092,"Registry: The login server URL of your Azure Container Registry, e.g., myregistry.azurecr.io."],[2083,"Registry: The URL of the GCP Artifact Registry, for example, https://us-west1-docker.pkg.dev."],[2127,"Registry: The URL of your registry."],[1991,"Rejected: The admin rejected the request without granting access"],[1611,"Release"],[294,"Release 550 family is recommended, especially version 550.144.03 or newer."],[217,"Release Machines"],[1610,"Release Protection"],[1625,"Release: Immediately evicts all pods, terminates any active workloads, and releases the node."],[2963,"remove : Remove a workspace from the record."],[2727,"remove : Removes a pod."],[2807,"remove : Removes a Ray cluster."],[2499,"remove : Removes a single job."],[2289,"remove : Removes an endpoint."],[2895,"remove : Removes the secret with the given name."],[2982,"Remove a workspace from the record. After removal, the locally stored url\nand auth token will be deleted. If the workspace is currently logged in, you\nwill be logged out."],[2986,"Remove all workspaces."],[2500,"remove-all : Removes all jobs matching the specified filters."],[2964,"removeall : Remove all workspaces."],[2766,"Removes a pod."],[2846,"Removes a Ray cluster."],[2577,"Removes a single job."],[2555,"Removes all jobs matching the specified filters. At least one filter must be\nprovided. For safety, name and user filters require exact matches. State\nfilter remains flexible. The --user option is required to prevent accidental\noperations on other users' jobs."],[2351,"Removes an endpoint."],[2905,"Removes the secret with the given name."],[472,"Repeat steps 2-4 for your AWS access key ID."],[3278,"Replica"],[3239,"Replica ID"],[172,"Replicas"],[2501,"replicas : Prints the replicas id of a job."],[1972,"Request for Access"],[1974,"Request States"],[1997,"Request Time: When the request was submitted"],[421,"Requests 1 node with 8 GPUs"],[1435,"Require Approval to Make Replicas Ready"],[509,"Requirements"],[632,"Requirements"],[676,"Requirements"],[1057,"Requirements"],[1634,"Reservations"],[1662,"Reservations do not expire automatically and must be deleted manually."],[1702,"Reserve only as many nodes as needed to avoid idle capacity."],[335,"Resource"],[355,"Resource"],[372,"Resource"],[1135,"Resource"],[1321,"Resource"],[1386,"Resource"],[1792,"Resource"],[3008,"Resource"],[404,"Resource Shape"],[1714,"Resource Shape"],[1091,"Resource Shape: At least eight GPUs with 80+ GB of GPU memory is suggested for running SFT due to the longer sequence lengths of the reasoning dataset. Adding additional workers for multi-node fine-tuning will increase training throughput but one worker is sufficient for this example."],[1141,"Resource shape: The instance type that the job will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details."],[1404,"Resource shape: The instance type that the pod will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details. The number of GPUs must satisfy the notebook's listed requirements as shown in the preview."],[1328,"Resource shape: The instance type the pod will run on. Select from a variety of CPU and GPU shapes. Refer to Node Group Shapes for more details."],[1077,"Resource Shape: This process is mainly network-bound and does not require much CPU resources and does not require a GPU. A cpu.medium resource should be sufficient, or a single-GPU resource if CPU-only resource shapes are not available in your node group."],[656,"resource_shape = f\"gpu.{devices}xa100-80gb\" and resource_shape = \"gpu.a100-80gb\": Replace the a100-80gb piece in both lines with the desired resource shape. This is the GPU type and configuration to use for the job. If your cluster has H100 GPUs, this would likely be h100-80gb. Be sure to update this in both sections as this will automatically handle the different formats for resource shapes."],[584,"resource_shape=\"gpu.8xh100-80gb\": Replace gpu.8xh100-80gb with the desired resource shape. This is the GPU type and configuration to use for the job, such as gpu.8xh100-80gb might refer to a pod with 8x H100 GPUs available in it."],[569,"resource_shape=\"gpu.h100-80gb\": Replace gpu.h100-80gb with the desired resource shape. This is the GPU type and configuration to use for the job, such as gpu.8xh100-80gb might refer to a pod with 8x H100 GPUs available in it."],[2686,"resource-shape : List resource shapes per node group."],[429,"Resource:"],[500,"Resource: For the performance benchmark, choose 8xH100 to use the entire node for the NCCL performance benchmark, and set the number of workers to 2 to use both nodes. You will need to select a node group matching the resource shape requirements. It's recommended to use your own dedicated node group for this job."],[486,"Resource: Select H100 x8 GPUs and set the worker count to 1. You can use multiple workers to speed up the training process."],[2290,"restart : Restarts an endpoint."],[2347,"Restarts an endpoint."],[1959,"Results in the following permissions:"],[1607,"Resume Schedule"],[1616,"Resume scheduling on a specific node.\nOnce resumed, the node is available for new workloads."],[2631,"Retrieve and display logs from deployments, jobs, or replicas"],[31,"Review BYOC Requirements"],[27,"Review Requirements: Ensure your machines meet the BYOC Requirements for hardware, software, network, and storage configurations."],[1977,"Revoke Access"],[2000,"Revoke Time: When the access was revoked"],[1990,"Revoked: The admin manually revoked the access before expiration"],[1677,"Role"],[1995,"Role: The access level requested (Viewer or User)"],[2130,"Roles and Permissions"],[302,"Root: NVMe SSD in RAID-1 configuration (post-RAID capacity of at least 1 TB)."],[1774,"Route traffic to endpoints by weights"],[1953,"Rules:"],[1754,"Run a Validation"],[782,"Run apt install tmux to install tmux."],[1340,"Run as: When using a custom image, choose to run the container as root or the image's default user (defaults to image default user)."],[410,"Run Command (DGX Cloud Lepton will auto wrap with /bin/bash -c)"],[1487,"Run command: Choose the command to run. For example: python -m http.server 8080."],[488,"Run command: Copy the following code to the run command field."],[1080,"Run Command: Enter the following for the command. Note that your STORAGE_DIR might change depending on where you mount storage (see Storage item below):"],[1148,"Run Command: The command to run when the container starts."],[502,"Run command: We will load code from a remote GitHub repository and run the NCCL performance benchmark. Enter the command as follows:"],[75,"Run NCCL test"],[866,"Run NCCL Test on Dev Pods"],[784,"Run ngrok http 18889 in the tmux session."],[993,"Run the Gradio Application"],[783,"Run tmux to attach to the tmux session."],[1384,"Running a Notebook"],[143,"Running in a node reservation"],[680,"Running jobs in the RayCluster"],[732,"Running Jobs with Conda Environment"],[423,"Runs a container from a Singularity image file"],[1169,"Sample Value"],[947,"Save Model in Cache"],[665,"Save the file and run the script again with:"],[669,"Save the file and run the script again with:"],[204,"Save the script above to a file such as run.py and launch it with:"],[915,"Saving AlphaFold2 Model in Cache"],[1028,"Saving Model in Cache"],[1446,"Scale replicas to zero based on no‑traffic timeout: Specify the initial number of replicas and the no‑traffic timeout (seconds)."],[1278,"Scaling the RayCluster"],[1550,"Scheduling Policy"],[1561,"Scheduling Policy"],[1641,"Scheduling Workloads with Reservations"],[3276,"Scope"],[2101,"Scopes: In this case, we only need the read:packages scope."],[2244,"secret : Manage secrets on the DGX Cloud Lepton."],[3202,"Secret Key"],[3206,"Secret Key"],[131,"Secrets"],[173,"Secrets"],[2171,"Secrets"],[2158,"Secrets (Store and manage sensitive configuration)"],[2176,"Secrets are a secure and reusable way to add credentials and other sensitive information to your workspace.\nYou can use secrets to store sensitive data, such as passwords, API keys, and tokens, and then reference them in your deployments, jobs, and pods."],[2177,"Secrets are similar to environment variables, but the actual value is no longer editable or revealable once created."],[1459,"Secrets are similar to environment variables, but their values are pre-stored in the platform and not exposed in the development environment.\nLearn more about secrets here."],[2172,"Securely store and manage sensitive information like API keys and credentials in DGX Cloud Lepton, and use them in your deployments and jobs."],[1978,"Security and Auditing"],[843,"Security configurations optimized for container environments"],[3064,"See also https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts-minimum-sdk.html for\nfull details on various AWS SDKs."],[1873,"Seed: This is the random seed used during fine-tuning. All other settings equal, fine-tuning will be deterministic when the same seed is used between runs. This must be a non-negative integer. Minimum: 0. Maximum: 65535. Default: 42."],[1599,"Select \"Enabled\" to turn on image acceleration for the node group. Select \"Disabled\" to turn it off."],[695,"Select a Custom image and specify nvcr.io/nvidia/nemo:25.07 for the image."],[1006,"Select a Custom image and specify nvcr.io/nvidia/pytorch:25.06-py3 for the image."],[1510,"Select a model image from the list of built‑in models, or enter a custom model image."],[430,"Select a node group with available GPUs. If you don't have one, refer to this guide to request a node group. For this example, we request 1 node with 8 GPUs, so we can select a GPU type like H100-80GB-HBM3 and specify x8 for the GPU count."],[693,"Select a single GPU in your node group for the resource shape."],[1004,"Select a single GPU in your node group for the resource shape."],[475,"Select Advanced Configuration, go to Environment Variables, and click Add Secret. Add your AWS Access Key ID and secret access key as secrets. This should look like the image below."],[1481,"Select Create Endpoint."],[1482,"Select Create from Container Image."],[1523,"Select Create LLM Endpoint."],[1536,"Select Create LLM Endpoint."],[696,"Select Custom entrypoint for the container Entrypoint."],[1007,"Select Custom entrypoint for the container Entrypoint."],[819,"Select Custom Image: In the container configuration section, select custom image and input the image name nvcr.io/nvidia/pytorch:25.04-py3."],[3165,"Select Enable SSH Host Network to pre-populate the container port configuration with the default SSH port 2222 and the Host Network option selected."],[242,"Select Graceful Release or Release, enter the reason, and submit the request."],[1456,"Select IP allowlist to allow access to your endpoint only from specified IP addresses or CIDR ranges."],[334,"Select Job Type"],[354,"Select Job Type"],[608,"Select Load from storage for the model and select the Volume where the checkpoint was saved and enter /nemo-workspace/nemotronh_8b/huggingface as the Model file path. This will load the Hugging Face model directly from the shared storage."],[1240,"Select MPI at the top of the create job page."],[343,"Select MPI on the top of the create job page."],[3277,"Select or enter a deployment, job, or pod name to query the logs of all replicas within that scope."],[3279,"Select or enter a specific replica name to query the logs of that replica."],[2142,"Select Read Only or Read/Write."],[1537,"Select SGLang as the inference engine."],[610,"Select the GPU resource shape to deploy the model in. The 8B model can fit in a single GPU with at least 24 GB of GPU memory."],[3179,"Select the Host Network checkbox."],[1038,"Select the model to deploy by either loading the model from Hugging Face or from storage attached to your node group. To load a model hosted on Hugging Face, click the Load from Hugging Face button and enter or select the model repo, such as meta-llama/Llama-3.1-8B-Instruct. If deploying a gated model, select your Hugging Face token from the UI or add one using the + Add token button."],[1512,"Select the NGC API key you saved as a secret in your workspace."],[1013,"Select the node group and resource shape to use for the deployment in the Resource section. Given the model is small, a single GPU is sufficient."],[960,"Select the node group and resource shape to use for the deployment in the Resource section. GPT-OSS-120B requires at least one GPU with a minimum of 80 GB of GPU memory. Deploying with multiple GPUs can yield higher throughput for responses."],[1040,"Select the node group and resource shape to use for the deployment in the Resource section. The number of GPUs will vary depending on the model, but a general rule of thumb is to have at least 2 GB of GPU memory for every billion parameters for a model in BF16 precision. For example, an 8 billion parameter model will need approximately 16 GB of GPU memory to deploy."],[704,"Select the node group for the job to run on."],[720,"Select the node group for the job to run on."],[609,"Select the node group to run the deployment in."],[1831,"Select the number and type of GPUs to run on. While there is no standard rule for determining the required number of GPUs for any arbitrary model or dataset, the general guidelines are:"],[1511,"Select the NVIDIA registry auth you created (see registry auth)."],[3261,"Select the replicas tab."],[365,"Select the resource type you want to use, for example, gpu.8xh100-sxm, and set the number of workers to the desired number.