Ricardo Martins

ARO with Nvidia GPU Workloads

This article was originally published at ARO with Nvidia GPU Workloads | Red Hat Cloud Experts

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:

Bash
sudo dnf install jq moreutils gettext

MacOS:

Bash
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

Bash
helm repo add mobb https://rh-mobb.github.io/helm-charts/

2. Update your repositories

Bash
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.com , 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

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.

Bash
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 values file for configuration options

Bash
helm upgrade --install -n openshift-machine-api \
    gpu mobb/aro-gpu

2. Switch to the proper namespace (project):

Bash
oc project openshift-machine-api

3. Wait for the new GPU nodes to be available

Bash
watch oc -n openshift-machine-api get machines
Bash
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 setsFor 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.

Bash
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

Bash
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.

Bash
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

Bash
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

Bash
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

Bash
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.

Bash
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

Bash
jq '.spec.template.spec.providerSpec.value.zone = "1"' gpu_machineset.json | sponge gpu_machineset.json

9. Delete the .status section of the yaml file

Bash
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 portal 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

Bash
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

Bash
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.

Bash
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

Bash
oc create namespace openshift-nfd
oc create namespace nvidia-gpu-operator

2. Use the mobb/operatorhub chart to deploy the needed operators

Bash
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.

Bash
oc wait --for=jsonpath='{.status.replicas}'=1 deployment \
  nfd-controller-manager -n openshift-nfd --timeout=600s
Bash
oc wait --for=jsonpath='{.status.replicas}'=1 deployment \
  gpu-operator -n nvidia-gpu-operator --timeout=600s

4. Install the Nvidia GPU Operator chart

Bash
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

Bash
cat <<EOF | oc apply -f -
apiVersion: v1
kind: Namespace
metadata:
  name: nvidia-gpu-operator
EOF

2. Create Operator Group

Bash
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

Bash
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 guidance .

1. Get latest nvidia package

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

2. Create Subscription

Bash
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.

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

Bash
cat <<EOF | oc apply -f -
apiVersion: v1
kind: Namespace
metadata:
  name: openshift-nfd
EOF

2. Create OperatorGroup

Bash
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

Bash
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

Bash
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

Apply nVidia Cluster Config

We’ll now apply the nvidia cluster config. Please read the nvidia documentation 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

Bash
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.

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)

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

You should see output like:

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

2. Verify node labels

Bash
oc get node -l nvidia.com/gpu.present
Bash
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.

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

4. Nvidia SMI tool verification

Bash
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)

5. Create Pod to run a GPU workload

Bash
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

Bash
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):

Bash
[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

Bash
oc delete pod cuda-vector-add

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