Create a multi-instance GPU node pool in Azure Kubernetes Service (AKS)

Стаття
08/01/2024

Nvidia's A100 GPU can be divided in up to seven independent instances. Each instance has its own memory and Stream Multiprocessor (SM). For more information on the Nvidia A100, see Nvidia A100 GPU.

This article walks you through how to create a multi-instance GPU node pool in an Azure Kubernetes Service (AKS) cluster.

Prerequisites and limitations

An Azure account with an active subscription. If you don't have one, you can create an account for free.
Azure CLI version 2.2.0 or later installed and configured. Run az --version to find the version. If you need to install or upgrade, see Install Azure CLI.
The Kubernetes command-line client, kubectl, installed and configured. If you use Azure Cloud Shell, kubectl is already installed. If you want to install it locally, you can use the az aks install-cli command.
Helm v3 installed and configured. For more information, see Installing Helm.
You can't use Cluster Autoscaler with multi-instance node pools.

GPU instance profiles

GPU instance profiles define how GPUs are partitioned. The following table shows the available GPU instance profile for the Standard_ND96asr_v4:

Profile name	Fraction of SM	Fraction of memory	Number of instances created
MIG 1g.5gb	1/7	1/8	7
MIG 2g.10gb	2/7	2/8	3
MIG 3g.20gb	3/7	4/8	2
MIG 4g.20gb	4/7	4/8	1
MIG 7g.40gb	7/7	8/8	1

As an example, the GPU instance profile of MIG 1g.5gb indicates that each GPU instance has 1g SM(Computing resource) and 5gb memory. In this case, the GPU is partitioned into seven instances.

The available GPU instance profiles available for this instance size include MIG1g, MIG2g, MIG3g, MIG4g, and MIG7g.

Important

You can't change the applied GPU instance profile after node pool creation.

Create an AKS cluster

Create an Azure resource group using the az group create command.

az group create --name myResourceGroup --location southcentralus

Create an AKS cluster using the az aks create command.

az aks create \
    --resource-group myResourceGroup \
    --name myAKSCluster\
    --node-count 1 \
    --generate-ssh-keys

Create a multi-instance GPU node pool

You can use either the Azure CLI or an HTTP request to the ARM API to create the node pool.

Azure CLI
HTTP request

Create a multi-instance GPU node pool using the az aks nodepool add command and specify the GPU instance profile.

az aks nodepool add \
    --name mignode \
    --resource-group myResourceGroup \
    --cluster-name myAKSCluster \
    --node-vm-size Standard_ND96asr_v4 \
    --gpu-instance-profile MIG1g

Create a multi-instance GPU node pool by placing the GPU instance profile in the request body.

{
    "properties": {
        "count": 1,
        "vmSize": "Standard_ND96asr_v4",
        "type": "VirtualMachineScaleSets",
        "gpuInstanceProfile": "MIG1g"
    }
}

Determine multi-instance GPU (MIG) strategy

Before you install the Nvidia plugins, you need to specify which multi-instance GPU (MIG) strategy to use for GPU partitioning: Single strategy or Mixed strategy. The two strategies don't affect how you execute CPU workloads, but how GPU resources are displayed.

Single strategy: The single strategy treats every GPU instance as a GPU. If you use this strategy, the GPU resources are displayed as nvidia.com/gpu: 1.
Mixed strategy: The mixed strategy exposes the GPU instances and the GPU instance profile. If you use this strategy, the GPU resource are displayed as nvidia.com/mig1g.5gb: 1.

Install the NVIDIA device plugin and GPU feature discovery

Set your MIG strategy as an environment variable. You can use either single or mixed strategy.
```
# Single strategy
export MIG_STRATEGY=single

# Mixed strategy
export MIG_STRATEGY=mixed
```

Add the Nvidia device plugin and GPU feature discovery helm repos using the helm repo add and helm repo update commands.

helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
helm repo add nvgfd https://nvidia.github.io/gpu-feature-discovery
helm repo update

Install the Nvidia device plugin using the helm install command.

helm install \
--version=0.14.0 \
--generate-name \
--set migStrategy=${MIG_STRATEGY} \
nvdp/nvidia-device-plugin

Install the GPU feature discovery using the helm install command.

