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Schedule GPUs

Kubernetes includes experimental support for managing AMD and NVIDIA GPUs spread across nodes. The support for NVIDIA GPUs was added in v1.6 and has gone through multiple backwards incompatible iterations. The support for AMD GPUs was added in v1.9 via device plugin.

This page describes how users can consume GPUs across different Kubernetes versions and the current limitations.

v1.8 onwards

From 1.8 onwards, the recommended way to consume GPUs is to use device plugins.

To enable GPU support through device plugins before 1.10, the DevicePlugins feature gate has to be explicitly set to true across the system: --feature-gates="DevicePlugins=true". This is no longer required starting from 1.10.

Then you have to install GPU drivers from the corresponding vendor on the nodes and run the corresponding device plugin from the GPU vendor (AMD, NVIDIA).

When the above conditions are true, Kubernetes will expose nvidia.com/gpu or amd.com/gpu as a schedulable resource.

You can consume these GPUs from your containers by requesting <vendor>.com/gpu just like you request cpu or memory. However, there are some limitations in how you specify the resource requirements when using GPUs:

Here’s an example:

apiVersion: v1
kind: Pod
metadata:
  name: cuda-vector-add
spec:
  restartPolicy: OnFailure
  containers:
    - name: cuda-vector-add
      # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
      image: "k8s.gcr.io/cuda-vector-add:v0.1"
      resources:
        limits:
          nvidia.com/gpu: 1 # requesting 1 GPU

Deploying AMD GPU device plugin

The official AMD GPU device plugin has the following requirements:

To deploy the AMD device plugin once your cluster is running and the above requirements are satisfied:

# For Kubernetes v1.9
kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.9/k8s-ds-amdgpu-dp.yaml

# For Kubernetes v1.10
kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.10/k8s-ds-amdgpu-dp.yaml

Report issues with this device plugin to RadeonOpenCompute/k8s-device-plugin.

Deploying NVIDIA GPU device plugin

There are currently two device plugin implementations for NVIDIA GPUs:

Official NVIDIA GPU device plugin

The official NVIDIA GPU device plugin has the following requirements:

To deploy the NVIDIA device plugin once your cluster is running and the above requirements are satisfied:

# For Kubernetes v1.8
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.8/nvidia-device-plugin.yml

# For Kubernetes v1.9
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.9/nvidia-device-plugin.yml

Report issues with this device plugin to NVIDIA/k8s-device-plugin.

NVIDIA GPU device plugin used by GKE/GCE

The NVIDIA GPU device plugin used by GKE/GCE doesn’t require using nvidia-docker and should work with any container runtime that is compatible with the Kubernetes Container Runtime Interface (CRI). It’s tested on Container-Optimized OS and has experimental code for Ubuntu from 1.9 onwards.

On your 1.9 cluster, you can use the following commands to install the NVIDIA drivers and device plugin:

# Install NVIDIA drivers on Container-Optimized OS:
kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/daemonset.yaml

# Install NVIDIA drivers on Ubuntu (experimental):
kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/nvidia-driver-installer/ubuntu/daemonset.yaml

# Install the device plugin:
kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/release-1.9/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml

Report issues with this device plugin and installation method to GoogleCloudPlatform/container-engine-accelerators.

Clusters containing different types of NVIDIA GPUs

If different nodes in your cluster have different types of NVIDIA GPUs, then you can use Node Labels and Node Selectors to schedule pods to appropriate nodes.

For example:

# Label your nodes with the accelerator type they have.
kubectl label nodes <node-with-k80> accelerator=nvidia-tesla-k80
kubectl label nodes <node-with-p100> accelerator=nvidia-tesla-p100

Specify the GPU type in the pod spec:

apiVersion: v1
kind: Pod
metadata:
  name: cuda-vector-add
spec:
  restartPolicy: OnFailure
  containers:
    - name: cuda-vector-add
      # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
      image: "k8s.gcr.io/cuda-vector-add:v0.1"
      resources:
        limits:
          nvidia.com/gpu: 1
  nodeSelector:
    accelerator: nvidia-tesla-p100 # or nvidia-tesla-k80 etc.

This will ensure that the pod will be scheduled to a node that has the GPU type you specified.