\nIn this guide, we want to use 2 replicas, so we set the number of workers to 2."],[345,"Select the resource type you want to use, for example, gpu.8xh100-sxm, and set the number of workers to the desired number.\nIn this guide, we will use two replicas, so we set the number of workers to 2."],[3218,"Select the secret key for your Datadog instance. You can add the secret key directly here or under Settings -> Secrets in the DGX Cloud Lepton dashboard. Refer to this guide for more details."],[3222,"Select the secret key of your Datadog instance. You can add the secret key directly here or under Settings -> Secrets in the DGX Cloud Lepton dashboard. Refer to this guide for more details."],[1258,"Select Torchrun at the top of the create job page."],[363,"Select Torchrun on the top of the create job page."],[1524,"Select vLLM as the inference engine."],[86,"Select vLLM as the LLM engine, and load a model from Hugging Face in the Model section. In this case, we will use the nvidia/Nemotron-Research-Reasoning-Qwen-1.5B model."],[525,"Send Requests to the Deployed Model using the API"],[524,"Send Requests to the Deployed Model using the Playground"],[1486,"Server Port: Choose the port your process listens on. For example: 8080."],[2159,"Service Requests (Request services from DGX Cloud Lepton team)"],[2116,"Services Included: The services included in the new API key, select Private Registry here."],[469,"Set the value to your AWS secret access key."],[470,"Set the visibility to Private if you do not want the secret to be used by other users in the workspace."],[825,"Set up SSH keys and authentication"],[520,"Set Up the Environment"],[333,"Set up the job"],[353,"Set up the job"],[517,"Set Up the Scripts"],[514,"Set Up VS Code Locally"],[432,"Set workers to 1. You can also specify a higher number of workers to accelerate the training process."],[892,"Setup"],[568,"Several lines in the script below will need to be modified to reflect your cluster. The lines are as follows:"],[1818,"SFT updates all or nearly all of the base model's weights using a supplied dataset containing many labeled examples. This approach builds on the base model's existing knowledge while allowing the fine-tuned model to specialize in specific domains or refine the output based on human preferences. Since it updates the full base model, SFT requires significant compute resources, particularly for very large models. The output is a full model checkpoint that can be deployed directly for inference."],[1834,"SFT: Eight (8) 80+ GB GPUs"],[1534,"SGLang is a fast serving framework for large language models and vision language models. It enables faster, more controllable interactions by co-designing the backend runtime and frontend language. To create a dedicated endpoint with SGLang, follow these steps:"],[1775,"Shadow traffic to an endpoint"],[1784,"Shadowing endpoints redirect a percentage of traffic to a different deployment. This feature is useful for testing new deployments in production. Currently, only one shadowing endpoint is allowed per domain."],[1557,"Shapes"],[1564,"Shapes"],[1596,"Shapes manage the resource configurations available in the node group. Refer to this documentation for details."],[1433,"Shared Memory"],[1346,"Shared Memory: The size of shared memory allocated to the container."],[1414,"Shared Memory: The size of the shared memory allocated to the container. Some Starter Kits require more than the default. Check the notebook's Requirements section and specify it here if needed."],[1156,"Shared Memory: The size of the shared memory that will be allocated to the container."],[1837,"Shared storage is required for checkpoints to be available after fine-tuning. The selected node group needs a shared storage volume connected to it which can be mounted in jobs. Select the desired volume and where it should be mounted inside the running job."],[2756,"Show pod detail and optionally save its spec JSON."],[2397,"Shows Endpoint detail and optionally saves its spec JSON."],[2850,"Shows Ray cluster detail and optionally saves its spec JSON."],[1279,"Shutting down the RayCluster"],[285,"Since BYOC in DGX Cloud Lepton follows the standards of NVIDIA DGX OS 6 and NVIDIA DGX OS 7, your machines must meet the following requirements."],[965,"Since models can take hours to download depending on their size, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."],[1045,"Since models can take hours to download depending on their size, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."],[936,"Since the AlphaFold2 model can take hours to download, you may want to store the model in a persistent cache. To do this, set an environment variable with a new cache path in persistent storage and mount that storage path."],[545,"Since the Nemotron-H model isn't gated, this step is not necessary for following this guide."],[3080,"Since the service account is always default in DGX Cloud Lepton and the namespace is\nws-workspaceID, substitute\nsystem:serviceaccount:ws-workspaceID:default as the\nSUBJECT_ATTRIBUTE_VALUE. The overall IAM Principal will therefore be:"],[1019,"Since this example is hosting a web server, it is recommended to enable public access and disable authentication."],[305,"Single OS Disk Setup"],[413,"Singularity  / Docker Image"],[395,"Slurm"],[1315,"Slurm Clusters"],[2160,"Slurm Clusters (Access for Slurm clusters management)"],[451,"Slurm Documentation"],[386,"Slurm to DGX Cloud Lepton Terminology"],[1832,"Small models (that is, less than 8B parameters for dense models):"],[274,"Software Packages"],[267,"Software Requirements"],[1912,"Solution"],[1469,"Some endpoints may need more time to start the container and initialize the model. Specify a custom delay by selecting Custom and entering the delay in seconds."],[978,"Some of these values may change depending on your workspace, such as:"],[1412,"Some Starter Kits require shared storage to be mounted in order to persist data after the notebook finishes. Check the Requirements section of the notebook to see if shared storage is required. If so, mount storage for the pod container. See this guide for more details."],[1399,"Specify a name for the Dev Pod to launch, such as my-starter-kit."],[1012,"Specify a name for the endpoint in the Endpoint Name field such as dia-app."],[959,"Specify a name for the endpoint in the Endpoint Name field such as gpt-oss-120b."],[1037,"Specify a name for the endpoint in the Endpoint Name field."],[1839,"Specify the base model to fine-tune with your dataset. There are two main options: loading from Hugging Face Hub or loading from shared storage."],[183,"Specify the job spec - this includes defining the resource shape, container, command, and number of workers"],[1863,"Specify the path to save all training logs, data, and the final checkpoints. This must be an absolute path and should be on mounted shared storage for the checkpoints to persist after the job is completed."],[1400,"Specify the resource for the Dev Pod to run on."],[1470,"Specify whether approval is required before replicas become ready.\nBy default, replicas are ready immediately after deployment."],[1471,"Specify whether to pull metrics from replicas.\nBy default, metrics are pulled from all replicas."],[415,"squeue, scancel"],[409,"srun, mpirun command"],[2728,"ssh : SSH into a running pod."],[3140,"SSH Access"],[2770,"SSH into a running pod."],[1407,"SSH Public Key: The SSH public key that will be used to access the pod. This is only available for default images with Dev Pod entrypoint. Refer to this guide for more details."],[1333,"SSH Public Key: The SSH public key used to access the pod. Available for default images with Dev Pod entrypoint. See this guide for details."],[3147,"SSH with Custom Images"],[3143,"SSH with Default Images"],[816,"SSHPUB: Your SSH public key (e.g., ssh-rsa AAAxxxxx lepton@sampleDomain.com)"],[271,"Standard Boot Option"],[2502,"start : Starts a job by its ID."],[827,"Start the services"],[1393,"Starter Kits are a curated set of Jupyter notebooks with self-contained AI/ML examples that run directly on DGX Cloud Lepton Dev Pods."],[1381,"Starter Kits on Dev Pods"],[2609,"Starts a job by its ID."],[1994,"State: Current status of the request"],[1934,"Static NFS Volume: Static NFS Volume enables workloads on multiple nodes to share a common filesystem. The data resides on an NFS server that any workload can mount and use."],[2291,"status : Gets the status of an endpoint."],[2965,"status : Prints the status of the current workspace."],[493,"Step 1: Create a New Job"],[868,"Step 1: Create Dev Pods"],[3096,"Step 1: Enable JupyterLab"],[3103,"Step 1: Enable JupyterLab"],[761,"Step 1: Enable JupyterLab When Creating"],[803,"Step 1: Prepare the Setup Script"],[3144,"Step 1: Start the Dev Pod with Built-in Image"],[3148,"Step 1: Start the Dev Pod with Custom Image"],[3145,"Step 2: Choose or Create an SSH Public Key"],[805,"Step 2: Create Dev Pod with PyTorch Image"],[3097,"Step 2: Expose JupyterLab Port"],[3105,"Step 2: Expose JupyterLab Port"],[3149,"Step 2: Prepare the Setup Script"],[494,"Step 2: Run the Job"],[762,"Step 2: Set JupyterLab Token"],[869,"Step 2: Setup SSH Keys for Pods Communication"],[3098,"Step 3: Configure JupyterLab Workspace Root Directory (Optional)"],[3146,"Step 3: Configure SSH Ports"],[763,"Step 3: Create ngrok Account"],[3106,"Step 3: Create the Dev Pod"],[3150,"Step 3: Execute the Script via Web Terminal"],[872,"Step 3: Get the local IP addresses for Pod 1 to Pod N"],[806,"Step 3: Run the Setup Script"],[3107,"Step 4: Access JupyterLab"],[3151,"Step 4: Connect to the Dev Pod via SSH"],[873,"Step 4: Create hostfile on Pod 1"],[3099,"Step 4: Create the Dev Pod"],[764,"Step 4: Get ngrok Authtoken"],[807,"Step 4: Verify the Configuration"],[874,"Step 5: Download the nccl-tests script on every Pod"],[765,"Step 5: Install and Set Up ngrok"],[3100,"Step 5: Set New Token for JupyterLab"],[3101,"Step 6: Access JupyterLab"],[875,"Step 6: Run the nccl-tests script on Pod 2 to Pod N"],[766,"Step 6: Start ngrok"],[767,"Step 7: Access JupyterLab"],[876,"Step 7: Run the nccl-tests script on Pod 1"],[848,"Steps"],[1058,"Steps"],[1274,"Steps"],[2292,"stop : Stops a deployment by its name."],[2503,"stop : Stops a job by its ID."],[2729,"stop : Stops a pod by its name."],[391,"Stop a Job"],[2562,"Stop all jobs matching the specified filters. At least one filter must be\nprovided. For safety, name and user filters require exact matches. State\nfilter remains flexible. The --user option is required to prevent accidental\noperations on other users' jobs."],[2504,"stop-all : Stop all jobs matching the specified filters."],[2402,"Stops a deployment by its name."],[2604,"Stops a job by its ID."],[2774,"Stops a pod by its name."],[1323,"Storage"],[1389,"Storage"],[1430,"Storage"],[1549,"Storage"],[1560,"Storage"],[1794,"Storage"],[1919,"Storage"],[461,"Storage associated with your node group"],[276,"Storage Configuration"],[2161,"Storage Permission (Manage storage mounts and permissions)"],[1921,"Storage Type"],[1081,"Storage: Mount shared storage in the jobs to save the checkpoint after the job finishes. Select the shared storage in your node group and set your mount path to something like /storage. Your STORAGE_DIR in the Run Command above must start with the mount path listed here."],[1155,"Storage: Mount storage for the job container. Refer to this guide for more details."],[1092,"Storage: Select the same storage volume that was used for the model preparation in the previous section. While you can specify a different path to mount the storage under in the container, it is easiest to use the same mount path as earlier, such as /storage."],[1735,"Storage: The amount of storage reserved for the shape, measured in GB."],[1505,"Store the NGC API key on DGX Cloud Lepton as a secret."],[1589,"Strict: Allocate resources strictly according to workload priority. If you select \"Strict\", configure the \"Strict Queueing Scope\" as either \"within the workspace\" or \"across workspaces\"."],[1195,"Subdomain name assigned to the job service"],[2808,"submit : Submits a job to a Ray cluster."],[1286,"Submit tasks to the RayCluster"],[1653,"Submit the request for admin approval."],[2861,"Submits a job to a Ray cluster."],[206,"Support"],[3255,"Support search and filtering."],[288,"Swap memory must be disabled."],[69,"Switch to the Terminal tab. DGX Cloud Lepton automatically establishes a connection to the Dev Pod, allowing you to control it through a full web terminal."],[273,"System Libraries"],[1629,"System: FUSE, OS, library status, and more."],[2208,"Take Dev Pod template as an example, you can use the placeholder {{user.email_username}} in the Job Name or Pod Name field to make the workload name unique for each creator.