AI Applications | NVIDIA Run:ai Documentation

This guide explains the procedure for managing AI applications.

AI Applications Table

The AI applications table can be found under Workload manager in the NVIDIA Run:ai platform.

The AI applications table provides a list of all the AI applications scheduled on the NVIDIA Run:ai Scheduler, and allows you to manage them.

The AI applications table consists of the following columns:

Column	Description
AI application	The name of the AI application
Type	The name of the Helm chart
Status	The different phases in an AI application lifecycle
Project	The project in which the AI application runs
GPU compute request	Amount of GPU devices requested
GPU compute allocation	Amount of GPU devices allocated
GPU memory request	Amount of GPU memory Requested
GPU memory allocation	Amount of GPU memory allocated
CPU compute request	Amount of CPU cores requested
CPU compute allocation	Amount of CPU cores allocated
CPU memory request	Amount of CPU memory requested
CPU memory allocation	Amount of CPU memory allocated

Connections Associated with the AI Application

A connection refers to the method by which you can access and interact with the AI application’s running workloads. It is essentially the “doorway” through which you can reach and use the services the application provides.

Click one of the values in the Connection(s) column to view the list of connections and their parameters. Connections are network interfaces that communicate with the workloads running inside the AI application. A connection is either the URL the application exposes or the IP and port of the node the workload is running on.

Column	Description
Name	The name of the application running on the workload
Connection type	The network connection type selected for the workload
Access	Who is authorized to use this connection (everyone, specific groups/users)
Port	The port on the node through which the workload is accessible
Address	The connection URL
Copy button	Copy URL to clipboard
Connect button	Enabled only for supported tools

AI Application Status

The AI application status in NVIDIA Run:ai reflects the underlying Helm release status.

NVIDIA Run:ai surfaces the Helm chart state as-is and maps it to the AI application lifecycle. For a complete description of Helm release states, see the Helm documentation.

Customizing the Table View

Filter - Click ADD FILTER, select the column to filter by, and enter the filter values
Search - Click SEARCH and type the value to search by
Sort - Click each column header to sort by
Column selection - Click COLUMNS and select the columns to display in the table
Download table - Click MORE and then Click Download as CSV. Export to CSV is limited to 20,000 rows.
Refresh - Click REFRESH to update the table with the latest data
Show/Hide details - Click to view additional information on the selected row

Show/Hide Details

Click a row in the AI applications table and then click SHOW DETAILS at the upper-right of the action bar to open the details pane.

The details pane displays a diagram of the AI application’s structure. Each workload in the AI application appears in its own group, connected to the endpoints and Kubernetes resources it uses:

Workload - The workload, with a status indicator (Running, Pending, Failed, and so on)
Endpoint - A network endpoint the workload exposes
Secret - A Kubernetes Secret the workload references
PVC - A PersistentVolumeClaim the workload references
ConfigMap - A Kubernetes ConfigMap the workload references

A legend in the upper-left corner of the diagram identifies each node type. Use the Type filter or the search box above the diagram to filter nodes by type or name.

Click a workload node to expand it and view its GPU compute allocation, GPU memory allocation, CPU compute allocation, and CPU memory allocation. The expanded view includes a link to open the workload in the Workloads table.

Use the zoom and pan controls, or the minimap, to navigate the diagram.

Creating an AI Application

Note

AI application submission is enabled by default. If you do not see it in the menu, contact your administrator to enable it under General settings → Workloads → AI application submission.

To create an AI application, go to Workload manager → AI applications.
Click + NEW AI APPLICATION
Within the new form, select the cluster and project. To create a new project, click +NEW PROJECT and refer to Projects for a step-by-step guide.
Enter a unique name for the AI application. If the name already exists in the project, you will be requested to submit a different name.
Under Source, select the AI application source:
- NGC catalog - GPU-optimized containers, pre-trained models, and Helm charts pulled from the NVIDIA GPU Cloud (NGC). Recommended for standard NVIDIA-certified AI stacks and enterprise-ready models:
  - Select a Repository from the dropdown menu. Repositories can be public or private.
  - If the selected repository is private, a Set repository credentials section appears. Select an existing NGC API key credential or create a new one. The credential is used to authenticate with the private repository and browse its charts.
  - Select or type a Chart name from the available charts in the selected repository.
  - Select the Chart version to deploy. To browse available charts, click View on NGC catalog.
- Custom URL - Provide a link to a Helm chart hosted on a private or public repository (for example, GitHub, S3, or a private registry). Use this for proprietary applications or customized versions of existing charts:
  - Enter the Chart URL. The URL must point directly to a versioned Helm chart package (.tgz file), for example: https://helm.ngc.nvidia.com/nvidia/blueprint/charts/nvidia-blueprint-rag-v2.3.0.tgz.
- Upload file - Upload a packaged Helm chart (.tgz) file from your local machine. Use this to deploy a chart you built locally without hosting it in a repository or registry:
  - Click Chart file and select a .tgz file. The file must be no larger than 10 MB.
Optional: Under Set application overrides to override specific parameters in your Helm chart, enter the corresponding key-value pairs in YAML format. These will replace the defaults in your values.yaml.
```
key_one: "<value>"
key_two:
  key_two_subkey_one: "<value>"
  key_two_subkey_two: "<value>"
```
Click CREATE AI APPLICATION

Managing and Monitoring

After the AI application is created, the workloads are added to the Workloads table, where they can be managed and monitored.

Accessing AI Application Endpoints

NVIDIA Run:ai automatically discovers and displays externally accessible network endpoints for each workload within an AI application. Endpoints are surfaced when the Helm chart includes Kubernetes networking resources. For more information, see Kubernetes Services, Load Balancing, and Networking.

The Connection(s) column shows the endpoint URL directly if there is one workload endpoint, or the number of endpoints if there are multiple. Click the value to open the connections panel and see the full list of endpoints per workload.

Using an endpoint:

Click Copy to copy the URL to your clipboard.

No endpoints displayed:

If no endpoints appear, the workloads may not yet be in a running state, or the networking configuration in the Helm chart may not be set up correctly. Check the following:

Verify the AI application status is Running.
Confirm that the Helm chart includes the required networking configuration.
Check the Helm values to ensure networking is enabled — some charts disable it by default.

Using API

Go to the AI Applications API reference to view the available actions.