Use Windows GPUs for compute-intensive workloads on Azure Kubernetes Service (AKS) (preview)

Стаття
10/04/2024

Graphical processing units (GPUs) are often used for compute-intensive workloads, such as graphics and visualization workloads. AKS supports GPU-enabled Windows and Linux node pools to run compute-intensive Kubernetes workloads.

This article helps you provision Windows nodes with schedulable GPUs on new and existing AKS clusters (preview).

Supported GPU-enabled virtual machines (VMs)

To view supported GPU-enabled VMs, see GPU-optimized VM sizes in Azure. For AKS node pools, we recommend a minimum size of Standard_NC6s_v3. The NVv4 series (based on AMD GPUs) aren't supported on AKS.

Note

GPU-enabled VMs contain specialized hardware subject to higher pricing and region availability. For more information, see the pricing tool and region availability.

Limitations

Updating an existing Windows node pool to add GPU isn't supported.
Not supported on Kubernetes version 1.28 and below.

Before you begin

This article assumes you have an existing AKS cluster. If you don't have a cluster, create one using the Azure CLI, Azure PowerShell, or the Azure portal.
You need the Azure CLI version 1.0.0b2 or later installed and configured to use the --skip-gpu-driver-install field with the az aks nodepool add command. Run az --version to find the version. If you need to install or upgrade, see Install Azure CLI.
You need the Azure CLI version 9.0.0b5 or later installed and configured to use the --driver-type field with the az aks nodepool add command. Run az --version to find the version. If you need to install or upgrade, see Install Azure CLI.

Get the credentials for your cluster

Get the credentials for your AKS cluster using the az aks get-credentials command. The following example command gets the credentials for the myAKSCluster in the myResourceGroup resource group:
```
az aks get-credentials --resource-group myResourceGroup --name myAKSCluster
```

Using Windows GPU with automatic driver installation

Using NVIDIA GPUs involves the installation of various NVIDIA software components such as the DirectX device plugin for Kubernetes, GPU driver installation, and more. When you create a Windows node pool with a supported GPU-enabled VM, these components and the appropriate NVIDIA CUDA or GRID drivers are installed. For NC and ND series VM sizes, the CUDA driver is installed. For NV series VM sizes, the GRID driver is installed.

Important

AKS preview features are available on a self-service, opt-in basis. Previews are provided "as is" and "as available," and they're excluded from the service-level agreements and limited warranty. AKS previews are partially covered by customer support on a best-effort basis. As such, these features aren't meant for production use. For more information, see the following support articles:

Install the `aks-preview` Azure CLI extension

# Register the aks-preview extension
az extension add --name aks-preview

# Update the aks-preview extension
az extension update --name aks-preview

Register the `WindowsGPUPreview` feature flag

Register the WindowsGPUPreview feature flag using the az feature register command.
```
az feature register --namespace "Microsoft.ContainerService" --name "WindowsGPUPreview"
```
It takes a few minutes for the status to show Registered.

Verify the registration status using the az feature show command.

az feature show --namespace "Microsoft.ContainerService" --name "WindowsGPUPreview"

When the status reflects Registered, refresh the registration of the Microsoft.ContainerService resource provider using the az provider register command.
```
az provider register --namespace Microsoft.ContainerService
```

Create a Windows GPU-enabled node pool (preview)

To create a Windows GPU-enabled node pool, you need to use a supported GPU-enabled VM size and specify the os-type as Windows. The default Windows os-sku is Windows2022, but all Windows os-sku options are supported.

Create a Windows GPU-enabled node pool using the az aks nodepool add command.

az aks nodepool add \
   --resource-group myResourceGroup \
   --cluster-name myAKSCluster \
   --name gpunp \
   --node-count 1 \
   --os-type Windows \
   --kubernetes-version 1.29.0 \
   --node-vm-size Standard_NC6s_v3

Check that your GPUs are schedulable.
Once you confirm that your GPUs are schedulable, you can run your GPU workload.

Specify GPU Driver Type (preview)

By default, AKS specifies a default GPU driver type for each supported GPU-enabled VM. Because workload and driver compatibility are important for functioning GPU workloads, you can specify the driver type for your Windows GPU node. This feature is not supported for Linux GPU node pools.

When creating a Windows agent pool with GPU support, you have the option to specify the type of GPU driver using the --driver-type flag.