\nSet Pod Name to {{user.email_username}}-dev-pod, and the workload name will be hey-dev-pod for user with hey@nvidia.com email address."],[438,"Taking the example srun command above, this can be invoked with the CLI as follows:"],[1790,"Technique"],[905,"Tell W&B which values to send with wandb.log()"],[133,"Templates"],[174,"Templates"],[1230,"Templates"],[2186,"Templates"],[2162,"Templates (Create and manage deployment templates)"],[2195,"Templates are used to create workloads in your workspace. You can create templates for dev pods and batch jobs to improve productivity by reusing the same configuration."],[1879,"Tensor Parallelism multiplied by Context Parallelism must be less than or equal to the total number of GPUs in your job."],[1872,"Tensor Parallelism Size: This distributes your model across GPUs to lower GPU memory usage. This is useful with larger models as it lowers the amount of GPU memory required to run the model. This must be a power-of-two (1, 2, 4, 8, ...). This multiplied by the Context Parallelism Size must be less than or equal to the total number of GPUs for your job. If your model is larger than 10B parameters, it is recommended to use tensor parallelism. Minimum: 1. Default: 1."],[831,"Test Connection: Use the SSH connection command from the Pod details page under the \"Connect\" tab"],[953,"Test Endpoint"],[1532,"Test the endpoint in the playground by clicking the endpoint you created."],[1545,"Test the endpoint in the playground by clicking the endpoint you created."],[916,"Testing AlphaFold2 Endpoint"],[995,"Testing Endpoint"],[1030,"Testing Endpoint"],[1960,"The 2 Admin users (admin1 and admin2) have access to everything within the volume."],[381,"The above script will create a memory leak on a GPU by continuously allocating tensors using Pytorch and preventing them from being garbage collected, until it reaches a target memory usage (defaulting to 10GB but configurable via command line)."],[1992,"The access request list displays the following information for each request:"],[1883,"The Advanced Configuration section contains a few additional settings, mainly the Weights and Biases (W&B) integration. The fine-tuning feature has optional support for logging training metrics to W&B servers for an easy way to monitor training progress and compare different runs."],[935,"The AlphaFold2 NIM can take multiple hours to download the dataset initially. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 7200 seconds."],[1125,"The amount of GPU memory used per GPU during the indicated step."],[1893,"The base model used for fine-tuning."],[159,"The client variable can be reused for multiple tasks."],[2586,"The cloned job will: - Have the same configuration as the original job -\nHave a new name with '-clone' suffix"],[1111,"The completed form should resemble the following image:"],[2203,"The configuration for batch jobs is the same as creating a batch job. You can check the guide for more details."],[2202,"The configuration for dev pods is the same as creating a dev pod. You can check the guide for more details."],[899,"The container used to run your job on DGX Cloud Lepton needs the W&B Python package installed. Some NGC images like the NeMo Framework container (nvcr.io/nvidia/nemo) already have the package installed, while others like the PyTorch image (nvcr.io/nvidia/pytorch) do not. If your container does not have W&B installed already, run this command as part of an entrypoint on container start or in a running container."],[1896,"The container version the job is running in."],[1121,"The current epoch in the dataset (zero-based)."],[1914,"The dataset processing code was unable to read the specified keys from the column mapping while defining the dataset. Check to make sure that both the values next to Answer and Question in the dataset mapping are in the training dataset. For example, if you put response next to Answer in the column mapping, there must be a response column in the dataset."],[858,"The default images allow the root access by default.\nIf you want to use a custom image, you should make sure the image allows sudo access."],[1814,"The DGX Cloud Lepton Fine-Tuning feature can be accessed by clicking the Fine-Tuning button in the Explore page, or navigating directly to the Fine-Tuning page under Utilities."],[3226,"The DGX Cloud Lepton platform expects your workload application provide an HTTP endpoint that serves metrics in Prometheus format on the specified port and path. Most applications use the official Prometheus library."],[1646,"The DGX Cloud Lepton Reservation feature enables you to carve out a fixed number of nodes within a node group for exclusive use by selected users in a workspace. Reservations guarantee capacity, ensuring critical workloads have access to dedicated compute resources when needed."],[490,"The duration of the training job depends on the number of parameters you set and the number of GPUs you use. When the job is running, you can check the real-time logs and metrics."],[617,"The easiest way to send a request to the deployed model is via the Playground in the UI. Open the endpoint that was created for the model and select the Playground tab. This opens a chat interface for the model using the exposed API. Note that this is an easy way to test the model, but shouldn't be considered a full chat interface and can't handle very large input and output token sizes."],[620,"The endpoint also exposes a REST API that follows the OpenAI API standard for interacting with requests. This can be used to integrate with downstream applications like chat UIs, AI agents, or other backend services. The general format for sending a request from the terminal is as follows:"],[621,"The endpoint URL can be found in the UI by selecting the nemotronh-8b-base deployment in the Endpoints menu and copying the Endpoint URL at the top of the page under the model name. This URL typically has the format https://<workspace ID>-<endpoint name>.xenon.lepton.run. For example, if a workspace has an ID of abcdefg, the URL would be https://abcdefg-nemotronh-8b-base.xenon.lepton.run."],[3217,"The endpoint URL is the URL of your Datadog instance. Learn more about obtaining the endpoint URL from the Datadog documentation."],[3221,"The endpoint URL is the URL of your Datadog instance. Learn more about obtaining the endpoint URL from the Datadog documentation."],[179,"The example above does the following:"],[341,"The file has been saved in the GitHub repository here."],[361,"The file has been saved in the GitHub repository here."],[1813,"The Fine-Tuning feature currently supports both Supervised Fine-Tuning (SFT) and Low-Rank Adaptation (LoRA), giving users flexibility depending on their use-case. These techniques are described in more detail below."],[1815,"The Fine-Tuning tab displays a list of jobs that have been run previously, if any."],[958,"The first path to deploying the NIM is via a web browser. To get started, navigate to the create endpoint page on the dashboard."],[559,"The first script downloads the medium, medium-high, and high quality subsets of the Nemotron-CC training dataset from Common Crawl to the remote filesystem. By default, Nemotron-CC includes data subsets ranging from low quality to high quality, but you only want to filter out the lower quality data as studies have shown this degrades model performance (Blakeman et al., 2025)."],[3055,"The first step is to create a new S3 bucket if necessary in the AWS console (or CLI).\nNote that the region depends on where you provision the S3 bucket. Be sure to select the correct region in the console\nafter logging in. In the following instructions, we will use a $BUCKET_NAME of my-s3-bucket-$ACCOUNT_ID."],[225,"The first way is to add your machines via SSH access. Follow the steps below:"],[460,"The following are required before running the data download workload:"],[556,"The following example walks through downloading, extracting, concatenating, and preprocessing the Nemotron-CC dataset, which includes a large corpus of curated text from several domains and has been deduplicated and cleaned to make it an excellent candidate for pre-training LLMs. While the remainder of the document will be based on the Nemotron-CC dataset, this general process can be followed for most custom datasets and will provide guidance on how to adapt as needed."],[532,"The following is a list of requirements to follow this complete workflow:"],[685,"The following is a list of requirements to follow this guide:"],[203,"The following is a self-contained example of launching a batch job using the Python SDK following the flow outlined earlier."],[902,"The following is a trivial example of a job that sends metrics to W&B using the API. The key points are:"],[2145,"The following is the list of resources that you can assign permissions for:"],[563,"The following script takes 50 individual shards at a time and combines them into one large file, repeating for the entire dataset. Each rank concatenates a unique subsection of the dataset and deletes the individual shards in the end. Using the local VS Code session, save the script in the local directory as data_prep/concat.sh."],[157,"The following sections break down these steps and provide a complete example."],[420,"The following Slurm script launches an interactive job:"],[394,"The following table outlines the key terminology mapping between Slurm and DGX Cloud Lepton for job migration."],[739,"The foo.tar.gz file contains the Conda environment."],[2212,"The form will be filled with the template configuration automatically, and you can modify fields as needed."],[558,"The four scripts that need to be created are as follows:"],[1123,"The gradient normalization value."],[1015,"The Gradio application runs on port 7860 by default. Enter 7860 in the Server Port field as this will expose the application on the URL when the endpoint is deployed. If the server port in the container is changed for any reason, the Server Port field needs to match the port that the application runs on inside the container."],[1284,"The high‑level steps for running jobs with Ray on DGX Cloud Lepton are:"],[561,"The individual dataset shards are compressed in the Zstandard or .zstd format and must be decompressed. The following script distributes the downloaded files across all ranks, decompresses the shards, and then removes the compressed downloads to keep the filesystem clean. Using the local VS Code session, save the script in the local directory as data_prep/extract.py."],[1675,"The job is scheduled on permissible capacity and a reservation owner submits a workload."],[1306,"The job requires PyTorch, which is not installed in the base Ray images. Install it on the head node and configure Ray to use the package on all workers. In the head node terminal, run:"],[1309,"The job will also be viewable in the Recent Jobs list in the dashboard to show status, nodes, usage, and more."],[596,"The job will be scheduled with DGX Cloud Lepton and will launch once resources become available. After submission, the job will appear in the DGX Cloud Lepton Batch Jobs page."],[383,"The job will fail after reaching the target memory usage. You will then see a failure message on the job details page.\nFor this example, you will see an error tag showing the job failed due to ERR_GPU_OUT_OF_MEMORY.\nHover over the error tag to see the detailed error message."],[600,"The job will take around three weeks to complete on eight nodes. Since NeMo Framework pre-training scales linearly, doubling the number of nodes should halve the amount of time required to pre-train the model."],[3021,"The key piece of information needed for any federation is the location of the public keys used by DGX Cloud Lepton to sign the\ntokens projected into workloads. This location is known as the OIDC Provider URL. The OIDC Provider URL can be found in\nthe DGX Cloud Lepton dashboard on the Nodes -> <NodeGroup> page."],[3026,"The Kubernetes namespace is always ws- followed by the DGX Cloud Lepton workspaceID."],[3027,"The Kubernetes service account (SA) is always default."],[1122,"The latest training loss."],[1124,"The learning rate used during the indicated step."],[649,"The Lepton Python SDK needs to authenticate with the DGX Cloud Lepton workspace to schedule the jobs. To authenticate with the CLI, run the following in your virtual environment:"],[1396,"The list shows the title and a brief description of each notebook so you can preview what it covers. Select a notebook from the list or search for a specific topic in the search field at the top of the form. The raw list of notebooks is mirrored from GitHub."],[1769,"The Logs tab shows validation logs categorized by node. Select a node from the dropdown in the top-right corner to change the view."],[3169,"The machine accessing the Dev Pod must be able to reach and communicate with either the public or private IP of the node(s) on which the Dev Pods are running."],[3186,"The machine accessing the Dev Pod must be able to reach and communicate with either the public or private IP of the node(s) on which the Dev Pods are running."],[224,"The machines should meet the requirements of BYOC."],[243,"The machines will be released from the node group, which will remove the dependencies installed during the BYOC process."],[625,"The model's response will be in the choices list, specifically in the assistant role shown. For example, the exact response begins \"Sure, here's a short story: once upon a time...