Cloud Experts Documentation

ARO with Nvidia GPU Workloads

This content is authored by Red Hat experts, but has not yet been tested on every supported configuration.

ARO guide to running Nvidia GPU workloads.

Prerequisites

Note: If you need to install an ARO cluster, please read our ARO Terraform Install Guide . Please be sure if you’re installing or using an existing ARO cluster that it is 4.14.x or higher.

Note: Please ensure your ARO cluster was created with a valid pull secret (to verify make sure you can see the Operator Hub in the cluster’s console). If not, you can follow these instructions.

Linux:

sudo dnf install jq moreutils gettext

MacOS:

brew install jq moreutils gettext helm openshift-cli

Helm Prerequisites

If you plan to use Helm to deploy the GPU operator, you will need do the following

  1. Add the MOBB chart repository to your Helm

    helm repo add mobb https://rh-mobb.github.io/helm-charts/
    
  2. Update your repositories

    helm repo update
    

GPU Quota

All GPU quotas in Azure are 0 by default. You will need to login to the azure portal and request GPU quota. There is a lot of competition for GPU workers, so you may have to provision an ARO cluster in a region where you can actually reserve GPU.

ARO supports the following GPU workers:

  • NC4as T4 v3
  • NC6s v3
  • NC8as T4 v3
  • NC12s v3
  • NC16as T4 v3
  • NC24s v3
  • NC24rs v3
  • NC64as T4 v3

Please remember that when you request quota that Azure is per core. To request a single NC4as T4 v3 node, you will need to request quota in groups of 4. If you wish to request an NC16as T4 v3 you will need to request quota of 16.

  1. Login to azure

    Login to portal.azure.comexternal link (opens in new tab) , type “quotas” in search by, click on Compute and in the search box type “NCAsv3_T4”. Select the region your cluster is in (select checkbox) and then click Request quota increase and ask for quota (I chose 8 so i can build two demo clusters of NC4as T4s). The Helm chart we use below will request a single Standard_NC4as_T4_v3 machine.

  2. Configure quota

    GPU Quota Request on Azure

Log in to your ARO cluster

  1. Login to OpenShift - we’ll use the kubeadmin account here but you can login with your user account as long as you have cluster-admin.

    oc login <apiserver> -u kubeadmin -p <kubeadminpass>
    

GPU Machine Set

ARO still uses Kubernetes Machinsets to create a machine set. I’m going to export the first machine set in my cluster (az 1) and use that as a template to build a single GPU machine in southcentralus region 1.

You can create the machine set the easy way using Helm, or Manually. We recommend using the Helm chart method.

Option 1 - Helm

  1. Create a new machine-set (replicas of 1), see the Chart’s valuesexternal link (opens in new tab) file for configuration options

    helm upgrade --install -n openshift-machine-api \
        gpu mobb/aro-gpu
    
  2. Switch to the proper namespace (project):

    oc project openshift-machine-api
    
  3. Wait for the new GPU nodes to be available

    watch oc -n openshift-machine-api get machines
    
    NAME                                                  PHASE     TYPE                   REGION           ZONE   AGE
    xxxx-gpu-aro-gpu-southcentralus-9wnzz   Running   Standard_NC4as_T4_v3   southcentralus   1      11m
    xxxx-master-0                           Running   Standard_D8s_v3        southcentralus   1      64m
    ...
    ...
    
  4. Skip past Option 2 - Manually to Install Nvidia GPU Operator

Option 2 - Manually

  1. View existing machine sets

    For ease of set up, I’m going to grab the first machine set and use that as the one I will clone to create our GPU machine set.

