2. Installation

This page outlines how to install or update Clara Deploy SDK on your system.

• As of version 0.8.0, Clara must have the following dependencies met:
  1. Operating System: Ubuntu 18.04 or 20.04
  2. NVIDIA Docker: 2.2.0
  3. Docker: Docker-ce (for Ubuntu 18.04), Docker.io (for Ubuntu 20.04)
  4. Kubernetes: 1.19
  5. Helm: 3.4
• Additionally, it is important to note that because Clara does not provide authentication based security, it should be deployed in a secure environment which is able to restrict access to Clara to approved users and services.

All of the necessary package dependencies can be installed via our Ansible installer.

• We also need CUDA version >= 11.1
• NVIDIA Driver version to be at least 450.80.02.
• For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
  • Installation of CUDA Toolkit would make both CUDA and NVIDIA Display Drivers available
• NVIDIA GPU is Pascal or newer, including Pascal, Volta, Turing and Ampere families
• At least 30GB of available disk space

See this Ansible page on NGC for Ansible installation instructions.

The deploy script will automatically start the following components:

• The Clara Platform
• The DICOM Adapter
• The Render Server

To verify that the installation is successful, run the following command:

            

            
helm ls
        

The following three helm charts should be returned:

• clara
• clara-dicom-adapter
• clara-render-server
• clara-console

To verify that the helm charts are up and running, run the following command:

            

            
kubectl get pods
        

The command should return pods with the below prefixes:

• clara-clara-platformapiserver-
• clara-dicom-adapter-
• clara-render-server-clara-renderer-
• clara-resultsservice-
• clara-ui-
• clara-console-
• clara-console-mongodb-
• clara-workflow-controller-

They should be all in a Running state

Researchers and data scientists who don’t have access to a GPU server can still get started with Clara Deploy SDK without needing to become a Docker and Kubernetes expert. Clara Deploy SDK has been validated on the following services:

• Amazon Web Services (AWS)
• Microsoft Azure Cloud Services (Azure)
• Google Cloud Platform Services (GCP)

The following subsections describe the configuration for each CSP. Once the VM is provisioned according to the documentation, you can follow the Steps to Install section above to install Clara Deploy SDK.

2.4.1. AWS Virtual Machine Configuration

The AWS VM configuration used for testing can be found below:

• Location : US East (Ohio)
• Operating System : Ubuntu 18.04
• Amazon machine image : Ubuntu Server 18.04 LTS (HVM), SSD Volume Type (64-bit)
• Instance type : p3.8xlarge (32 vcpus, 244 GB memory, 4 NVIDIA GPUs in the Pascal, Volta and Turing families)
• Storage: General Purpose SSD (100 GB)
• Ports Open : SSH, HTTP, HTTPS

2.4.2. Azure Virtual Machine Configuration

The Azure VM configuration used for testing can be found below:

• Location : West US2
• Operating System: Ubuntu 18.04
• Size : Standard NC6s_v2 (6 vcpus, 112 GB memory, 1 GPU-NVIDIA Tesla P100)
• OS Disk Size : Premium SSD, 300GB (mounted on root)
• Ports Open : SSH, HTTP, HTTPS

2.4.3. GCP Virtual Machine Configuration

The GCP VM configuration used for testing can be found below:

• Location :
  • Region: us-central1 (Iowa)
  • Zone: us-central1-c
• Operating System : Ubuntu 18.04 LTS
• Machine type: 8vCPU, 32GB, 1 GPU (NVIDIA Tesla P4), Turn on display device
• Disk Size: SSD 100GB
• Ports Open : SSH, HTTP, HTTPS

2.4.4. ESXi Installation

To run on an ESXi host machine, ensure the following settings are enabled to allow PCIe passthrough:

• In the BIOS settings on the ESXI host, enable SR-IOV & Intel Virtualization Technology for Directed I/O (VT-d) or AMD I/O Virtualization Technology (IOMMU)
• When in the ESXi console, when creating a new VM:
  • Under Customize Settings > Virtual Hardware, after selecting the desired amount of system memory, click the toggle to view all memory-related settings
    • Click the checkbox for Reserve all guest memory (All locked)
  • Under Customize Settings > VM Options, select Advanced, and then click Edit configuration.
  • Select EFI under boot options
  • To enable GPU Passthrough, while still under the Configuration Parameters of the VM Options tab add the following 2x key-value pairs:
    • key:pciPassthru.64bitMMIOSizeGB value“128”
    • key:pciPassthru.use64bitMMIO value:“TRUE”

This document is a guide to follow for installing Clara in a system protected by a firewall. It assumes that there are two systems, a staging system which has access to internet and a production system guarded by a firewall. This assumes that Docker is installed on the staging system. Please follow the following steps to make the container images needed for Clara platform to be available on the production system.

2.5.1. Login to staging system

            

            
ssh <staging-system-username>@<staging-system-ip>
        

2.5.2. Log in to the DGX Container Registry.

            

            
$ docker login nvcr.io
Username: $oauthtoken
Password: apikey
        

Type “$oauthtoken” exactly as shown for the Username. This is a special username that enables API key authentication. In place of apikey, paste in the API Key text that you obtained from the DGX website.

2.5.3. Download container images on staging system

            

            
docker pull nvcr.io/nvidia/clara/platformapiserver:<clara-version>
docker pull nvcr.io/nvidia/clara/model-sync-daemon:<clara-version>
docker pull nvcr.io/nvidia/clara/podmanager:<clara-version>
docker pull nvcr.io/nvidia/clara/resultsservice:<clara-version>
docker pull nvcr.io/nvidia/clara/nodemonitor:<clara-version>
docker pull nvcr.io/nvidia/tensorrtserver:20.02-py3
docker pull argoproj/argoui:v2.2.1 argoproj/workflow-controller:v2.2.1
docker pull fluent/fluentd-kubernetes-daemonset:v1.11.0-debian-elasticsearch7-1.0
        

2.5.4. Generate a tar.gz for container images on staging system

            

            
docker image save -o clara-images.tar nvcr.io/nvidia/clara/platformapiserver:<clara-version> nvcr.io/nvidia/clara/model-sync-daemon:<clara-version> nvcr.io/nvidia/clara/podmanager:<clara-version> nvcr.io/nvidia/clara/resultsservice:<clara-version> nvcr.io/nvidia/clara/nodemonitor:<clara-version> nvcr.io/nvidia/tensorrtserver:20.02-py3 argoproj/argoui:v2.2.1 argoproj/workflow-controller:v2.2.1 fluent/fluentd-kubernetes-daemonset:v1.11.0-debian-elasticsearch7-1.0; gzip clara-images.tar
        

In addition to the list specified above, you can also save any images in use for Clara. These images generally have the following prefixes:

            

            
nvcr.io.
ngc.nvidia.com.
nvidia.github.io.
        

You can get a list of any such images using the docker images command. For example:

            

            
docker images | grep "nvcr.io."
        

2.5.5. Login to production system

            

            
ssh <production-system-username>@<production-system-ip>
        

2.5.6. Transfer Images to production system

            

            
scp <staging-system-username>@<staging-system-ip>:/<path-to-images-archive>/clara-images.tar.gz .
        

2.5.7. Load Images on production system

            

            
docker load -i clara-images.tar.gz
        

After this is done, the minimum set of images needed for running Clara are available on the production system. So, Clara Platform can now be installed on the production system.