Using NVIDIA GPU Operator with EKS Anywhere

How to use the NVIDIA GPU Operator with EKS Anywhere on bare metal

The NVIDIA GPU Operator allows GPUs to be exposed to applications in Kubernetes clusters much like CPUs. Instead of provisioning a special OS image for GPU nodes with the required drivers and dependencies, a standard OS image can be used for both CPU and GPU nodes. The NVIDIA GPU Operator can be used to provision the required software components for GPUs such as the NVIDIA drivers, Kubernetes device plugin for GPUs, and the NVIDIA Container Toolkit. See the licensing section of the NVIDIA GPU Operator documentation for information on the NVIDIA End User License Agreements.

In the example on this page, a single-node EKS Anywhere cluster on bare metal is used with an Ubuntu 20.04 image produced from image-builder without modifications and Kubernetes version 1.27.

1. Configure an EKS Anywhere cluster spec and hardware inventory

See the Configure for Bare Metal page and the Prepare hardware inventory page for details. If you use cluster spec sample below is used, your hardware inventory definition must have type=cp for the labels field in the hardware inventory for your server.

Expand for a sample cluster spec

apiVersion: anywhere.eks.amazonaws.com/v1alpha1
kind: Cluster
metadata:
  name: gpu-test
spec:
  clusterNetwork:
    cniConfig:
      cilium: {}
    pods:
      cidrBlocks:
      - 192.168.0.0/16
    services:
      cidrBlocks:
      - 10.96.0.0/12
  controlPlaneConfiguration:
    count: 1
    endpoint:
      host: "<my-cp-ip>"
    machineGroupRef:
      kind: TinkerbellMachineConfig
      name: gpu-test-cp
  datacenterRef:
    kind: TinkerbellDatacenterConfig
    name: gpu-test
  kubernetesVersion: "1.27"
---
apiVersion: anywhere.eks.amazonaws.com/v1alpha1
kind: TinkerbellDatacenterConfig
metadata:
  name: gpu-test
spec:
  tinkerbellIP: "<my-tb-ip>"
  osImageURL: "https://<url-for-image>/ubuntu.gz"
---
apiVersion: anywhere.eks.amazonaws.com/v1alpha1
kind: TinkerbellMachineConfig
metadata:
  name: gpu-test-cp
spec:
  hardwareSelector: {type: "cp"}
  osFamily: ubuntu
  templateRef: {}

2. Create a single-node EKS Anywhere cluster

Replace hardware.csv with the name of your hardware inventory file
Replace cluster.yaml with the name of your cluster spec file

eksctl anywhere create cluster --hardware hardware.csv -f cluster.yaml

Expand for sample output

Warning: The recommended number of control plane nodes is 3 or 5
Warning: No configurations provided for worker node groups, pods will be scheduled on control-plane nodes
Performing setup and validations
Private key saved to gpu-test/eks-a-id_rsa. Use 'ssh -i gpu-test/eks-a-id_rsa <username>@<Node-IP-Address>' to login to your cluster node
✅ Tinkerbell Provider setup is valid
✅ Validate OS is compatible with registry mirror configuration
✅ Validate certificate for registry mirror
✅ Validate authentication for git provider
Creating new bootstrap cluster
Provider specific pre-capi-install-setup on bootstrap cluster
Installing cluster-api providers on bootstrap cluster
Provider specific post-setup
Creating new workload cluster
Installing networking on workload cluster
Creating EKS-A namespace
Installing cluster-api providers on workload cluster
Installing EKS-A secrets on workload cluster
Installing resources on management cluster
Moving cluster management from bootstrap to workload cluster
Installing EKS-A custom components (CRD and controller) on workload cluster
Installing EKS-D components on workload cluster
Creating EKS-A CRDs instances on workload cluster
Installing GitOps Toolkit on workload cluster
GitOps field not specified, bootstrap flux skipped
Writing cluster config file
Deleting bootstrap cluster
🎉 Cluster created!