    MACHINESET=$(oc get machineset -n openshift-machine-api -o=jsonpath='{.items[0]}' | jq -r '[.metadata.name] | @tsv')
    
  2. Save a copy of example machine set

    oc get machineset -n openshift-machine-api $MACHINESET -o json > gpu_machineset.json
    
  3. Change the .metadata.name field to a new unique name

    I’m going to create a unique name for this single node machine set that shows nvidia-worker- that follows a similar pattern as all the other machine sets.

    jq '.metadata.name = "nvidia-worker-southcentralus1"' gpu_machineset.json| sponge gpu_machineset.json
    
  4. Ensure spec.replicas matches the desired replica count for the MachineSet

    jq '.spec.replicas = 1' gpu_machineset.json| sponge gpu_machineset.json
    
  5. Change the .spec.selector.matchLabels.machine.openshift.io/cluster-api-machineset field to match the .metadata.name field

    jq '.spec.selector.matchLabels."machine.openshift.io/cluster-api-machineset" = "nvidia-worker-southcentralus1"' gpu_machineset.json| sponge gpu_machineset.json
    
  6. Change the .spec.template.metadata.labels.machine.openshift.io/cluster-api-machineset to match the .metadata.name field

    jq '.spec.template.metadata.labels."machine.openshift.io/cluster-api-machineset" = "nvidia-worker-southcentralus1"' gpu_machineset.json| sponge gpu_machineset.json
    
  7. Change the spec.template.spec.providerSpec.value.vmSize to match the desired GPU instance type from Azure.

    The machine we’re using is Standard_NC4as_T4_v3.

    jq '.spec.template.spec.providerSpec.value.vmSize = "Standard_NC4as_T4_v3"' gpu_machineset.json | sponge gpu_machineset.json
    
  8. Change the spec.template.spec.providerSpec.value.zone to match the desired zone from Azure

    jq '.spec.template.spec.providerSpec.value.zone = "1"' gpu_machineset.json | sponge gpu_machineset.json
    
  9. Delete the .status section of the yaml file

    jq 'del(.status)' gpu_machineset.json | sponge gpu_machineset.json
    
  10. Verify the other data in the yaml file.

Create GPU machine set

These steps will create the new GPU machine. It may take 10-15 minutes to provision a new GPU machine. If this step fails, please login to the azure portalexternal link (opens in new tab) and ensure you didn’t run across availability issues. You can go “Virtual Machines” and search for the worker name you created above to see the status of VMs.

  1. Create GPU Machine set

    oc create -f gpu_machineset.json
    

    This command will take a few minutes to complete.

  2. Verify GPU machine set

    Machines should be getting deployed. You can view the status of the machine set with the following commands

    oc get machineset -n openshift-machine-api
    oc get machine -n openshift-machine-api
    

    Once the machines are provisioned, which could take 5-15 minutes, machines will show as nodes in the node list.

    oc get nodes
    

    You should see a node with the “nvidia-worker-southcentralus1” name it we created earlier.

Install Nvidia GPU Operator

This will create the nvidia-gpu-operator name space, set up the operator group and install the Nvidia GPU Operator.

Like ealier you can do it the easy way with Helm, or the hard way by doing it manually.

Option 1 - Helm

  1. Create namespaces

    oc create namespace openshift-nfd
    oc create namespace nvidia-gpu-operator
    
  2. Use the mobb/operatorhub chart to deploy the needed operators

    helm upgrade -n nvidia-gpu-operator nvidia-gpu-operator \
      mobb/operatorhub --install \
      --values https://raw.githubusercontent.com/rh-mobb/helm-charts/main/charts/nvidia-gpu/files/operatorhub.yaml
    
  3. Wait until the two operators are running

    Note: If you see an error like Error from server (NotFound): deployments.apps “nfd-controller-manager” not found, wait a few minutes and try again.

    oc wait --for=jsonpath='{.status.replicas}'=1 deployment \
      nfd-controller-manager -n openshift-nfd --timeout=600s
    
    oc wait --for=jsonpath='{.status.replicas}'=1 deployment \
      gpu-operator -n nvidia-gpu-operator --timeout=600s
    
  4. Install the Nvidia GPU Operator chart

    helm upgrade --install -n nvidia-gpu-operator nvidia-gpu \
      mobb/nvidia-gpu --disable-openapi-validation
    
  5. Skip past Option 2 - Manually to Validate GPU

Option 2 - Manually

  1. Create Nvidia namespace

    cat <<EOF | oc apply -f -
    apiVersion: v1
    kind: Namespace
    metadata:
      name: nvidia-gpu-operator
    EOF
    
