Name: k8s-device-plugin
Owner: NVIDIA Corporation
Description: NVIDIA device plugin for Kubernetes
Created: 2017-10-10 21:31:02.0
Updated: 2018-04-01 17:19:59.0
Pushed: 2018-03-28 03:17:42.0
Homepage: null
Size: 1686
Language: Go
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The NVIDIA device plugin for Kubernetes is a Daemonset that allows you to automatically:
This repository contains NVIDIA's official implementation of the Kubernetes device plugin.
The list of prerequisites for running the NVIDIA device plugin is described below:
DevicePlugins
feature gate enabledThe 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": []
}
}
if
runtimes
is not already present, head to the install page of nvidia-docker
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:
ronment="KUBELET_EXTRA_ARGS=--feature-gates=DevicePlugins=true"
If you spot the Accelerators feature gate you should remove it as it might interfere with the DevicePlugins feature gate
Reload and restart the kubelet to pick up the config change:
do systemctl daemon-reload
do systemctl restart kubelet
In this guide we used kubeadm and kubectl as the method for setting up and administering the Kubernetes cluster, but there are many ways to deploy a Kubernetes cluster. To enable the
DevicePlugins
feature gate if you are not using the kubeadm + systemd configuration, you will need to make sure that the arguments that are passed to Kubelet include the following--feature-gates=DevicePlugins=true
.
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:
bectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.10/nvidia-device-plugin.yml
NVIDIA GPUs can now be consumed via container level resource requirements using the resource name nvidia.com/gpu:
ersion: v1
: Pod
data:
me: gpu-pod
:
ntainers:
- 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.
Please note that:
The next sections are focused on building the device plugin and running it.
Option 1, pull the prebuilt image from Docker Hub:
cker pull nvidia/k8s-device-plugin:1.10
Option 2, build without cloning the repository:
cker build -t nvidia/k8s-device-plugin:1.10 https://github.com/NVIDIA/k8s-device-plugin.git#v1.10
Option 3, if you want to modify the code:
t clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin
cker build -t nvidia/k8s-device-plugin:1.10 .
cker 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:1.10
bectl create -f nvidia-device-plugin.yml
INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build
k8s-device-plugin