\""],[690,"The NeMo-RL container must be built before launching jobs on DGX Cloud Lepton. Containers can be built directly on the platform using privileged dev pods. This process is documented here. Before building, ensure privileged mode is enabled for your node group as detailed in the linked guide."],[1830,"The Node Group is the specific compute resources the job will run on. If desired, select a subset of nodes to run the job on and/or a job priority to manage how the jobs get scheduled."],[1618,"The node may remain unhealthy temporarily during the reboot process."],[1120,"The number of forward passes or steps that have occurred."],[1126,"The number of tokens processed per second."],[918,"The NVIDIA AlphaFold2 NIM can:"],[1312,"The output indicates there is now an additional node in the cluster and two extra GPUs available for jobs."],[888,"The output will be similar to the following:"],[1577,"The page has two sections:"],[1072,"The paper follows the advice from this thread which found that increasing the output token count and the RoPE scaling did not have a significant impact on the model's performance in tasks. To replicate this, we must download the base model from Hugging Face and modify the config so it can handle longer sequences. We will use a Batch Job for this process."],[1944,"The permission system allows access to all users when no rules are configured. However, once any rule is configured, all access must be explicitly allowed. Only allow rules exist, and permission on a parent directory grants access to all sub-paths."],[3014,"The PID limit helps prevent fork bombs and resource exhaustion. If your application requires more processes, consider splitting it into multiple replicas or optimizing your process usage."],[3020,"The procedures for federating identity with each cloud vendor vary slightly and are described below. Most cloud vendors\nprovide Terraform or other automation methods for setting up the federation. Consult your cloud provider documentation\nfor detailed information."],[2196,"The process of creating a template is very similar to creating workloads as described in dev pods and batch jobs.\nYou can specify the configuration of the template, and use placeholders to make the template more flexible."],[3166,"The Proxy option for exposing container ports does not currently support SSH. For more information, refer to this guide."],[3180,"The Proxy option for exposing container ports does not currently support SSH. For more information, refer to this guide."],[1311,"The RayCluster supports manually scaling workers up and down to meet demand. To add resources to an existing RayCluster, click Edit next to the cluster and set the desired number of replicas. This updates the number of workers to match the replica levels specified in the form. For example, increasing from one to two replicas adds an additional worker. This may take some time as the image is pulled and the replica starts. After the worker is added, verify it by running ray status again:"],[1889,"The requested GPU resources."],[624,"The response should look similar to the following (response truncated - actual responses will vary):"],[1768,"The Result tab shows the final validation result."],[564,"The script combines 50 individual shards by default into a single file. For the complete dataset, this will yield 465 larger combined files representing the data, each being approximately 42 GB in size. To change how many shards are used in each file, increase or decrease the shards_per_file variable below. A larger number will result in fewer files that are larger in size. A smaller number will result in more files that are smaller in size."],[560,"The script retrieves the list of pages to download from Common Crawl, then removes all of the low and low-medium quality pages before downloading the remaining links using the cc-downloader tool. This can run on a single node for more efficient resource utilization. Using the local VS Code session created previously, save the following file in the local directory at data_prep/download.sh."],[655,"The script template will need to be modified based on your cluster. These settings are as follows:"],[970,"The second path to deploying the NIM is via the DGX Cloud Lepton CLI. To use the CLI, you must first install the Python package."],[1897,"The shared storage mounted in the job."],[1465,"The size of shared memory allocated to the container."],[551,"The source env/bin/activate command above activates a Python virtual environment with the dependencies installed. If you need to leave the virtual environment, you can run deactivate. To activate it again, navigate back to the directory where the virtual environment named env was saved and run source env/bin/activate again. If you run into ModuleNotFound errors, it is likely the environment needs to be re-activated."],[648,"The source env/bin/activate command above activates a Python virtual environment with the dependencies installed. If you need to leave the virtual environment, you can run deactivate. To activate it again, navigate back to the directory where the virtual environment named env was saved and run source env/bin/activate again. If you run into ModuleNotFound errors, it is likely the environment needs to be re-activated."],[1395,"The Starter Kits page shows a list of notebooks to choose from, as shown below."],[715,"The status should match the number of nodes and GPUs specified when the job was created."],[2015,"The status will update to Revoked, and the requestor will no longer be able to access the workspace."],[3028,"The sub field is the identity utilized for federation."],[2016,"The system thoroughly audits all temporary access, including the addition of support users to a workspace."],[1910,"The table below includes common scenarios and their solutions."],[977,"The template for launching the NIM via the CLI is as follows:"],[1256,"The Torchrun template streamlines PyTorch distributed training. To use it:"],[1127,"The total number of tokens that were processed in that step."],[662,"The train_step_timing in s line shows the overall throughput."],[1867,"The Training Configuration field contains several hyperparameters that can be modified if needed. Any optional fields left blank will be filled with default values. The settings are as follows:"],[1894,"The training dataset."],[1130,"The training process should take approximately five days to complete on eight H100 GPUs."],[1898,"The typical state progression for jobs is Queued -> Starting -> Running -> Completed. The job will first be in the queue until resources are available. It will then transition to Starting once resources have been allocated. Note that a job could be in the Starting state for a while if the image needs to be pulled from the registry."],[87,"Then, in the Resource section, select the node group and your desired resource shape. In this case, use H100-80GB-HBM3 x 1 from node group h100."],[887,"Then, on Pod 1, run the following command:"],[778,"Then, run the following command to set up ngrok. Replace $NGROK_AUTHTOKEN with the authtoken you copied in step 4."],[772,"Then, toggle the Enable JupyterLab checkbox to turn on that feature and click the Create button to create the Dev Pod."],[3112,"Then, toggle the Enable JupyterLab checkbox to turn on that feature."],[1948,"There are two kinds of permission policies:"],[700,"There are two primary methods for launching jobs with Ray, which NeMo-RL relies on: via a RayCluster and directly as a RayJob. The two are compared here. As a summary, choose RayCluster when you want resources to persist and plan on running several jobs, or choose RayJob when you want to schedule a job on just the resources you need, run a single task to completion, and immediately free up the resources once complete. This guide demonstrates both methods—follow whichever path best suits your needs."],[1930,"There are two types of storage available on DGX Cloud Lepton:"],[219,"There are two ways to add your machines to DGX Cloud Lepton:"],[62,"There are two ways to launch a Dev Pod: start from a blank container image or launch a Starter Kit, which contains a predefined notebook following various AI tasks and workflows."],[1499,"These models require an NVIDIA account with access to the NIM container registry."],[202,"This adds the job to the queue and schedules it when resources become available. The job should appear in the UI after the create function runs."],[1747,"This command returns a table showing resource shapes available in each node group. Values in the Shapes column indicate resource names you can use when submitting jobs via the CLI or SDK."],[3122,"This command sets a new token and restarts the JupyterLab instance."],[753,"This command should work with most custom Debian-based containers, including the latest PyTorch and NeMo containers available on ngc.nvidia.com."],[752,"This command will download the Cursor CLI and start a tunnel to the Dev Pod."],[795,"This command will download the VS Code CLI and start a tunnel to the Dev Pod."],[774,"This command will help set a new token and restart the JupyterLab instance."],[252,"This document focuses on how to create and manage BYOC node groups."],[1361,"This document guides you through configuring and accessing container ports in your Dev Pod."],[1067,"This document recreates the OpenMathReasoning model process as proposed by NVIDIA research to add math reasoning capabilities to an open source LLM (Moshkov et al., 2025). The specific example uses the open source Qwen2.5-Math-1.5B model as a base and fine-tunes it using NVIDIA's OpenMathReasoning dataset. This dataset includes millions of samples of math problems taken from the community with reasoning traces generated by DeepSeek-R1 and Qwen2.5-72B-Math-Instruct. The research team at NVIDIA found that running Supervised Fine-Tuning (SFT) with this dataset against top open-source models yielded state-of-the-art performance in various math benchmarks."],[878,"This example covers how to run NCCL test on a set of Dev Pods."],[459,"This example demonstrates how to create a batch job to download data from a private S3 bucket. A batch job workload is used due to the non-interactive nature of this task. The workload is configured with private information to access the S3 bucket and execute the commands necessary to install and run the s5cmd tool to download the data from that bucket to a mounted filestore."],[419,"This example demonstrates how to migrate a Slurm script to a DGX Cloud Lepton job."],[492,"This example shows how to run an NCCL performance benchmark job."],[812,"This example shows you how to configure SSH service and JupyterLab for a custom Docker image nvcr.io/nvidia/pytorch:25.04-py3 when using Dev Pod."],[495,"This example shows you how to run an NCCL performance benchmark job on DGX Cloud Lepton step by step."],[954,"This guide demonstrates how to deploy OpenAI's open source GPT-OSS-120B model as an endpoint on DGX Cloud Lepton for serving inference requests. The model is deployed as a NIM which is optimized for high throughput and low latency for your specific GPU architecture. This example showcases two different paths for deploying the NIM - via the UI in a web browser, and using the CLI in a local terminal."],[642,"This guide demonstrates how to launch an LLM pre-training job on DGX Cloud Lepton using NeMo Framework with minimal effort. You can use this guide to validate your cluster's setup and its readiness for larger-scale distributed jobs. For a complete end-to-end tutorial on training an LLM from scratch, refer to the full guide."],[1064,"This guide outlines how to use the Fine-tuning tool on DGX Cloud Lepton to add math reasoning capabilities to the Qwen2.5-Math-1.5B model to improve its accuracy in solving mathematical problems."],[3153,"This guide shows how to configure SSH to access your Dev Pod."],[1479,"This guide shows how to create a dedicated endpoint from a Docker image."],[1287,"This guide shows how to set up a RayCluster and launch a basic Ray job on the running cluster."],[539,"This guide uses two external services to simplify the LLM development process: Hugging Face and Weights & Biases."],[3043,"This guide will take you through each of the above steps."],[1916,"This happens when the dataset and or model are too large to fit in GPU memory. There are several ways to resolve this, typically in the following order.