# 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](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/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. ```yaml 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" ## 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 ```yaml 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" ## 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. ```yaml 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." ## 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" ```yaml 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** ### 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. ```yaml 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 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 ``` ## 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 ## 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.