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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.

Please note that:

  • The NVIDIA device plugin API is beta as of Kubernetes v1.10.
  • 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 (and not for forks or other variants of this plugin).

Prerequisites

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

  • NVIDIA drivers ~= 384.81
  • nvidia-docker version > 2.0 (see how to install and it's prerequisites)
  • docker configured with nvidia as the default runtime.
  • Kubernetes version >= 1.10

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.

Note that you need to install the nvidia-docker2 package and not the nvidia-container-toolkit. This is because the new --gpus options hasn't reached kubernetes yet. Example:

# Add the package repositories
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
$ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

$ sudo apt-get update && sudo apt-get install -y nvidia-docker2
$ sudo systemctl restart docker

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 configured the options above on all the GPU nodes in your cluster, you can enable GPU support by deploying the following Daemonset:

$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.6.0/nvidia-device-plugin.yml

Note: This is a simple static daemonset meant to demonstrate the basic features of the nvidia-device-plugin. Please see the instructions below for Deployment via helm when deploying the plugin in a production setting.

Running GPU Jobs

With the daemonset deployed, NVIDIA GPUs can now be requested by a container using the nvidia.com/gpu resource type:

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.

Deployment via helm

The preferred method to deploy the device plugin is as a daemonset using helm. Instructions for installing helm can be found here.

The helm chart for the latest release of the plugin (v0.6.0) includes the follow customizeable values:

  compatWithCPUManager:
      run with escalated privileges to be compatible with the static CPUManager policy
      (default 'false')
  legacyDaemonsetAPI:
      use the legacy daemonset API version 'extensions/v1beta1'
      (default 'false')

The compatWithCPUManager flag configures the daemonset to be able to interoperate with the static CPUManager of the kubelet. Setting this flag requires one to deploy the daemonset with elevated privileges, so only do so if you know you need to interoperate with the CPUManager.

The legacyDaemonsetAPI flag configures the daemonset to use version extensions/v1beta1 of the DaemonSet API. This API version was removed in Kubernetes v1.16, so is only intended to allow newer plugins to run on older versions of Kubernetes.

We also allow overrides of the following common user-specific settings:

  • namespace
  • image.pullPolicy
  • nodeSelector
  • affinity
  • tolerations

Installing via helm installfrom the nvidia-device-plugin helm repository

The preferred method of deployment is with helm install via the nvidia-device-plugin helm repository.

This repository can be installed as follows:

$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
$ helm repo update

Once this repo is updated, you can begin installing packages from it to depoloy the nvidia-device-plugin daemonset. Below are some examples of deploying the plugin with the various flags from above.

Using the default values for the flags:

$ helm install \
    --version=0.6.0 \
    --generate-name \
    nvdp/nvidia-device-plugin

Enabling compatibility with the CPUManager and deploying only to nodes with Tesla GPUs on them:

$ helm install \
    --version=0.6.0 \
    --generate-name \
    --set compatWithCPUManager=true \
    --set nodeSelector."nvidia\.com/gpu\.family"=tesla \
    nvdp/nvidia-device-plugin

Use the legacy Daemonset API (only available on Kubernetes < v1.16):

$ helm install \
    --version=0.6.0 \
    --generate-name \
    --set legacyDaemonsetAPI=true \
    nvdp/nvidia-device-plugin

Deploying via helm install with a direct URL to the helm package

If you prefer not to install from the nvidia-device-plugin helm repo, you can run helm install directly against the tarball of the plugin's helm package. The examples below install the same daemonsets as the method above, except that they use direct URLs to the helm package instead of the helm repo.

Using the default values for the flags:

$ helm install \
    --generate-name \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz

Enabling compatibility with the CPUManager and deploying only to nodes with Tesla GPUs on them:

$ helm install \
    --generate-name \
    --set compatWithCPUManager=true \
    --set nodeSelector."nvidia\.com/gpu\.family"=tesla \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz

Use the legacy Daemonset API (only available on Kubernetes < v1.16):

$ helm install \
    --generate-name \
    --set legacyDaemonsetAPI=true \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz

Deploying via kubectl apply

If you prefer to deploy the plugin directly with kubectl apply you can extract the generated template from helm using helm template and feed that to kubectl apply. The examples below install the same daemonsets as the helm install variants above, but use kubectl apply instead.

