cvaldit/nvidia-k8s-device-plugin repository overview

⁠NVIDIA device plugin for Kubernetes

⁠Table of Contents

• About⁠
• Prerequisites⁠
• Quick Start⁠
  • Preparing your GPU Nodes⁠
  • Enabling GPU Support in Kubernetes⁠
  • Running GPU Jobs⁠
• Docs⁠
• Changelog⁠
• Issues and Contributing⁠

⁠About

The NVIDIA device plugin for Kubernetes is a Daemonset that allows you to automatically:

• Expose the number of GPUs on each nodes of your cluster
• Keep track of the health of your GPUs
• Run GPU enabled containers in your Kubernetes cluster.

This repository contains NVIDIA's official implementation of the Kubernetes device plugin⁠.

⁠Prerequisites

The list of prerequisites for running the NVIDIA device plugin is described below:

• NVIDIA drivers ~= 361.93
• nvidia-docker version > 2.0 (see how to install⁠ and it's prerequisites⁠)
• docker configured with nvidia as the default runtime⁠.
• Kubernetes version = 1.11
• The DevicePlugins feature gate enabled

⁠Quick Start

⁠Preparing your GPU Nodes

The following steps need to be executed on all your GPU nodes. Additionally, this README assumes that the NVIDIA drivers and nvidia-docker has been installed.

First you will need to check and/or enable the nvidia runtime as your default runtime on your node. We will be editing the docker daemon config file which is usually present at /etc/docker/daemon.json:

{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

The second step is to enable the DevicePlugins feature gate on all your GPU nodes.

If your Kubernetes cluster is deployed using kubeadm and your nodes are running systemd you will have to open the kubeadm systemd unit file at /etc/systemd/system/kubelet.service.d/10-kubeadm.conf and add the following environment argument:

Environment="KUBELET_EXTRA_ARGS=--feature-gates=DevicePlugins=true"

Reload and restart the kubelet to pick up the config change:

$ sudo systemctl daemon-reload
$ sudo systemctl restart kubelet

⁠Enabling GPU Support in Kubernetes

Once you have enabled this option on all the GPU nodes you wish to use, you can then enable GPU support in your cluster by deploying the following Daemonset:

$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.11/nvidia-device-plugin.yml

⁠Running GPU Jobs

NVIDIA GPUs can now be consumed via container level resource requirements using the resource name nvidia.com/gpu:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  containers:
    - name: cuda-container
      image: nvidia/cuda:9.0-devel
      resources:
        limits:
          nvidia.com/gpu: 2 # requesting 2 GPUs
    - name: digits-container
      image: nvidia/digits:6.0
      resources:
        limits:
          nvidia.com/gpu: 2 # requesting 2 GPUs

⁠Docs

Please note that:

• the device plugin feature is still alpha which is why it requires the feature gate to be enabled.
• the NVIDIA device plugin is still considered alpha and is missing
  • Security features
  • More comprehensive GPU health checking features
  • GPU cleanup features
  • ...
• support will only be provided for the official NVIDIA device plugin.

The next sections are focused on building the device plugin and running it.

⁠With Docker

⁠Build

Option 1, pull the prebuilt image from Docker Hub⁠:

$ docker pull nvidia/k8s-device-plugin:1.11

Option 2, build without cloning the repository:

$ docker build -t nvidia/k8s-device-plugin:1.11 https://github.com/NVIDIA/k8s-device-plugin.git#v1.11

Option 3, if you want to modify the code:

$ git clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin
$ docker build -t nvidia/k8s-device-plugin:1.11 .

⁠Run locally

$ docker run --security-opt=no-new-privileges --cap-drop=ALL --network=none -it -v /var/lib/kubelet/device-plugins:/var/lib/kubelet/device-plugins nvidia/k8s-device-plugin:1.11

⁠Deploy as Daemon Set:

$ kubectl create -f nvidia-device-plugin.yml

⁠Without Docker

⁠Build

$ C_INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build

⁠Run locally

$ ./k8s-device-plugin

⁠Changelog

⁠Version 1.11

• No change.

⁠Version 1.10

• The device Plugin API is now v1beta1

⁠Version 1.9

• The device Plugin API changed and is no longer compatible with 1.8
• Error messages were added

⁠Issues and Contributing

• You can report a bug by filing a new issue⁠
• You can contribute by opening a pull request⁠

⁠Share GPU

Add share GPU by specifying DP_NUMBER_CONTAINERS_PER_GPU in your container environment.