3. Install Helm

curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 \ 
  && chmod 700 get_helm.sh \ 
  && ./get_helm.sh

4. Add NVIDIA Helm Repository

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \ 
  && helm repo update

5. Configure kubectl to use EKS Anywhere cluster

Replace <path-to-cluster-folder> with the directory location where your EKS Anywhere cluster folder is located. This is typically in the same directory in which the eksctl anywhere command was run.
Replace <cluster-name> with the name of your cluster.

KUBECONFIG=<path-to-cluster-folder>/<cluster-name>-eks-a-cluster.kubeconfig

6. Install NVIDIA GPU Operator

helm install --wait --generate-name \ 
  -n gpu-operator --create-namespace \ 
  nvidia/gpu-operator

7. Validate the operator was installed successfully

kubectl get pods -n gpu-operator

NAME                                                              READY   STATUS      RESTARTS   AGE
gpu-feature-discovery-6djnw                                       1/1     Running     0          5m25s
gpu-operator-1691443998-node-feature-discovery-master-55cfkzbl5   1/1     Running     0          5m55s
gpu-operator-1691443998-node-feature-discovery-worker-dw8m7       1/1     Running     0          5m55s
gpu-operator-59f96d7646-7zcn4                                     1/1     Running     0          5m55s
nvidia-container-toolkit-daemonset-c2mdf                          1/1     Running     0          5m25s
nvidia-cuda-validator-6m4kg                                       0/1     Completed   0          3m41s
nvidia-dcgm-exporter-jw5wz                                        1/1     Running     0          5m25s
nvidia-device-plugin-daemonset-8vjrn                              1/1     Running     0          5m25s
nvidia-driver-daemonset-6hklg                                     1/1     Running     0          5m36s
nvidia-operator-validator-2pvzx                                   1/1     Running     0          5m25s

8. Validate GPU specs

kubectl get node -o json | jq '.items[].metadata.labels'

{
... 
  "nvidia.com/cuda.driver.major": "535",
  "nvidia.com/cuda.driver.minor": "86",
  "nvidia.com/cuda.driver.rev": "10",
  "nvidia.com/cuda.runtime.major": "12",
  "nvidia.com/cuda.runtime.minor": "2",
  "nvidia.com/gfd.timestamp": "1691444179",
  "nvidia.com/gpu-driver-upgrade-state": "upgrade-done",
  "nvidia.com/gpu.compute.major": "7",
  "nvidia.com/gpu.compute.minor": "5",
  "nvidia.com/gpu.count": "2",
  "nvidia.com/gpu.deploy.container-toolkit": "true",
  "nvidia.com/gpu.deploy.dcgm": "true",
  "nvidia.com/gpu.deploy.dcgm-exporter": "true",
  "nvidia.com/gpu.deploy.device-plugin": "true",
  "nvidia.com/gpu.deploy.driver": "true",
  "nvidia.com/gpu.deploy.gpu-feature-discovery": "true",
  "nvidia.com/gpu.deploy.node-status-exporter": "true",
  "nvidia.com/gpu.deploy.nvsm": "",
  "nvidia.com/gpu.deploy.operator-validator": "true",
  "nvidia.com/gpu.family": "turing",
  "nvidia.com/gpu.machine": "PowerEdge-R7525",
  "nvidia.com/gpu.memory": "15360",
  "nvidia.com/gpu.present": "true",
  "nvidia.com/gpu.product": "Tesla-T4",
  "nvidia.com/gpu.replicas": "1",
  "nvidia.com/mig.capable": "false",
  "nvidia.com/mig.strategy": "single"
}

9. Run Sample App

Create a gpu-pod.yaml file with the following and apply it to the cluster

apiVersion: v1 
kind: Pod 
metadata: 
  name: gpu-pod 
spec: 
  restartPolicy: Never 
  containers: 
   - name: cuda-container 
     image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2 
     resources: 
       limits: 
         nvidia.com/gpu: 1 # requesting 1 GPU 
  tolerations: 
    - key: nvidia.com/gpu operator: Exists 
      effect: NoSchedule

kubectl apply -f gpu-pod.yaml

10. Confirm Sample App Succeeded

kubectl logs gpu-pod

[Vector addition of 50000 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