helm install \
--version=0.2.0 \
--generate-name \
--set migStrategy=${MIG_STRATEGY} \
nvgfd/gpu-feature-discovery

Confirm multi-instance GPU capability

Configure kubectl to connect to your AKS cluster using the az aks get-credentials command.
```
az aks get-credentials --resource-group myResourceGroup --name myAKSCluster
```
Verify the connection to your cluster using the kubectl get command to return a list of cluster nodes.
```
kubectl get nodes -o wide
```
Confirm the node has multi-instance GPU capability using the kubectl describe node command. The following example command describes the node named mignode, which uses MIG1g as the GPU instance profile.
```
kubectl describe node mignode
```
Your output should resemble the following example output:
```
# Single strategy output
Allocatable:
    nvidia.com/gpu: 56

# Mixed strategy output
Allocatable:
    nvidia.com/mig-1g.5gb: 56
```

Schedule work

The following examples are based on cuda base image version 12.1.1 for Ubuntu22.04, tagged as 12.1.1-base-ubuntu22.04.

Single strategy

Create a file named single-strategy-example.yaml and copy in the following manifest.

apiVersion: v1
kind: Pod
metadata:
  name: nvidia-single
spec:
  containers:
  - name: nvidia-single
    image: nvidia/cuda:12.1.1-base-ubuntu22.04
    command: ["/bin/sh"]
    args: ["-c","sleep 1000"]
    resources:
      limits:
        "nvidia.com/gpu": 1

Deploy the application using the kubectl apply command and specify the name of your YAML manifest.
```
kubectl apply -f single-strategy-example.yaml
```

Verify the allocated GPU devices using the kubectl exec command. This command returns a list of the cluster nodes.

kubectl exec nvidia-single -- nvidia-smi -L

The following example resembles output showing successfully created deployments and services:

GPU 0: NVIDIA A100 40GB PCIe (UUID: GPU-48aeb943-9458-4282-da24-e5f49e0db44b)
MIG 1g.5gb     Device  0: (UUID: MIG-fb42055e-9e53-5764-9278-438605a3014c)
MIG 1g.5gb     Device  1: (UUID: MIG-3d4db13e-c42d-5555-98f4-8b50389791bc)
MIG 1g.5gb     Device  2: (UUID: MIG-de819d17-9382-56a2-b9ca-aec36c88014f)
MIG 1g.5gb     Device  3: (UUID: MIG-50ab4b32-92db-5567-bf6d-fac646fe29f2)
MIG 1g.5gb     Device  4: (UUID: MIG-7b6b1b6e-5101-58a4-b5f5-21563789e62e)
MIG 1g.5gb     Device  5: (UUID: MIG-14549027-dd49-5cc0-bca4-55e67011bd85)
MIG 1g.5gb     Device  6: (UUID: MIG-37e055e8-8890-567f-a646-ebf9fde3ce7a)

Mixed strategy

Create a file named mixed-strategy-example.yaml and copy in the following manifest.

apiVersion: v1
kind: Pod
metadata:
  name: nvidia-mixed
spec:
  containers:
  - name: nvidia-mixed
    image: nvidia/cuda:12.1.1-base-ubuntu22.04
    command: ["/bin/sh"]
    args: ["-c","sleep 100"]
    resources:
      limits:
        "nvidia.com/mig-1g.5gb": 1

Deploy the application using the kubectl apply command and specify the name of your YAML manifest.
```
kubectl apply -f mixed-strategy-example.yaml
```
Verify the allocated GPU devices using the kubectl exec command. This command returns a list of the cluster nodes.
```
kubectl exec nvidia-mixed -- nvidia-smi -L
```
The following example resembles output showing successfully created deployments and services:
```
GPU 0: NVIDIA A100 40GB PCIe (UUID: GPU-48aeb943-9458-4282-da24-e5f49e0db44b)
MIG 1g.5gb     Device  0: (UUID: MIG-fb42055e-9e53-5764-9278-438605a3014c)
```

Important

The latest tag for CUDA images has been deprecated on Docker Hub. Please refer to NVIDIA's repository for the latest images and corresponding tags.

Troubleshooting

If you don't see multi-instance GPU capability after creating the node pool, confirm the API version isn't older than 2021-08-01.

Next steps

For more information on AKS node pools, see Manage node pools for a cluster in AKS.

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