* Reduce Local Batch Size to 1 if not already.* Divide the Global Batch Size in half until the error is gone.* If your dataset has long input or output sequences, increase your Context Parallelism Size. If your model is 10B parameters or larger, increase your Tensor Parallelism Size. * Increase the number of GPUs for the job. There isn't a perfect way to find settings for any model and dataset, so some of these steps might need to be combined or repeated."],[483,"This is a step-by-step guide for training a NanoGPT model with distributed training on DGX Cloud Lepton."],[747,"This is provided as an example that was tested with version 1.0 of Cursor and may not work with different versions. Remote access for version 1.0 could be removed by the Cursor team at any time as well. For any issues encountered while following this document, please reach out to Cursor support. NVIDIA is unable to provide assistance with remote access to Cursor tools."],[3082,"This is the IAM Principal to use (within GCP IAM) to grant access to workloads\nin the DGX Cloud Lepton workspace, as shown below:"],[654,"This is the template for launching pre-training. Copy this locally as train.py:"],[152,"This lists your available workspaces and should look similar to the following if authentication was successful:"],[2170,"This new role will override the previous role assigned to the member."],[1301,"This opens a bash session on the head node in a virtual terminal at the bottom of the page. To verify the RayCluster, run ray status in the terminal. The output looks similar to the following, depending on the number of resources selected:"],[3006,"This page describes the limits and constraints that apply to workloads running on DGX Cloud Lepton.\nUnderstanding these limits helps you manage your workloads effectively and avoid potential issues."],[1231,"This page explains MPI and Torchrun templates in DGX Cloud Lepton."],[1163,"This page lists all the environment variables that can be used on DGX Cloud Lepton."],[1573,"This prevents new workloads from being scheduled to unhealthy nodes and helps avoid prolonged unhealthy states."],[149,"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."],[553,"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."],[650,"This prompts you to authenticate with your DGX Cloud Lepton workspace. If you're in a GUI-supported environment such as a desktop, a browser will open to the credentials page in your workspace. Otherwise, a URL will be displayed. Open this URL in a browser."],[1744,"This prompts you to authenticate with your DGX Cloud Lepton workspace. In a GUI‑supported environment such as a desktop, a browser opens to the credentials page in your workspace. Otherwise, a URL is displayed for you to open in a browser."],[813,"This PyTorch image doesn't come with SSH service and JupyterLab pre-configured, so manual setup is required for remote access and JupyterLab."],[889,"This result is obtained by running the NCCL tests on nvcr.io/nvidia/cuda-dl-base:24.10-cuda12.6-devel-ubuntu22.04."],[593,"This script will run after the model completes pre-training and find the final checkpoint in the training directory and convert it to Hugging Face format where it can be used for downstream tasks."],[1979,"This section details the process for granting DGX Cloud Lepton support temporary, essential access to customer workspaces for debugging and support."],[2061,"This section guides you through setting up a Docker Hub registry for your workspace."],[2073,"This section guides you through setting up a GCP Registry for your workspace."],[2096,"This section guides you through setting up a GitHub registry for your workspace using an access token."],[2086,"This section guides you through setting up an Azure Registry for your workspace."],[2111,"This section guides you through setting up an NVIDIA registry for your workspace."],[604,"This section is NOT intended for production inference deployments. The purpose of this section is to provide a quick way for engineers, QA teams, and other internal stakeholders to evaluate the model with user-generated prompts and inform decisions on the model's readiness. A production deployment would include user tracking, performance optimizations, and more."],[1093,"This section should look similar to the following image once complete:"],[2327,"This sets up autoscaling based on queries per minute,\nscaling between 1 and 3 replicas when QPM per replica exceeds 2.5."],[2382,"This sets up autoscaling based on queries per minute,\nscaling between 1 and 3 replicas when QPM per replica exceeds 2.5."],[844,"This setup enables you to remotely access your PyTorch Dev Pod for machine learning development, model training, and interactive computing tasks with the full power of the NVIDIA PyTorch container."],[938,"This should now look like the image below."],[1047,"This should now look like the image below."],[1305,"This simple script runs on two nodes with one GPU per node. It prints the node name and GPU index used for each worker, as well as the result of a simple matrix multiplication."],[1068,"This step-by-step guide shows how to use the Fine-tuning tool on DGX Cloud Lepton to add reasoning capabilities to Qwen2.5-Math-1.5B."],[3231,"This table contains DGX Cloud Lepton platform-specific labels attached to exported logs and metrics. These labels are available in the export destination (for example, Datadog) and can be used for filtering logs and metrics."],[661,"This will copy the training script to the cluster storage and spin up a Batch Job using the NeMo:25.04 container on a single GPU. You can view the job in the Batch Jobs page in the UI. After the job starts, it will take a couple of minutes for all of the libraries to load and training progress will be shown in the logs. You'll know training has started when you see a line similar to this:"],[1307,"This will install PyTorch on the head node which can be used on workers via a Ray runtime environment. To install and use different dependencies which aren't available in the base container, install the packages on the head node with pip3 and update the following line with additional package names by updating the [\"torch\"] list:"],[972,"This will install the Python SDK which includes the CLI."],[670,"This will launch a third job on 16 total GPUs on 2 nodes. Check the latest job in the UI and compare the train_step_timing in s again. This should ideally be approximately 2x faster than the eight GPU job run earlier."],[666,"This will launch the same job as before, but now on eight GPUs. Check the job in the UI and view the performance. Ideally, the train_step_timing in s should be approximately 8x faster than the 1 GPU numbers from earlier."],[1087,"This will open a new form to specify all of the settings for the fine-tuning job. The first prompts the user to select their fine-tuning method, either Supervised Fine-Tuning (SFT) or LoRA, a Parameter-Efficient Fine-Tuning (PEFT) technique. SFT updates the full model weights based on an instruction-following dataset while PEFT adds additional weights to the bottom of the model while freezing the base model weights. For this example, we will select SFT as we want to train the model to specifically respond with math reasoning traces for all prompts."],[528,"This workflow provides a full end-to-end example of preparing a dataset and training a hybrid SSM foundation model based on Nemotron-H using the redesigned NeMo 2.0 for NeMo Framework. This guide is split into sub-sections to describe each part in detail."],[541,"This workflow walks through training a Nemotron-H 8B model from scratch. The dataset you use needs to be tokenized using a custom tokenizer. NVIDIA, who produced the Nemotron-H models, published the tokenizer for the Nemotron-H models on Hugging Face. To use the tokenizer, you need to create a Hugging Face account and get an API token. The following steps guide you through that process."],[1246,"THIS_ADDR: Hostname of the current node"],[1264,"THIS_ADDR: Hostname of the current node"],[3274,"Timeline"],[1022,"To access the application, copy the URL after Endpoint in the job description and open it in a new browser tab. This will open the Gradio frontend where you can experiment with the Dia model."],[1533,"To access the endpoint via API, click the API tab on the endpoint detail page to find the API key and endpoint URL."],[1546,"To access the endpoint via API, click the API tab on the endpoint detail page to find the API key and endpoint URL."],[2185,"To access the secret value in the deployment, you can use the os module:"],[198,"To add your secret to the job specification, enter it in the image_pull_secrets field like below:"],[2005,"To approve a request, click Grant. The status will update to Approved."],[2002,"To approve or reject a request:"],[906,"To authenticate with W&B, set the WANDB_API_KEY environment variable to your API key created earlier:"],[284,"To bring your own compute to DGX Cloud Lepton, ensure your machines meet the following requirements."],[222,"To bring your own compute to DGX Cloud Lepton, you need to have a node group created first. If you don't have one, refer to the BYOC node group documentation to create a node group."],[691,"To build the container, create a new dev pod with the following options:"],[1002,"To build the container, create a new dev pod with the following options:"],[3224,"To collect and export metrics from workloads, enable metrics export when creating the workload and specify the export port and path."],[1300,"To connect to the head node, click the Terminal button to the right of the RayCluster as highlighted in the image below."],[1779,"To create a domain manually, navigate to the domains section under the Utilities tab and click the Add Domain button. In the pop-up window, specify the domain you want to route traffic to."],[1073,"To create a modified base model, open the Batch Jobs page and click the green Create Job button in the top right of the page."],[197,"To create a new secret, navigate to the Settings > Registries page in the UI and click the green + New registry auth button in the top-right corner of the page. Follow the prompts to add your private image token."],[3049,"To create a provider, choose Add provider."],[1291,"To create a RayCluster, navigate to the RayCluster creation page and click the Create Cluster button in the top right. This opens the form for creating the cluster."],[1649,"To create a reservation:"],[3163,"To create an SSH public key, first generate an SSH key pair in your local terminal:"],[1909,"To deploy the model for inference on DGX Cloud Lepton, refer to the Create LLM Endpoints with vLLM and SGLang guide. When prompted, load your model from storage and specify the full path to the checkpoint. This should end with epoch_X_step_YYYY/model/consolidated."],[605,"To deploy the model for inference, navigate to the Endpoints page, click the Create Endpoint button, and select the Create LLM Endpoint box."],[955,"To deploy the NIM, you need:"],[1904,"To do so, open the job details page by clicking the job in the Fine-tunes list. Click the Replicas tab to list all containers and click the Terminal button for the first container to open a bash session inside the container at the bottom of the page."],[1905,"To find your checkpoint, navigate to the location specified in Checkpoint Output Path section during job creation. For example, if you put /nfs-storage/fine-tuning/my-fine-tune-model for your checkpoint path, navigate to that directory with:"],[186,"To find your shared storage name, navigate to Nodes in the UI and select your desired node group. Next, click Actions > Config > Storage in the top-right corner of the page to list the available storage in the node group. If you have shared storage available, you should see something similar to the following image:"],[997,"To follow this example, you need:"],[547,"To get started with Weights & Biases, navigate to https://wandb.ai in a web browser and click the Sign Up button in the top right to create a free account. Once logged in, go to https://wandb.ai/settings and go to the bottom to create a new API key. This API key will be used while launching workflows to automatically log to Weights & Biases."],[986,"To get your <ENDPOINT_URL>, either copy the URL shown after the Endpoint: string in the UI under your deployed model, or run lep endpoint get -n <endpoint name> | grep external_endpoint. Use this URL to send requests to your model."],[66,"To launch a Dev Pod with a connected Jupyter session, go to the Starter Kits page to view curated notebooks. Select a notebook to open a read-only preview. To run it, click Create pod from this notebook at the top of the page and complete the Dev Pod settings."],[1304,"To launch a job with Ray, open the terminal on the head node and