Using the default values for the flags (i.e. no compatibility with the CPUManager and without the legacy DaemonSet API):

$ helm template \
    --name-template=nvidia-device-plugin-$(date +%s) \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz \
  | kubectl apply -f -

Enabling compatibility with the CPUManager and deploying only to nodes with Tesla GPUs on them:

$ helm template \
    --name-template=nvidia-device-plugin-$(date +%s) \
    --set compatWithCPUManager=true \
    --set nodeSelector."nvidia\.com/gpu\.family"=tesla \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz \
  | kubectl apply -f -

Using the legacy Daemonset API (only available on Kubernetes < v1.16):

$ helm template \
    --name-template=nvidia-device-plugin-$(date +%s) \
    --set legacyDaemonsetAPI=true \
    https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.6.0.tgz \
  | kubectl apply -f -

Building and Running Locally

The next sections are focused on building the device plugin locally and running it. It is intended purely for development and testing, and not required by most users. It assumes you are pinning to the latest release tag (i.e. v0.6.0), but can easily be modified to work with any available tag or branch.

With Docker

Build

Option 1, pull the prebuilt image from Docker Hub:

$ docker pull nvidia/k8s-device-plugin:v0.6.0
$ docker tag nvidia/k8s-device-plugin:v0.6.0 nvidia/k8s-device-plugin:devel

Option 2, build without cloning the repository:

$ docker build \
    -t nvidia/k8s-device-plugin:devel \
    -f docker/amd64/Dockerfile.ubuntu16.04 \
    https://github.com/NVIDIA/k8s-device-plugin.git#v0.6.0

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:devel \
    -f docker/amd64/Dockerfile.ubuntu16.04 \
    .

Run

Without compatibility for the CPUManager static policy:

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

With compatibility for the CPUManager static policy:

$ docker run \
    -it \
    --privileged \
    --network=none \
    -v /var/lib/kubelet/device-plugins:/var/lib/kubelet/device-plugins \
    nvidia/k8s-device-plugin:devel --pass-device-specs

Without Docker

Build

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

Run

Without compatibility for the CPUManager static policy:

$ ./k8s-device-plugin

With compatibility for the CPUManager static policy:

$ ./k8s-device-plugin --pass-device-specs

Changelog

Version v0.6.0

  • Update CI, build system, and vendoring mechanism
  • Change versioning scheme to v0.x.x instead of v1.0.0-betax
  • Introduced helm charts as a mechanism to deploy the plugin

Version v0.5.0

  • Add a new plugin.yml variant that is compatible with the CPUManager
  • Change CMD in Dockerfile to ENTRYPOINT
  • Add flag to optionally return list of device nodes in Allocate() call
  • Refactor device plugin to eventually handle multiple resource types
  • Move plugin error retry to event loop so we can exit with a signal
  • Update all vendored dependencies to their latest versions
  • Fix bug that was inadvertently always disabling health checks
  • Update minimal driver version to 384.81

Version v0.4.0

  • Fixes a bug with a nil pointer dereference around getDevices:CPUAffinity

Version v0.3.0

  • Manifest is updated for Kubernetes 1.16+ (apps/v1)
  • Adds more logging information

Version v0.2.0

  • Adds the Topology field for Kubernetes 1.16+

Version v0.1.0

  • If gRPC throws an error, the device plugin no longer ends up in a non responsive state.

Version v0.0.0

  • Reversioned to SEMVER as device plugins aren't tied to a specific version of kubernetes anymore.

Version v1.11

  • No change.

Version v1.10

  • The device Plugin API is now v1beta1

Version v1.9

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

Issues and Contributing

Checkout the Contributing document!

Versioning

Before v1.10 the versioning scheme of the device plugin had to match exactly the version of Kubernetes. After the promotion of device plugins to beta this condition was was no longer required. We quickly noticed that this versioning scheme was very confusing for users as they still expected to see a version of the device plugin for each version of Kubernetes.

This versioning scheme applies to the tags v1.8, v1.9, v1.10, v1.11, v1.12.

We have now changed the versioning to follow SEMVER. The first version following this scheme has been tagged v0.0.0.

Going forward, the major version of the device plugin will only change following a change in the device plugin API itself. For example, version v1beta1 of the device plugin API corresponds to version v0.x.x of the device plugin. If a new v2beta2 version of the device plugin API comes out, then the device plugin will increase its major version to 1.x.x.

As of now, the device plugin API for Kubernetes >= v1.10 is v1beta1. If you have a version of Kubernetes >= 1.10 you can deploy any device plugin version > v0.0.0.

Upgrading Kubernetes with the Device Plugin

Upgrading Kubernetes when you have a device plugin deployed doesn't require you to do any, particular changes to your workflow. The API is versioned and is pretty stable (though it is not guaranteed to be non breaking). Starting with Kubernetes version 1.10, you can use v0.3.0 of the device plugin to perform upgrades, and Kubernetes won't require you to deploy a different version of the device plugin. Once a node comes back online after the upgrade, you will see GPUs re-registering themselves automatically.

Upgrading the device plugin itself is a more complex task. It is recommended to drain GPU tasks as we cannot guarantee that GPU tasks will survive a rolling upgrade. However we make best efforts to preserve GPU tasks during an upgrade.

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