Manage GPU devices with dynamic resource allocation

Dynamic resource allocation is a Kubernetes API that lets you request and share resources, like GPUs, across Pods and containers in your Google Distributed Cloud bare metal clusters. Third-party drivers manage these devices, and Kubernetes allocates them dynamically, much like storage volumes. Your AI workloads don't need to target specific nodes or request the exact number of attached devices. This document shows you how to configure your GPU workloads to use dynamic resource allocation.

Before you begin

Before you configure your GPU workloads to use dynamic resource allocation, verify that the following prerequisites are met:

  • Your bare metal cluster is at version 1.33.0 or later.
  • Your operating system is either Ubuntu 22.04 or Red Hat Enterprise Linux (RHEL) 9.4.
  • You have updated your cluster to enable dynamic resource allocation as described in Enable dynamic resource allocation.
  • You have at least one node machine with a GPU attached and the NVIDIA GPU driver installed. For more information, see Install or uninstall the bundled NVIDIA GPU Operator.
  • If you are using the bundled NVIDIA GPU operator, version 1.33.0 or later enables Container Device Interface (CDI) by default. If you are using a manually installed GPU operator, ensure CDI is enabled in the ClusterPolicy.
  • You have followed the instructions in NVIDIA DRA Driver for GPUs to install the NVIDIA DRA driver on all GPU-attached nodes.

Create GPU workloads that use dynamic resource allocation

For your GPU workloads to take advantage of dynamic resource allocation to request GPUs, they must be in a shared namespace with a ResourceClaim that describes the request for GPU device allocation. Your workloads must reference the ResourceClaim for Kubernetes to assign GPU resources.

The following steps set up an environment in which your workloads use dynamic resource allocation to request GPU resources:

  1. To create resources related to dynamic resource allocation, create a new Namespace in your cluster:

    cat <<EOF | kubectl apply --kubeconfig=CLUSTER_KUBECONFIG -f -
apiVersion: v1
kind: Namespace
metadata:
  name: NAMESPACE_NAME
EOF

    Replace the following:

    • CLUSTER_KUBECONFIG: the path of the user cluster kubeconfig file.

    • NAMESPACE_NAME with the name for your dynamic resource allocation namespace.

  2. Create a ResourceClaim to describe the request for GPU access:

    cat <<EOF | kubectl apply --kubeconfig=CLUSTER_KUBECONFIG -f -
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaim
metadata:
  namespace: NAMESPACE_NAME
  name: RESOURCE_CLAIM_NAME
spec:
    devices:
      requests:
      - name: gpu
        deviceClassName: gpu.nvidia.com
EOF

    Replace RESOURCE_CLAIM_NAME with the name of your resource claim for GPU requests.

  3. Create workloads that reference the ResourceClaim created in the preceding step.

    The following workload examples show how to reference a ResourceClaim named gpu-claim in the dra-test namespace. The containers in the pod1 Pod are NVIDIA compute unified device architecture (CUDA) samples designed to run CUDA workloads on the GPUs. When the pod1 Pod completes successfully, it indicates that the dynamic resource allocation capability is working properly and dynamic resource allocation is ready to manage GPU resources in your cluster.

    Ubuntu

    1. Use the following command to apply the manifest to your cluster:

      cat <<EOF | kubectl apply --kubeconfig=CLUSTER_KUBECONFIG -f -
apiVersion: v1
kind: Pod
metadata:
  name: pod1
  namespace: dra-test
spec:
  restartPolicy: OnFailure
  resourceClaims:
    - name: gpu
      resourceClaimName: gpu-claim
  containers:
    - name: ctr0
      image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0
      resources:
        claims:
          - name: gpu
    - name: ctr1
      image: nvcr.io/nvidia/k8s/cuda-sample:devicequery
      resources:
        claims:
          - name: gpu
EOF

    RHEL

    1. Use the following command to apply the manifest to your cluster:

      cat <<EOF | kubectl apply --kubeconfig=CLUSTER_KUBECONFIG -f -
apiVersion: v1
kind: Pod
metadata:
  name: pod1
  namespace: dra-test
spec:
  restartPolicy: OnFailure

  resourceClaims:
    - name: gpu
      resourceClaimName: gpu-claim
  containers:
    - name: ctr0
      image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0
      resources:
        claims:
          - name: gpu
    - name: ctr1
      image: nvcr.io/nvidia/k8s/cuda-sample:devicequery
      resources:
        claims:
          - name: gpu
EOF

Limitations

Consider the following limitations when you use dynamic resource allocation:

  • When you use RHEL OS with a supported version of the NVIDIA GPU operator, the Container Device Interface (CDI) handles device injection and SELinux labeling automatically. You aren't required to use manual SELinux policy configuration or set pod securityContext.

  • This feature uses the resource.k8s.io/v1beta1 API group, which differs from the open source Kubernetes API group for this feature, resource.k8s.io/v1. The v1 open source API group provides more features and better stability than the v1beta1 API group.

What's next