  2. Create Operator Group

    cat <<EOF | oc apply -f -
    apiVersion: operators.coreos.com/v1
    kind: OperatorGroup
    metadata:
      name: nvidia-gpu-operator-group
      namespace: nvidia-gpu-operator
    spec:
      targetNamespaces:
      - nvidia-gpu-operator
    EOF
    
  3. Get latest nvidia channel

    CHANNEL=$(oc get packagemanifest gpu-operator-certified -n openshift-marketplace -o jsonpath='{.status.defaultChannel}')
    

If your cluster was created without providing the pull secret, the cluster won’t include samples or operators from Red Hat or from certified partners. This will result in the following error message:

Error from server (NotFound): packagemanifests.packages.operators.coreos.com “gpu-operator-certified” not found.

To add your Red Hat pull secret on an Azure Red Hat OpenShift cluster, follow this guidanceexternal link (opens in new tab) .

  1. Get latest nvidia package

    PACKAGE=$(oc get packagemanifests/gpu-operator-certified -n openshift-marketplace -ojson | jq -r '.status.channels[] | select(.name == "'$CHANNEL'") | .currentCSV')
    
  2. Create Subscription

    envsubst  <<EOF | oc apply -f -
    apiVersion: operators.coreos.com/v1alpha1
    kind: Subscription
    metadata:
      name: gpu-operator-certified
      namespace: nvidia-gpu-operator
    spec:
      channel: "$CHANNEL"
      installPlanApproval: Automatic
      name: gpu-operator-certified
      source: certified-operators
      sourceNamespace: openshift-marketplace
      startingCSV: "$PACKAGE"
    EOF
    
  3. Wait for Operator to finish installing

    Don’t proceed until you have verified that the operator has finished installing. It’s also a good point to ensure that your GPU worker is online.

    Verify Operator

Install Node Feature Discovery Operator

The node feature discovery operator will discover the GPU on your nodes and appropriately label the nodes so you can target them for workloads. We’ll install the NFD operator into the opneshift-ndf namespace and create the “subscription” which is the configuration for NFD.

Official Documentation for Installing Node Feature Discovery Operator

  1. Set up Name Space

    cat <<EOF | oc apply -f -
    apiVersion: v1
    kind: Namespace
    metadata:
      name: openshift-nfd
    EOF
    
  2. Create OperatorGroup

    cat <<EOF | oc apply -f -
    apiVersion: operators.coreos.com/v1
    kind: OperatorGroup
    metadata:
      generateName: openshift-nfd-
      name: openshift-nfd
      namespace: openshift-nfd
    EOF
    
  3. Create Subscription

    cat <<EOF | oc apply -f -
    apiVersion: operators.coreos.com/v1alpha1
    kind: Subscription
    metadata:
      name: nfd
      namespace: openshift-nfd
    spec:
      channel: "stable"
      installPlanApproval: Automatic
      name: nfd
      source: redhat-operators
      sourceNamespace: openshift-marketplace
    EOF
    
  4. Wait for Node Feature discovery to complete installation

    You can login to your openshift console and view operators or simply wait a few minutes. The next step will error until the operator has finished installing.

  5. Create NFD Instance

    cat <<EOF | oc apply -f -
    kind: NodeFeatureDiscovery
    apiVersion: nfd.openshift.io/v1
    metadata:
      name: nfd-instance
      namespace: openshift-nfd
    spec:
      customConfig:
        configData: {}
      operand:
        image: >-
          registry.redhat.io/openshift4/ose-node-feature-discovery@sha256:07658ef3df4b264b02396e67af813a52ba416b47ab6e1d2d08025a350ccd2b7b      
        servicePort: 12000
      workerConfig:
        configData: |
          core:
            sleepInterval: 60s
          sources:
            pci:
              deviceClassWhitelist:
                - "0200"
                - "03"
                - "12"
              deviceLabelFields:
                - "vendor"      
    EOF
    
  6. Verify NFD is ready.

    This operator should say Available in the status

    NFD Operator Ready

Apply nVidia Cluster Config

We’ll now apply the nvidia cluster config. Please read the nvidia documentationexternal link (opens in new tab) on customizing this if you have your own private repos or specific settings. This will be another process that takes a few minutes to complete.