The available options are:

GRID: For applications requiring virtualization support.
CUDA: Optimized for computational tasks in scientific computing and data-intensive applications.

Note

When you set the --driver-type flag, you assume responsibility for ensuring that the selected driver type is compatible with the specific VM size and configuration of your node pool. While AKS attempts to validate compatibility, there are scenarios where the node pool creation might fail due to incompatibilities between the specified driver type and the underlying VM or hardware.

To create a Windows GPU-enabled node pool with a specific GPU Driver type, use the az aks nodepool add command.

az aks nodepool add \
    --resource-group myResourceGroup \
    --cluster-name myAKSCluster \
    --name gpunp \
    --node-count 1 \
    --os-type Windows \
    --kubernetes-version 1.29.0 \
    --node-vm-size Standard_NC6s_v3 \
    --driver-type GRID

For example, the above command creates a GPU-enabled node pool using the GRID GPU driver type. Selecting this driver type overrides the default of CUDA driver type for NC series VM skus.

Using Windows GPU with manual driver installation

When creating a Windows node pool with N-series (NVIDIA GPU) VM sizes in AKS, the GPU driver and Kubernetes DirectX device plugin are installed automatically. To bypass this automatic installation, use the following steps:

Skip GPU driver installation (preview) using --skip-gpu-driver-install.
Manual installation of the Kubernetes DirectX device plugin.

Skip GPU driver installation (preview)

AKS has automatic GPU driver installation enabled by default. In some cases, such as installing your own drivers, you may want to skip GPU driver installation.

Important

# Register the aks-preview extension
az extension add --name aks-preview

# Update the aks-preview extension
az extension update --name aks-preview

Create a node pool using the az aks nodepool add command with the --skip-gpu-driver-install flag to skip automatic GPU driver installation.

az aks nodepool add \
    --resource-group myResourceGroup \
    --cluster-name myAKSCluster \
    --name gpunp \
    --node-count 1 \
    --os-type windows \
    --os-sku windows2022 \
    --skip-gpu-driver-install

Note

If the --node-vm-size that you're using isn't yet onboarded on AKS, you can't use GPUs and --skip-gpu-driver-install doesn't work.

Manually install the Kubernetes DirectX device plugin

You can deploy a DaemonSet for the Kubernetes DirectX device plugin, which runs a pod on each node to provide the required drivers for the GPUs.

Add a node pool to your cluster using the az aks nodepool add command.

az aks nodepool add \
    --resource-group myResourceGroup \
    --cluster-name myAKSCluster \
    --name gpunp \
    --node-count 1 \
    --os-type windows \
    --os-sku windows2022

Create a namespace and deploy the Kubernetes DirectX device plugin

Create a namespace using the kubectl create namespace command.
```
kubectl create namespace gpu-resources
```

Create a file named k8s-directx-device-plugin.yaml and paste the following YAML manifest provided as part of the NVIDIA device plugin for Kubernetes project:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nvidia-device-plugin-daemonset
  namespace: gpu-resources
spec:
  selector:
    matchLabels:
      name: nvidia-device-plugin-ds
  updateStrategy:
    type: RollingUpdate
  template:
    metadata:
      # Mark this pod as a critical add-on; when enabled, the critical add-on scheduler
      # reserves resources for critical add-on pods so that they can be rescheduled after
      # a failure.  This annotation works in tandem with the toleration below.
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ""
      labels:
        name: nvidia-device-plugin-ds
    spec:
      tolerations:
      # Allow this pod to be rescheduled while the node is in "critical add-ons only" mode.
      # This, along with the annotation above marks this pod as a critical add-on.
      - key: CriticalAddonsOnly
        operator: Exists
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      - key: "sku"
        operator: "Equal"
        value: "gpu"
        effect: "NoSchedule"
      containers:
      - image: mcr.microsoft.com/oss/nvidia/k8s-device-plugin:v0.14.1
        name: nvidia-device-plugin-ctr
        securityContext:
          allowPrivilegeEscalation: false
          capabilities:
            drop: ["ALL"]
        volumeMounts:
          - name: device-plugin
            mountPath: /var/lib/kubelet/device-plugins
      volumes:
        - name: device-plugin
          hostPath:
            path: /var/lib/kubelet/device-plugins

Create the DaemonSet and confirm the NVIDIA device plugin is created successfully using the kubectl apply command.
```
kubectl apply -f nvidia-device-plugin-ds.yaml
```
Now that you successfully installed the NVIDIA device plugin, you can check that your GPUs are schedulable.

