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Step-by-step instructions for performing operations with Kubernetes.

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Schedule GPUs

Kubernetes includes experimental support for managing NVIDIA GPUs spread across nodes. The support for NVIDIA GPUs was added in v1.6 and has gone through multiple backwards incompatible iterations. This page describes how users can consume GPUs across different Kubernetes versions and the current limitations.

v1.6 and v1.7

To enable GPU support in 1.6 and 1.7, a special alpha feature gate Accelerators has to be set to true across the system: --feature-gates="Accelerators=true". It also requires using the Docker Engine as the container runtime.

Further, the Kubernetes nodes have to be pre-installed with NVIDIA drivers. Kubelet will not detect NVIDIA GPUs otherwise.

When you start Kubernetes components after all the above conditions are true, Kubernetes will expose alpha.kubernetes.io/nvidia-gpu as a schedulable resource.

You can consume these GPUs from your containers by requesting alpha.kubernetes.io/nvidia-gpu just like you request cpu or memory. However, there are some limitations in how you specify the resource requirements when using GPUs:

When using alpha.kubernetes.io/nvidia-gpu as the resource, you also have to mount host directories containing NVIDIA libraries (libcuda.so, libnvidia.so etc.) to the container.

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:
          alpha.kubernetes.io/nvidia-gpu: 1 # requesting 1 GPU
      volumeMounts:
        - name: "nvidia-libraries"
          mountPath: "/usr/local/nvidia/lib64"
  volumes:
    - name: "nvidia-libraries"
      hostPath:
        path: "/usr/lib/nvidia-375"

The Accelerators feature gate and alpha.kubernetes.io/nvidia-gpu resource works on 1.8 and 1.9 as well. It will be deprecated in 1.10 and removed in 1.11.

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, a special alpha feature gate DevicePlugins has to be set to true across the system: --feature-gates="DevicePlugins=true".

Then you have to install NVIDIA drivers on the nodes and run an NVIDIA GPU device plugin (see below).

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

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

Unlike with alpha.kubernetes.io/nvidia-gpu, when using nvidia.com/gpu as the resource, you don’t have to mount any special directories in your pod specs. The device plugin is expected to inject them automatically in the container.

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 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 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.

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