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Part 2: Blueprint

What Will You Do

In this part of the self-paced exercise, you will create a custom cluster blueprint with Nvidia's GPU Operator based on declarative specifications.

Step 1: GPU Operator Repository

Nvidia distributes their GPU Operator software via their official Helm repository. In this step, you will create a repository in your project so that the controller can retrieve the Helm charts automatically.

  • Open Terminal (on macOS/Linux) or Command Prompt (Windows) and navigate to the folder where you forked the Git repository
  • Navigate to the folder "/getstarted/gpumks/addon"

The "repository.yaml" file contains the declarative specification for the repository. In this case, the specification is of type "Helm Repository" and the "endpoint" is pointing to Nvidia's official Helm repository. 

apiVersion: config.rafay.dev/v2
kind: Repository
metadata:
  name: gpu
spec:
  repositoryType: HelmRepository
  endpoint:  https://helm.ngc.nvidia.com/nvidia
  credentialType: CredentialTypeNotSet

Type the command below 

rctl create repository -f repository.yaml

If you did not encounter any errors, you can optionally verify if everything was created correctly on the controller.

  • Navigate to your Org and Project
  • Select Integrations -> Repositories and click on "gpu"

Repository

Step 2: Create Namespace

In this step, you will create a namespace for the Nvidia GPU Operator. The "namespace.yaml" file contains the declarative specification

The following items may need to be updated/customized if you made changes to these or used alternate names.

  • value: demo-gpu-mks
kind: ManagedNamespace
apiVersion: config.rafay.dev/v2
metadata:
  name: gpu-operator-resources
  description: namespace for gpu-operator
  labels:
  annotations:
spec:
  type: RafayWizard
  resourceQuota:
  placement:
    placementType: ClusterSpecific
    clusterLabels:
    - key: rafay.dev/clusterName
      value: demo-gpu-mks
  • Open Terminal (on macOS/Linux) or Command Prompt (Windows) and navigate to the folder where you forked the Git repository
  • Navigate to the folder "/getstarted/gpumks/addon"
  • Type the command below 
rctl create namespace -f namespace.yaml

If you did not encounter any errors, you can optionally verify if everything was created correctly on the controller.

  • Navigate to the "defaultproject" project in your Org
  • Select Infrastructure -> Namespaces
  • You should see a namespace called "gpu-operator-resources"

Nvidia GPU Namespace

Step 3: Create Addon

In this step, you will create a custom addon for the Nvidia GPU Operator. The "addon.yaml" file contains the declarative specification

  • "v1" because this is our first version
  • Name of addon is "gpu-operator"
  • The addon will be deployed to a namespace called "gpu-operator-resources"
  • You will be using "v23.3.1" of the Nvidia GPU Operator Helm chart
  • You will be using a custom "values.yaml as an override 
kind: AddonVersion
metadata:
  name: v1
  project: defaultproject
spec:
  addon: gpu-operator
  namespace: gpu-operator-resources
  template:
    type: Helm3
    valuesFile: values.yaml
    repository_ref: gpu
    repo_artifact_meta:
      helm:
       tag: v23.3.1
       chartName: gpu-operator

Type the command below 

rctl create addon version -f addon.yaml

If you did not encounter any errors, you can optionally verify if everything was created correctly on the controller.

  • Navigate to your Org and "Default" Project
  • Select Infrastructure -> Addons
  • You should see an addon called "gpu-operator

GPU Operator Addon

Step 4: Create Blueprint

In this step, you will create a custom cluster blueprint with the Nvidia GPU Operator and a number of other system addons. The "blueprint.yaml" file contains the declarative specification.

  • Open Terminal (on macOS/Linux) or Command Prompt (Windows) and navigate to the folder where you forked the Git repository
  • Navigate to the folder "/getstarted/gpumks/blueprint"
kind: Blueprint
metadata:
  # blueprint name
  name: gpu-blueprint
  #project name
  project: defaultproject
  • Type the command below 
rctl create blueprint -f blueprint.yaml

If you did not encounter any errors, you can optionally verify if everything was created correctly on the controller.

  • Navigate to your Org -> defaultproject
  • Select Infrastructure -> Blueprint
  • You should see an blueprint called gpu-blueprint

GPU Blueprint

New Version

Although we have a custom blueprint, we have not provided any details on what it comprises. In this step, you will create and add a new version to the custom blueprint. The YAML below is a declarative spec for the new version. 

kind: BlueprintVersion
metadata:
  name: v1
  project: defaultproject
  description: Nvidia GPU Operator 
spec:
  blueprint: gpu-blueprint
  baseSystemBlueprint: default
  baseSystemBlueprintVersion: ""
  addons:
    - name: gpu-operator
      version: v1
  # cluster-scoped or namespace-scoped
  pspScope: cluster-scoped
  rafayIngress: false
  rafayMonitoringAndAlerting: true
  kubevirt: false
  # BlockAndNotify or DetectAndNotify
  driftAction: BlockAndNotify
  • Type the command below to add a new version
rctl create blueprint version -f blueprint-v1.yaml

If you did not encounter any errors, you can optionally verify if everything was created correctly on the controller.

  • Navigate to your Org -> defaultproject
  • Select Infrastructure -> Blueprint
  • Click on the gpu-blueprint custom cluster blueprint

v1 GPU Blueprint

Next, we will update the cluster to use the newly created blueprint.

  • Type the command below. Be sure to update the cluster name, demo-gpu-mks, and the blueprint name in the command below with the name of your resources
rctl update cluster demo-gpu-mks --blueprint gpu-blueprint --blueprint-version v1

Apply Blueprint

Step 5: Verify GPU Operator

Now, let us verify whether the Nvidia GPU Operator's resources are operational on the MKS cluster

  • Click on the kubectl link and type the following command 
kubectl get po -n gpu-operator-resources

You should see something like the following. Note, it will take ~6 minutes for all of the pods to get to a running state.

NAME                                                          READY   STATUS      RESTARTS   AGE
gpu-feature-discovery-clgss                                   1/1     Running     0          6m10s
gpu-operator-54b8dfbc87-rn9vd                                 1/1     Running     0          6m46s
gpu-operator-node-feature-discovery-master-645bd79495-vkwlh   1/1     Running     0          6m46s
gpu-operator-node-feature-discovery-worker-zzws7              1/1     Running     0          6m46s
nvidia-container-toolkit-daemonset-rq5mz                      1/1     Running     0          6m10s
nvidia-cuda-validator-qv24d                                   0/1     Completed   0          70s
nvidia-dcgm-exporter-tvt4s                                    1/1     Running     0          6m10s
nvidia-device-plugin-daemonset-qxssv                          1/1     Running     0          6m10s
nvidia-device-plugin-validator-k7gvc                          0/1     Completed   0          14s
nvidia-driver-daemonset-nc7w5                                 1/1     Running     0          6m28s
nvidia-operator-validator-9bzrt                               1/1     Running     0          6m10s

The GPU Operator will automatically add "required labels" to the GPU enabled worker nodes.

  • Click on nodes and expand the node that belongs to the "gpu" node group

GPU Node

Recap

As of this step, you have created and applied a "cluster blueprint" with the GPU Operator as one of the addons.

You are now ready to move on to the next step where you will deploy a "GPU Workload" and review the integrated "GPU Dashboards"

Note that you can also reuse this cluster blueprint for as many clusters as you require in this project and also share the blueprint with other projects.