Confirm that GPUs are schedulable

After creating your cluster, confirm that GPUs are schedulable in Kubernetes.

List the nodes in your cluster using the kubectl get nodes command.
```
kubectl get nodes
```
Your output should look similar to the following example output:
```
NAME                   STATUS   ROLES   AGE   VERSION
aks-gpunp-28993262-0   Ready    agent   13m   v1.20.7
```
Confirm the GPUs are schedulable using the kubectl describe node command.
```
kubectl describe node aks-gpunp-28993262-0
```
Under the Capacity section, the GPU should list as microsoft.com/directx: 1. Your output should look similar to the following condensed example output:
```
Capacity:
[...]
 microsoft.com.directx/gpu:                 1
[...]
```

Use Container Insights to monitor GPU usage

Container Insights with AKS monitors the following GPU usage metrics:

Metric name	Metric dimension (tags)	Description
containerGpuDutyCycle	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `containerName`, `gpuId`, `gpuModel`, `gpuVendor`	Percentage of time over the past sample period (60 seconds) during which GPU was busy/actively processing for a container. Duty cycle is a number between 1 and 100.
containerGpuLimits	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `containerName`	Each container can specify limits as one or more GPUs. It's not possible to request or limit a fraction of a GPU.
containerGpuRequests	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `containerName`	Each container can request one or more GPUs. It's not possible to request or limit a fraction of a GPU.
containerGpumemoryTotalBytes	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `containerName`, `gpuId`, `gpuModel`, `gpuVendor`	Amount of GPU Memory in bytes available to use for a specific container.
containerGpumemoryUsedBytes	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `containerName`, `gpuId`, `gpuModel`, `gpuVendor`	Amount of GPU Memory in bytes used by a specific container.
nodeGpuAllocatable	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `gpuVendor`	Number of GPUs in a node that Kubernetes can use.
nodeGpuCapacity	`container.azm.ms/clusterId`, `container.azm.ms/clusterName`, `gpuVendor`	Total Number of GPUs in a node.

Clean up resources

Remove the associated Kubernetes objects you created in this article using the kubectl delete job command.
```
kubectl delete jobs windows-gpu-workload
```

Next steps

To run Apache Spark jobs, see Run Apache Spark jobs on AKS.
For more information on features of the Kubernetes scheduler, see Best practices for advanced scheduler features in AKS.
For more information on Azure Kubernetes Service and Azure Machine Learning, see:

Поділитися через

Use Windows GPUs for compute-intensive workloads on Azure Kubernetes Service (AKS) (preview)

Supported GPU-enabled virtual machines (VMs)

Limitations

Before you begin

Get the credentials for your cluster

Using Windows GPU with automatic driver installation

Install the `aks-preview` Azure CLI extension

Register the `WindowsGPUPreview` feature flag

Create a Windows GPU-enabled node pool (preview)

Specify GPU Driver Type (preview)

Using Windows GPU with manual driver installation

Skip GPU driver installation (preview)

Manually install the Kubernetes DirectX device plugin

Create a namespace and deploy the Kubernetes DirectX device plugin

Confirm that GPUs are schedulable

Use Container Insights to monitor GPU usage

Clean up resources

Next steps

Додаткові ресурси

Поділитися через

Use Windows GPUs for compute-intensive workloads on Azure Kubernetes Service (AKS) (preview)

Supported GPU-enabled virtual machines (VMs)

Limitations

Before you begin

Get the credentials for your cluster

Using Windows GPU with automatic driver installation

Install the aks-preview Azure CLI extension

Register the WindowsGPUPreview feature flag

Create a Windows GPU-enabled node pool (preview)

Specify GPU Driver Type (preview)

Using Windows GPU with manual driver installation

Skip GPU driver installation (preview)

Manually install the Kubernetes DirectX device plugin

Create a namespace and deploy the Kubernetes DirectX device plugin

Confirm that GPUs are schedulable

Use Container Insights to monitor GPU usage

Clean up resources

Next steps

Додаткові ресурси

Install the `aks-preview` Azure CLI extension

Register the `WindowsGPUPreview` feature flag