create a new file named train.py with the following contents:"],[702,"To launch a simple RayCluster on DGX Cloud Lepton, create a new Batch Job in the UI and enter the following fields:"],[718,"To launch a simple RayJob on DGX Cloud Lepton, create a new Batch Job in the UI and enter the following fields:"],[1308,"To launch the job, run python3 train.py in the terminal. This allocates two workers with a single GPU per worker in the RayCluster and runs the script. The output should look similar to the following:"],[448,"To list all jobs in some specific states, run:"],[447,"To list all jobs, run:"],[1840,"To load a model directly from Hugging Face Hub, click the Load from Hugging Face button. The field will update with a drop-down list where you can specify the model to fine-tune. The drop-down list contains several of the most popular text-generation LLMs available on Hugging Face Hub. You can either click an item in the list or start entering the name of a model's Hugging Face repo ID and selecting it from the list. For example, to use NVIDIA's Nemotron-Nano-9B-v2, either enter the full repo ID of nvidia/NVIDIA-Nemotron-Nano-9B-v2 in the field, or start typing Nemotron and select the model from the list."],[1039,"To load a model from storage, click the Load from Storage button and specify the volume to mount and the path to the model in the mounted storage, such as /path/to/my/model."],[1845,"To load a model from storage, ensure the desired model checkpoint is available on the shared storage that was mounted in the resource configuration section above. The model must be saved to shared storage prior to launching the fine-tuning job."],[1852,"To load the dataset from storage, specify the absolute path to the dataset file that is mounted in storage, such as /nfs-storage/my/custom/dataset.jsonl, assuming your shared storage was mounted as /nfs-storage."],[2006,"To reject the request, click Reject."],[1318,"To request a Slurm cluster, navigate to the Slurm Cluster page under Clusters and click the Request Slurm Cluster button to fill out the request form. Your NVIDIA Technical Account Manager (TAM) will reach out with more information and next steps."],[1454,"To require token authentication, click Add Endpoint Token to create a new token. DGX Cloud Lepton automatically generates the token."],[2012,"To revoke access before it expires:"],[1398,"To run a notebook in a Dev Pod, click the Create pod from this notebook button at the top of the preview page. This opens the Dev Pod creation form with some fields pre-configured (such as the container image). Enter the remaining fields to complete the setup, as described below."],[925,"To run the AlphaFold2 NIM, you need:"],[1032,"To run the Multi-LLM NIM, you need:"],[464,"To securely provide AWS credentials to your workload, you'll need to create secrets in DGX Cloud Lepton."],[622,"To send a request from the terminal, replace X.X.X.X with the endpoint URL in the curl command above. Additionally, replace Write me a short story about a baby dragon that learns to fly with your prompt of choice. This command will generate 2048 tokens but this can be changed as needed depending on the prompt."],[3071,"To set up workload identity federation, navigate to your organization's Google\nCloud Console, then go to IAM & Admin -> Workload Identity Federation.\nClick the CREATE POOL button to proceed."],[431,"To specify the priority, click on the dropdown in the Resource section and select one of the priorities. Refer to this guide for more details."],[1816,"To start a new fine-tuning job, click the green Create Fine-tune button on the page. This will open a new form to define the fine-tuning settings which are explained in the following sub-sections."],[26,"To start using your own compute with Lepton, follow these steps:"],[449,"To stop a job, run:"],[628,"To stop the job, go to the Endpoints page in the UI and click the Delete button next to the nemotronh-8b-base endpoint shown in the list."],[664,"to this:"],[668,"to this:"],[2209,"To use a template for dev pods that you have created, follow these steps:"],[1238,"To use the MPI template, follow the steps below:"],[1237,"To use these templates, select the Template option at the top of the create job page."],[976,"To validate authentication, run lep workspace list and you should see a table containing the workspace you connected to. You are now ready to use the CLI to launch endpoints."],[643,"To validate NeMo Framework on DGX Cloud Lepton, you need:"],[714,"To verify the RayCluster, run:"],[3268,"To view advanced logs:"],[1741,"To view available resource shapes using the CLI, first ensure the latest version of the DGX Cloud Lepton CLI is installed. Run the following command in a terminal with Python 3.10+:"],[3259,"To view real-time logs:"],[1394,"To view the list of notebooks, navigate to the Dev Pods page and click the green Create Pod button followed by Create From Notebooks. You can also access the list directly with this link."],[598,"To view your charts, navigate to https://wandb.ai. You should see a link to the newly created project on your home page. Clicking the link will take you to your project dashboard, which should look similar to the following. Note that the figure below includes training results for two different runs where the second run is a continuation of the first."],[1746,"To view your resource shapes, run:"],[2641,"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day in UTC)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:01 (1:10:01 AM of the current day)\ntoday 01:10:01.123456 (1:10:01 AM with microseconds precision)"],[2647,"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day in UTC)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:01 (1:10:01 AM of the current day)\ntoday 01:10:01.123456 (1:10:01 AM with microseconds precision)"],[2539,"today or td:\nExample Variations:\ntoday (defaults to midnight of the current day)\ntoday 01 (1 AM of the current day)\ntoday 01:10 (1:10 AM of the current day)\ntoday 01:10:05 (1:10:05 AM of the current day)"],[130,"Token"],[2213,"Token"],[3018,"Token"],[2966,"token : Prints the authentication token of the current workspace."],[2225,"Token can be used to authenticate requests to the Lepton API. Simply include the token in the Authorization header of your request:"],[2063,"Token description: A short description for the new access token."],[2221,"Token Name: The name of the token."],[2163,"Tokens (Access for API Tokens)"],[2218,"Tokens could be used to authenticate and authorize requests to DGX Cloud Lepton. They are essential for logging in via the CLI, API, or SDKs."],[2540,"tomorrow or tm:\nExample Variations:\ntomorrow (defaults to midnight of the next day)\ntomorrow 13 (1 PM of the next day)\ntomorrow 13:10 (1:10 PM of the next day)\ntomorrow 13:10:05 (1:10:05 PM of the next day)"],[1260,"Torchrun Command: The template provides a default command. You can customize it using the predefined environment variables."],[1233,"Torchrun Template"],[1236,"Torchrun: For PyTorch distributed training"],[312,"Total data disks: Minimum 640GB per GPU"],[1189,"Total number of workers assigned to the job"],[1221,"Total number of workers assigned to the job"],[1804,"Training Configuration"],[1613,"Trigger Health Check"],[1810,"Troubleshooting"],[272,"Trusted Launch Boot Option"],[891,"Tutorial for sending training metrics to Weights & Biases"],[135,"Tutorial on using the Python SDK to interact with DGX Cloud Lepton"],[599,"Two of the most important charts to monitor during pre-training are the reduced_train_loss and val_loss charts, which show how the model is learning over time. In general, these charts should have an exponential decay shape."],[1180,"Type of hardware accelerator used"],[3174,"Under Entrypoint, select one of the following options:"],[3177,"Under Expose Ports, click the Add Port button."],[1100,"Under Validation Dataset, select Load from Hugging Face and enter Maxwell-Jia/AIME_2024 for the dataset, train for the split, Problem for the Question field, and Solution for the Answer field as shown in the image below. Note that the dataset mapping is case-sensitive."],[24,"Unify your compute resources under Lepton's management interface"],[1183,"Unique identifier for the current worker"],[1740,"Unlike default GPU shapes, custom shapes reserve only the specified resources and won't burst beyond those limits."],[2809,"update : Updates a Ray cluster worker group's min replicas."],[2293,"update : Updates an endpoint."],[590,"Update the nodes=8 line to the desired number of nodes to train with. Keep gpus_per_node at eight as this allows optimal multi-node communication over NCCL."],[2856,"Updates a Ray cluster worker group's min replicas. The worker group name is\ninferred from the existing cluster when there is exactly one worker group."],[2366,"Updates an endpoint. Note that for all the update options, changes are made\nas replacements, and not incrementals. For example, if you specify\n--tokens, old tokens are replaced by the new set of tokens."],[2967,"url : Prints the url of the current workspace."],[2229,"Usage"],[2248,"Usage"],[2252,"Usage"],[2255,"Usage"],[2258,"Usage"],[2261,"Usage"],[2264,"Usage"],[2267,"Usage"],[2270,"Usage"],[2273,"Usage"],[2276,"Usage"],[2279,"Usage"],[2408,"Usage"],[2412,"Usage"],[2415,"Usage"],[2418,"Usage"],[2421,"Usage"],[2447,"Usage"],[2451,"Usage"],[2454,"Usage"],[2457,"Usage"],[2460,"Usage"],[2463,"Usage"],[2466,"Usage"],[2469,"Usage"],[2472,"Usage"],[2475,"Usage"],[2478,"Usage"],[2481,"Usage"],[2484,"Usage"],[2487,"Usage"],[2621,"Usage"],[2625,"Usage"],[2655,"Usage"],[2665,"Usage"],[2673,"Usage"],[2677,"Usage"],[2680,"Usage"],[2700,"Usage"],[2704,"Usage"],[2707,"Usage"],[2710,"Usage"],[2713,"Usage"],[2716,"Usage"],[2719,"Usage"],[2780,"Usage"],[2784,"Usage"],[2787,"Usage"],[2790,"Usage"],[2793,"Usage"],[2796,"Usage"],[2799,"Usage"],[2878,"Usage"],[2882,"Usage"],[2885,"Usage"],[2888,"Usage"],[2911,"Usage"],[2915,"Usage"],[2926,"Usage"],[2930,"Usage"],[2933,"Usage"],[2936,"Usage"],[2939,"Usage"],[2942,"Usage"],[2945,"Usage"],[2948,"Usage"],[2951,"Usage"],[2954,"Usage"],[2339,"Usage examples:\n--replica-spread required/r (strict)\n--replica-spread preferred/p (soft)"],[2387,"Usage examples:\n--replica-spread required/r (strict)\n--replica-spread preferred/p (soft)"],[2862,"Usage: lep raycluster submit -n [cluster] -- [entrypoint command] Everything\nafter \"--\" is treated as the entrypoint command, just like native Ray."],[2164,"Usages (View resource usage and billing information)"],[770,"Use a Built-in image provided by DGX Cloud Lepton."],[3110,"Use a Built-in image provided by DGX Cloud Lepton."],[3085,"Use a command like the following to copy all objects from the bucket to a local /data\ndirectory:"],[104,"Use a Node Group"],[2029,"Use a Private Registry"],[2194,"Use a Template"],[1703,"Use auto-approval for self-service team workflows."],[3036,"Use IRSA with the AWS SDK"],[3042,"Use IRSA with the AWS SDK – Confirm that the workload uses an AWS SDK of a supported version and that the workload uses the default credential chain."],[73,"Use SSH to access your Dev Pod"],[1928,"Use Storage for Workloads"],[771,"Use the Dev pod entrypoint as the Entrypoint."],[3111,"Use the Dev pod entrypoint as the Entrypoint."],[79,"Use the Endpoint"],[1055,"Use the fine-tuning tool to add math reasoning capabilities to an LLM"],[3089,"Use the gcloud CLI to authenticate, then set your project for the subsequent instructions:"],[255,"Used By: List of workspaces that can allocate resources from this node group. The current workspace is pre-selected and cannot be removed."],[1556,"User Quota"],[1579,"User Quota"],[1643,"User Roles and Permissions"],[1993,"User: Email address of the support team member"],[1984,"User: Grants the specialist permissions equivalent to a standard user in your organization."],[1591,"User: The user to allocate the quota to."],[1961,"User1 has access to /Common, /Data/dataset1, /Data/dataset2 and /User/user1"],[1962,"User2 has access to /Common, /Dataset/dataset2 and /user/user2"],[2070,"Username: Your Docker Hub username."],[2093,"Username: Your Registry username shown in the Azure portal."],[2128,"Username: Your Registry username."],[1844,"Users can also load a model directly from shared storage in case the model hasn't been uploaded to Hugging Face, such as models that shouldn't be shared publicly or early previews that were trained directly on the platform."],[1661,"Users can monitor approval status in the dashboard. Notifications are not yet available."],[1666,"Users can target a reservation when launching workloads. DGX Cloud Lepton attempts to place the workload on the reservation's nodes first. If insufficient capacity is available:"],[1665,"Users who are part of the reservation"],[422,"Uses a specific partition"],[89,"Uses one H100 GPU from node group h100"],[44,"Uses one H100 GPU from node group h100sxm-0"],[45,"Uses the default image and runs a simple counter that processes 10 items