  1. Apply cluster config

    cat <<EOF | oc apply -f -
    apiVersion: nvidia.com/v1
    kind: ClusterPolicy
    metadata:
      name: gpu-cluster-policy
    spec:
      migManager:
        enabled: true
      operator:
        defaultRuntime: crio
        initContainer: {}
        runtimeClass: nvidia
        deployGFD: true
      dcgm:
        enabled: true
      gfd: {}
      dcgmExporter:
        config:
          name: ''
      driver:
        licensingConfig:
          nlsEnabled: false
          configMapName: ''
        certConfig:
          name: ''
        kernelModuleConfig:
          name: ''
        repoConfig:
          configMapName: ''
        virtualTopology:
          config: ''
        enabled: true
        use_ocp_driver_toolkit: true
      devicePlugin: {}
      mig:
        strategy: single
      validator:
        plugin:
          env:
            - name: WITH_WORKLOAD
              value: 'true'
      nodeStatusExporter:
        enabled: true
      daemonsets: {}
      toolkit:
        enabled: true
    EOF
    
  2. Verify Cluster Policy

    Login to OpenShift console and browse to operators and make sure you’re in nvidia-gpu-operator namespace. You should see it say State: Ready once everything is complete.

    cluster policy

Validate GPU

It may take some time for the nVidia Operator and NFD to completely install and self-identify the machines. These commands can be ran to help validate that everything is running as expected.

  1. Verify NFD can see your GPU(s)

    oc describe node | egrep 'Roles|pci-10de' | grep -v master
    

    You should see output like:

    Roles:              worker
                    feature.node.kubernetes.io/pci-10de.present=true
    
  2. Verify node labels

    oc get node -l nvidia.com/gpu.present
    
    NAME                                                  STATUS   ROLES    AGE   VERSION
    xxxxx-gpu-aro-gpu-southcentralus-9wnzz   Ready    worker   14m   v1.27.10+c79e5e2
    
  3. Wait until Cluster Policy is ready

    Note: This step may take a few minutes to complete.

    oc wait --for=jsonpath='{.status.state}'=ready clusterpolicy \
      gpu-cluster-policy -n nvidia-gpu-operator --timeout=600s
    
  4. Nvidia SMI tool verification

    oc project nvidia-gpu-operator
    for i in $(oc get pod -lopenshift.driver-toolkit=true --no-headers |awk '{print $1}'); do echo $i; oc exec -it $i -- nvidia-smi ; echo -e '\n' ;  done
    

    You should see output that shows the GPUs available on the host such as this example screenshot. (Varies depending on GPU worker type)

    Nvidia SMI
  5. Create Pod to run a GPU workload

    oc project nvidia-gpu-operator
    cat <<EOF | oc apply -f -
    apiVersion: v1
    kind: Pod
    metadata:
      name: cuda-vector-add
    spec:
      restartPolicy: OnFailure
      containers:
        - name: cuda-vector-add
          image: "quay.io/giantswarm/nvidia-gpu-demo:latest"
          resources:
            limits:
              nvidia.com/gpu: 1
          nodeSelector:
            nvidia.com/gpu.present: true
    EOF
    
  6. View logs

    oc logs cuda-vector-add --tail=-1
    

    Please note, if you get an error “Error from server (BadRequest): container “cuda-vector-add” in pod “cuda-vector-add” is waiting to start: ContainerCreating”, try running “oc delete pod cuda-vector-add” and then re-run the create statement above. I’ve seen issues where if this step is ran before all of the operator consolidation is done it may just sit there.

    You should see Output like the following (mary vary depending on GPU):

    [Vector addition of 5000 elements]
    Copy input data from the host memory to the CUDA device
    CUDA kernel launch with 196 blocks of 256 threads
    Copy output data from the CUDA device to the host memory
    Test PASSED
    Done
    
  7. If successful, the pod can be deleted

    oc delete pod cuda-vector-add
    

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