NVIDIA device plugin for Kubernetes
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flx42 Add toleration for the resource advertised by the device plugin
Yes, this is backward, but we don't have a better solution yet.

Fixes: #68

Signed-off-by: Felix Abecassis <fabecassis@nvidia.com>
Latest commit 2d56964 Aug 22, 2018

README.md

NVIDIA device plugin for Kubernetes

Table of Contents

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

Quick Start

Preparing your GPU Nodes

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

You will need to 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": []
        }
    }
}

if runtimes is not already present, head to the install page of nvidia-docker

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

WARNING: if you don't request GPUs when using the device plugin with NVIDIA images all the GPUs on the machine will be exposed inside your container.

Docs

Please note that:

  • the device plugin feature is beta as of Kubernetes v1.11.
  • the NVIDIA device plugin is still considered beta and is missing
    • 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