with a 2-second delay between each"],[2175,"Using Secrets"],[1209,"Using the DGX Cloud Lepton environment variables, you can construct the environment variables for various AI training framework abstractions."],[618,"Using the sliders on the right of the page, set the Temperature to 0.7, Max Tokens to 512, and Top P to 0.6."],[2217,"Using Tokens"],[2542,"UTC:2024/12/25 13:10:01"],[2543,"utc:tomorrow 13:10"],[1105,"Val Every Steps: Enter 100 to run a validation pass every 100 steps. On eight H100 GPUs, this will run validation approximately every 45 minutes."],[1870,"Val Every Steps: This is how often to run a validation pass using the validation dataset to determine how the model is learning over time. This must be a positive integer. Typical ranges are from 50 - a few thousand steps depending on your dataset size. Minimum: 1. Default: 50."],[137,"Validate installation"],[216,"Validate the Nodes"],[1802,"Validation Dataset"],[1756,"Validation is required for each node to confirm the health and operational status of the entire node group."],[809,"Verify JupyterLab"],[810,"Verify PyTorch Environment"],[808,"Verify SSH Setup"],[1044,"Very large models can take a long time to download if not already available in storage. To prevent the endpoint from failing before the model is downloaded, go to Advanced Configuration and set Healthcheck Initial Delay Seconds to Custom with a value of 7200 seconds or higher as necessary."],[2017,"View Access Granting Logs: Access logs for granting temporary access under Utilities → Observability → Audit logs."],[3192,"View audit logs"],[1808,"View Checkpoints"],[1478,"View Endpoint"],[3256,"View log context."],[1678,"View Reservation"],[37,"View the Batch Job"],[522,"View the Project Dashboard on Weights & Biases"],[1755,"View Validation Result and Logs"],[1700,"View: Only users included in the reservation (and all admins) can view or select it when launching workloads."],[1983,"Viewer: Allows the specialist to view information within the workspace."],[3280,"Viewing log context is helpful when filters match multiple time periods or scopes."],[1383,"Viewing Notebooks"],[1975,"Viewing Requests"],[2216,"Viewing Tokens"],[1432,"Visibility"],[2174,"Visibility"],[1348,"Visibility: If set to private, only the creator can access the job. If set to public, all users in the workspace can access the job."],[1416,"Visibility: Specifies the visibility of the job. If set to private, only the creator can access the job. If set to public, all users in the workspace can access it."],[1161,"Visibility: Specifies the visibility of the job. If set to private, only the creator can access the job. If set to public, all users in the workspace can access the job."],[256,"Visible To: List of workspaces that can view the node group."],[1521,"vLLM is a fast and easy-to-use library for LLM inference and serving. To create a dedicated endpoint with vLLM, follow these steps:"],[1966,"Volume: Select the specific volume you want to mount by name. An indicator shows the volume type."],[534,"VS Code installed on a local machine. Download instructions are available here."],[793,"VS Code is a powerful code editor that can be used to access a Dev Pod. In this guide, we will show you how to connect to a Dev Pod with VS Code via Remote Tunnels."],[897,"W&B supports a simple Python API to send training information to their servers. To use the API, users will need to create an access token on W&B, install the Python package, and tell W&B which values to track."],[823,"Wait for Completion: The script will automatically:"],[1449,"We do not currently support scaling up from zero replicas. If a deployment is\nscaled down to zero replicas, it will not be able to serve any requests until\nit is scaled up again."],[2179,"We provide quick ways to add SSH public keys, GitHub, Hugging Face, OpenAI, Datadog tokens, NGC API keys, and custom secrets as secrets to your workspace.\nAll secrets are stored and associated with the workspace in which they are created."],[315,"We recommend having at least eight physical CPU cores per GPU, for example, AMD EPYC 9004 Series or Intel 4th or 5th Gen Xeon CPUs."],[1490,"We recommend setting up an access token for your endpoint instead of making it public."],[1530,"We recommend setting up an access token for your endpoint instead of making it public."],[1543,"We recommend setting up an access token for your endpoint instead of making it public."],[653,"We'll start with a template script for single-GPU pre-training, then modify it to run on eight GPUs on a single node, and finally scale to 16 GPUs across two nodes."],[896,"Weights & Biases (W&B) is a widely-used tool for charting training metrics for machine learning jobs, such as loss curves, resource usage, accuracy scores, and more. This makes it easy to validate how a model learns over time and to compare multiple runs to determine the best models for certain outcomes."],[546,"Weights & Biases is a tool that allows developers to easily track experiments for AI applications. NeMo Framework natively supports logging many values such as training loss, learning rate, and gradient norm as well as resource utilization with Weights & Biases. Weights & Biases is highly recommended for tracking NeMo Framework jobs."],[3191,"What are audit logs?"],[1352,"What are Container Ports?"],[3244,"What are logs?"],[17,"What is Bring Your Own Compute?"],[811,"What This Example Achieves"],[1166,"When a batch job is launched on DGX Cloud Lepton, the following environment variable names are automatically set, with the values corresponding to the job configurations."],[1138,"When creating a batch job, you can configure the resource, container, and additional options."],[1368,"When creating a Dev Pod, configure container ports to expose in the Container Configuration > Expose Ports section."],[1325,"When creating a dev pod, you can configure resources, the container, and other options."],[1473,"When enabled, specify which replica to use for a request by including the X-Lepton-Replica-Target header with the replica ID. Example:"],[1515,"When finished, click Create Endpoint to create the endpoint."],[1841,"When loading a model from Hugging Face, the fine-tuning job will automatically download the model from Hugging Face into local container storage and begin the fine-tuning process using that model as a basis."],[1981,"When requesting access to your workspace, the support specialists will specify the permission level and duration of access."],[1115,"When resources are available, the job will get scheduled and the image will be pulled from the container registry and launch the fine-tuning job once ready."],[716,"When the cluster is ready, you can launch a NeMo-RL job in the terminal session. For example, to run GRPO against a specific model on two workers with 8 GPUs each, run the following:"],[627,"When the deployment is no longer needed, it can be stopped to free up additional compute resources."],[1418,"When the Dev Pod is running, click the job in the Dev Pods list to open the job details page. On this page, you can see additional information, including access via Terminal and JupyterLab."],[1900,"When the fine-tuning process starts, your job will be in the Running state. Clicking the job in the list will bring up more details. To view the live output including training progress, click the Logs button. This will display progress after each step indicating the latest training loss, the GPU memory usage at that step, how many steps have been completed, and more."],[1084,"When the job gets scheduled, it will pull the selected container with storage mounted at the designated location. The script will then download the Qwen2.5-Math-1.5B model to the mounted storage and modify the max_position_embeddings and rope_theta values in the downloaded model's config file as well as max_new_tokens in the generation config to support 128K context length. This modified model will be available in the fine-tuning jobs when loading from storage."],[1891,"When the job was created and by whom."],[3079,"where SUBJECT_ATTRIBUTE_VALUE represents the Kubernetes identifier\n(sub field in the token)."],[1895,"Where the checkpoint is saved."],[1572,"Whether to automatically pause scheduling for unhealthy nodes."],[1474,"Whether to collect logs from replicas.\nBy default, this follows the workspace setting."],[1096,"While inspecting the dataset on Hugging Face, there are a few splits which contain subsets of the dataset for specific tasks, such as cot for chain-of-thought, tir for tool-integrated reasoning, genselect for selecting promising responses, and additional_problems for a list of more problems. For this guide, we will just select the cot split which should yield noticeable improvements in downstream accuracy. As stated in the paper, each of these stages can be stacked on top of each other. You are welcome to follow these steps again to fine-tune with additional splits."],[1310,"While simple, this shows how to use the allocated RayCluster to run a multi‑node GPU job. For more complex examples, refer to the Ray documentation, which includes several tutorials covering a wide range of tasks."],[1903,"While the job is running, you can open a terminal directly in the container to view checkpoints and other files."],[601,"While the model trains, a checkpoint will be periodically saved in the background automatically. Per the command above, the checkpoints will be saved in the /nemo-workspace/nemotronh_8b/nemotronh_8b/<date>/checkpoints directory where <date> is a timestamp of when the job was launched. Only the 10 checkpoints with the best val_loss values as well as the latest checkpoint will be saved. These checkpoints will be used for future fine-tuning runs. If the pre-training process gets interrupted for any reason, it can be re-launched by either cloning the job in the UI or running the python3 nemotronh-pretrain.py command again. Training will automatically resume from the latest checkpoint."],[530,"While this guide demonstrates pre-training a Nemotron-H 8B model from scratch, you can modify it to train any supported model with NeMo 2.0. For more information about NeMo 2.0, including the latest list of supported models, see the NVIDIA NeMo Framework User Guide."],[1727,"While this is the upper bound, the GPU shape initially requests a much smaller CPU/memory footprint to leave room for CPU-only/memory shapes to be co-located on the same node.\nIf required, the GPU shape can then burst up to its maximum allocation as explained above.\nTo reiterate, a 1-GPU shape on an 8-GPU machine can burst up to one-eighth of the node's total CPU and memory."],[1299,"With a RayCluster started, you can connect to the head node to submit jobs or view the dashboard."],[3246,"With advanced logs:"],[1668,"With preemption enabled: The job may evict lower-priority workloads—even on other reservations or unreserved nodes."],[3245,"With real-time logs:"],[1085,"With the modified model saved to local storage, it is time to launch the fine-tuning job. The fine-tuning tool can be accessed from the explore page in your DGX Cloud Lepton workspace. Click the Fine-tuning box or navigate directly to the fine-tuning page."],[188,"With the storage name captured, we can define the mount specifications. To mount the volume, you will need to create a Python dictionary as follows:"],[2060,"With these configurations, you can launch your Dev Pod with a private image."],[70,"With these steps, you can launch a Dev Pod and start developing."],[548,"With VS Code installed on your local machine, run the application and open a new directory to save the scripts you'll use for launching jobs on the DGX Cloud cluster."],[1460,"Within the deployment, the secret value is available as an environment variable with the same name as the secret."],[348,"Within the job details page, you can see the status of each worker and the logs for each worker.\nYou can also use the Web Terminal to connect to the worker node and check the worker's status.\nOnce the job is finished, you will see the job with a Completed state."],[368,"Within the job details page, you can see the status of each worker and the logs for each worker.\nYou can also use the Web Terminal to connect to the worker node and check the worker's status.