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.
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
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 dockerYou 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
runtimesis not already present, head to the install page of nvidia-docker
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/v0.6.0/nvidia-device-plugin.ymlNVIDIA 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 GPUsWARNING: 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 device plugin feature 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.
The next sections are focused on building the device plugin and running it.
Option 1, pull the prebuilt image from Docker Hub:
$ docker pull nvidia/k8s-device-plugin:v0.6.0Option 2, build without cloning the repository:
$ docker build -t nvidia/k8s-device-plugin:v0.6.0 https://github.com/NVIDIA/k8s-device-plugin.git#v0.6.0Option 3, if you want to modify the code:
$ git clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin
$ git checkout 0.6.1
$ docker build -t nvidia/k8s-device-plugin:v0.6.0 .$ 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:v0.6.0$ kubectl create -f nvidia-device-plugin.yml$ C_INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build$ ./k8s-device-plugin- Update CI, build system, and vendoring mechanism
- 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
- Fixes a bug with a nil pointer dereference around
getDevices:CPUAffinity
- Manifest is updated for Kubernetes 1.16+ (apps/v1)
- Adds more logging information
- Adds the Topology field for Kubernetes 1.16+
- If gRPC throws an error, the device plugin no longer ends up in a non responsive state.
- Reversioned to SEMVER as device plugins aren't tied to a specific version of kubernetes anymore.
- No change.
- The device Plugin API is now v1beta1
- The device Plugin API changed and is no longer compatible with 1.8
- Error messages were added
Checkout the Contributing document!
- You can report a bug by filing a new issue
- You can contribute by opening a pull request
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 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.