\nOnce the job is finished, you will see the job with a Completed state."],[3022,"Within the workload, the token is mapped to the file /var/run/secrets/kubernetes.io/serviceaccount/token and\n/run/secrets/kubernetes.io/serviceaccount/token. Workload applications can use this token for authentication."],[1667,"Without preemption: The job waits."],[1175,"worker"],[1202,"worker"],[1297,"Worker Group: Select the Node Group and resource shape for the worker(s). Using the same node group as the head node is recommended. Workers primarily run the workloads for submitted. To request multiple workers, increase Min Replicas. Add a storage mount using + Mount Storage. You can optionally specify a Group Name; otherwise, a name is generated for you."],[1223,"WORKER_ADDRS"],[1078,"Worker: Enter 1 for a single worker."],[402,"Workers"],[1145,"Workers: The number of workers to launch for the job. Defaults to 1."],[3017,"Workload Identity"],[3070,"Workload Identity Federation eliminates the\nmaintenance and security burden associated with service account keys. With\nWorkload Identity Federation, you can use Identity and Access Management (IAM)\nto grant external identities IAM roles, and direct access on Google Cloud\nresources. You can also grant access through service account impersonation (not\ncovered here)."],[3069,"Workload Identity Federation via the gcloud CLI"],[3001,"Workload Limits"],[3015,"Workload Overview"],[1670,"Workloads from other users not in the reservation cannot use the reserved nodes unless preemption rules allow it."],[116,"Workspace"],[2245,"workspace : Manage workspace access to the DGX Cloud Lepton."],[1656,"Workspace > Nodes > Config > Reservations"],[1682,"Workspace Admin"],[1664,"Workspace Admins"],[2226,"Workspace ID could be found on the Settings - General."],[2165,"Workspace Info (View and edit workspace information)"],[1555,"Workspace Quota"],[1578,"Workspace Quota"],[2010,"Workspace Restriction: Access is restricted solely to the specified Workspace. Other Workspaces remain inaccessible."],[1687,"Workspace User (in reservation)"],[1692,"Workspace User (not in reservation)"],[13,"Workspace: Configure your workspace settings, including members, tokens, secrets, registry, and more."],[175,"Workspaces"],[3087,"Workspaces within DGX Cloud Lepton become Kubernetes namespaces prefixed by ws-. Therefore,\nworkspaceID will become the Kubernetes namespace ws-workspaceID."],[1220,"WORLD_SIZE"],[2642,"yesterday or yd:\nExample Variations:\nyesterday (defaults to midnight of the previous day in UTC)\nyesterday 13 (1 PM of the previous day)\nyesterday 13:10 (1:10 PM of the previous day)\nyesterday 13:10:05 (1:10:05 PM of the previous day)\nyesterday 13:10:01.123456 (1:10:01 PM with microseconds precision on the previous day)"],[2648,"yesterday or yd:\nExample Variations:\nyesterday (defaults to midnight of the previous day in UTC)\nyesterday 13 (1 PM of the previous day)\nyesterday 13:10 (1:10 PM of the previous day)\nyesterday 13:10:05 (1:10:05 PM of the previous day)\nyesterday 13:10:01.123456 (1:10:01 PM with microseconds precision on the previous day)"],[1825,"You are compute constrained."],[1933,"You are responsible for managing and cleaning up the data."],[1783,"You can add multiple endpoints to a domain and assign weights to each one. Traffic is routed to endpoints based on the assigned weights."],[1455,"You can add multiple tokens and modify generated token values."],[2143,"You can add permissions for more than one resource to a role."],[1938,"You can add those two types of storage for your node group."],[229,"You can add up to 10 machines at once by adding each machine on a new line."],[233,"You can also add your machines via local command, powered by GPUd:"],[3263,"You can also click the Live button in the top-right corner to switch to live mode."],[1590,"You can also configure the user quota for the node group to allocate resource quota to users to run workloads."],[2057,"You can also create a custom registry auth by providing the registry URL, username, and password."],[3267,"You can also enable advanced logs for a specific deployment, job, or pod by selecting the option in the Advanced Configuration section during creation."],[240,"You can also release machines from the node group, which are represented as Nodes in the node group page."],[1313,"You can also scale down to use fewer workers if you do not need the existing capacity. When scaling down, extra workers are deleted and Ray updates automatically to reflect the desired number of workers."],[210,"You can also send feedback for non-urgent issues or general comments by clicking the Send Feedback link. Note that a response is not guaranteed when using this feedback option."],[907,"You can also set this environment variable directly in the platform when defining the job."],[193,"You can also specify multiple mounts by making the dictionary above a list of dictionaries, similar to the following:"],[295,"You can also use release 535 family, with version ≥ 535.230.02."],[94,"You can also use the endpoint URL to make API requests. Go to the API tab on the endpoint details page for details."],[864,"You can change the Dockerfile based on your needs to build your own container image. For more information about the Dockerfile, please refer to the Dockerfile reference."],[900,"You can check if your container already has W&B installed with:"],[1569,"You can configure Gang Scheduling, which determines whether multiple replicas in the same group are scheduled together.\nBy default, this option is enabled."],[1566,"You can configure the storage for the node group, including Node Local Volume and Static NFS Volume."],[2220,"You can create a new token on the Settings - Tokens tab on the left-hand side of the workspace settings page.\nWhen creating a new token, following fields can be configured based on your needs:"],[1440,"You can create an endpoint in several ways. Refer to the following guides for details:"],[1575,"You can enable Auto Reboot only when Auto Pause Schedule is enabled."],[1597,"You can enable container image acceleration for the node group. With this feature, a node executing pods can pull container image data from other nodes in the group if the image is already cached there."],[2547,"You can filter jobs by: - State: Case-insensitive prefix match (e.g., 'run'\nmatches 'Running') - User: Case-insensitive prefix match (e.g., 'alice'\nmatches 'alice123') - Name/ID: Case-insensitive substring match (e.g.,\n'train' matches 'training-job-123') - Node Group: Case-insensitive substring\nmatch"],[885,"You can find the ssh port from the Pod detail page. Commonly, the ssh port is 2222 since the Pod is utilizing a full node."],[2089,"You can learn more about Azure access tokens in the official documentation."],[2119,"You can learn more details in the official documentation."],[2067,"You can learn more in the official documentation."],[1782,"You can manage routing rules for a domain by clicking the Edit button."],[1785,"You can monitor domain traffic by clicking the domain name. Available metrics include QPM, request size, response size, and more."],[1117,"You can monitor the job's progress by clicking the Logs tab in the job detail page. Given the large size of the dataset, it can take 15 minutes or more for the dataset to fully load and process. During this time you might not see log output for a while so don't be alarmed if your logs appear to be stuck after this state:"],[1969,"You can mount multiple storage volumes simultaneously. After the pod is created, you can view the mounted storage volumes in the details page."],[3139,"You can now access JupyterLab by clicking the generated proxy URL shown in the JupyterLab tab. Alternatively, visit the host node IP address with port 18889 (or 8888) if you chose to expose the port through Host Network."],[3123,"You can now access JupyterLab by clicking the generated proxy URL shown in the JupyterLab tab. Alternatively, visit the host node IP address with port 18889 if you chose to expose the port through Host Network."],[882,"You can obtain the local IP of the node from the dashboard by clicking on the node name and find it in the node detail page."],[2117,"You can only include Private Registry when you are using Enterprise account on NVIDIA NGC."],[2224,"You can only see the tokens created by you in the list."],[1445,"You can override this behavior with three autoscaling options and related flags:"],[2104,"You can read more in the official documentation."],[786,"You can reattach to the tmux session by running tmux a."],[2578,"You can remove a job by either name or id: - If removing by name, only the\nnewest job with that exact name will be removed - If removing by id, the\nspecific job with that id will be removed"],[1764,"You can run a node validation and view past results for the node group under the Validations tab."],[2570,"You can search by either name or id: - If searching by name, returns all\njobs with that exact name - If searching by id, returns the specific job\nwith that id"],[505,"You can see the logs of the job by clicking on the Logs button to check the test result."],[3213,"You can see two export configurations:"],[1411,"You can specify additional environment variables or secrets to set inside the running container. The name of the selected notebook is pre-configured as the LEPTON_DEVPOD_NOTEBOOK_NAME variable."],[3225,"You can specify the export port and path under Advanced Configuration when creating the workload."],[3172,"You can still use SSH to access your Dev Pod by manually setting up SSH in the container. Install and run the SSH server after starting the Dev Pod (see the example below), or install the SSH server as part of the run command (see the Install and Configure SSH and Jupyter for Custom Images example)."],[740,"You can then load the Conda environment in the job by adding the following command in the Run Command field during job creation.\nMake sure Conda is installed in the container image."],[2204,"You can use placeholders in the configuration to make your template more flexible. Use placeholders in the following fields:"],[3271,"You can use the following filters from the left sidebar to get a refined search experience."],[1021,"You can view your endpoint in the Endpoints page. Once it's running, it will look similar to the following:"],[2223,"You can view your tokens on the Token tab on the left-hand side of the workspace settings page."],[1822,"You desire deep expertise in a broader field and accuracy is crucial."],[1823,"You have a dataset comprising thousands of examples of labeled data."],[1827,"You have a small dataset with hundreds of examples."],[1826,"You have a very targeted use-case with very specific examples."],[1821,"You have lots of compute to fine-tune the model."],[1500,"You must have an NVIDIA account with access to the NIM container registry and configure the registry auth key on DGX Cloud Lepton."],[287,"You must have root access to your machine."],[2069,"You need to fill in the following fields:"],[2091,"You need to fill in the following fields:"],[2107,"You need to fill in the following fields:"],[2125,"You need to fill in the following fields:"],[503,"You need to fill in the YOUR_SSH_PUBLIC_KEY and YOUR_SSH_PRIVATE_KEY with your own credentials."],[65,"You need to have a node group with available nodes in the workspace first."],[47,"You need to have a node group with available nodes in your workspace first."],[91,"You need to have a node group with available nodes in your workspace first."],[1376,"You need to select at least one Pass Through method for each port you configure."],[886,"You should see \"Done\" printed at the end of the output."],[861,"You should see the GPU setup information like the following."],[2168,"You will see a list of all the members in your current workspace."],[2137,"You will see a list of built-in roles at the top: admin, user, and viewer. By default, the user role has read access to all resources, and the admin role has both read and write access to all resources. Built-in roles cannot be edited or deleted."],[236,"You will see the generated command like the following script and the expiration time:"],[780,"You will see the ngrok tunnel start, and the public URL will be displayed in the terminal."],[557,"You will use four different scripts to prepare the Nemotron-CC dataset for pre-training a Nemotron-H-based SSM. These scripts will be automatically copied to the remote filesystem once launched. First, create a new sub-directory locally to save all of the files using this command:"],[1461,"Your defined environment variables should not start with the name prefix\nLEPTON_, as this prefix is reserved for predefined environment variables."],[1152,"Your defined environment variables should not start with the name prefix LEPTON_, as this prefix is reserved for predefined environment variables. The following environment variables are predefined and will be available in the job:"],[286,"Your machine must run Ubuntu 22.04 LTS or a newer LTS version with a corresponding kernel version. For example, you can use Ubuntu 22.04 LTS with either kernel 5.15 or 6.8 since they are both compatible with 22.04 LTS.\nRefer to the official Ubuntu kernel release cycle for more details."],[327,"Your machines must pass the NCCL test provided by NVIDIA."],[1980,"Your Technical Account Manager will notify you of pending requests. Administrators can find these requests in the Temporary Access tab within your workspace's Settings, where they will have a Pending status."],[2025,"Your workspace members should be in the same NCA account. You can verify this from the NCA 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