- About
- Prerequisites
- Quick Start
- Deployment via
helm
- Building and Running Locally
- Changelog
- Issues and Contributing
- Versioning
- Upgrading Kubernetes with the Device Plugin
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).
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 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
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/v0.9.0/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.
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: nvcr.io/nvidia/cuda:9.0-devel
resources:
limits:
nvidia.com/gpu: 2 # requesting 2 GPUs
- name: digits-container
image: nvcr.io/nvidia/digits:20.12-tensorflow-py3
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.
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 (v0.9.0
) includes
a number of customizable values. The most commonly overridden ones are:
failOnInitError:
fail the plugin if an error is encountered during initialization, otherwise block indefinitely
(default 'true')
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')
migStrategy:
the desired strategy for exposing MIG devices on GPUs that support it
[none | single | mixed] (default "none")
deviceListStrategy:
the desired strategy for passing the device list to the underlying runtime
[envvar | volume-mounts] (default "envvar")
deviceIDStrategy:
the desired strategy for passing device IDs to the underlying runtime
[uuid | index] (default "uuid")
nvidiaDriverRoot:
the root path for the NVIDIA driver installation (typical values are '/' or '/run/nvidia/driver')
When set to true, the failOnInitError
flag fails the plugin if an error is
encountered during initialization. When set to false, it prints an error
message and blocks the plugin indefinitely instead of failing. Blocking
indefinitely follows legacy semantics that allow the plugin to deploy
successfully on nodes that don't have GPUs on them (and aren't supposed to have
GPUs on them) without throwing an error. In this way, you can blindly deploy a
daemonset with the plugin on all nodes in your cluster, whether they have GPUs
on them or not, without encountering an error. However, doing so means that
there is no way to detect an actual error on nodes that are supposed to have
GPUs on them. Failing if an initilization error is encountered is now the
default and should be adopted by all new deployments.
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.
The migStrategy
flag configures the daemonset to be able to expose
Multi-Instance GPUs (MIG) on GPUs that support them. More information on what
these strategies are and how they should be used can be found in Supporting
Multi-Instance GPUs (MIG) in
Kubernetes.
Note: With a migStrategy
of mixed, you will have additional resources
available to you of the form nvidia.com/mig-<slice_count>g.<memory_size>gb
that you can set in your pod spec to get access to a specific MIG device.
The deviceListStrategy
flag allows one to choose which strategy the plugin
will use to advertise the list of GPUs allocated to a container. This is
traditionally done by setting the NVIDIA_VISIBLE_DEVICES
environment variable
as described
here.
This strategy can be selected via the (default) envvar
option. Support was
recently added to the nvidia-container-toolkit
to also allow passing the list
of devices as a set of volume mounts instead of as an environment variable.
This strategy can be selected via the volume-mounts
option. Details for the
rationale behind this strategy can be found
here.
The deviceIDStrategy
flag allows one to choose which strategy the plugin will
use to pass the device ID of the GPUs allocated to a container. The device ID
has traditionally been passed as the UUID of the GPU. This flag lets a user
decide if they would like to use the UUID or the index of the GPU (as seen in
the output of nvidia-smi
) as the identifier passed to the underlying runtime.
Passing the index may be desirable in situations where pods that have been
allocated GPUs by the plugin get restarted with different physical GPUs
attached to them.
Please take a look in the following values.yaml
file to see the full set of
overridable parameters for the device plugin.
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.
Note: Since this is a pre-release version, you will need to pass the
--devel
flag to helm search repo
in order to see this release listed.
Using the default values for the flags:
$ helm install \
--version=0.9.0 \
--generate-name \
nvdp/nvidia-device-plugin
Enabling compatibility with the CPUManager
and running with a request for
100ms of CPU time and a limit of 512MB of memory.
$ helm install \
--version=0.9.0 \
--generate-name \
--set compatWithCPUManager=true \
--set resources.requests.cpu=100m \
--set resources.limits.memory=512Mi \
nvdp/nvidia-device-plugin
Use the legacy Daemonset API (only available on Kubernetes < v1.16
):
$ helm install \
--version=0.9.0 \
--generate-name \
--set legacyDaemonsetAPI=true \
nvdp/nvidia-device-plugin
Enabling compatibility with the CPUManager
and the mixed
migStrategy
$ helm install \
--version=0.9.0 \
--generate-name \
--set compatWithCPUManager=true \
--set migStrategy=mixed \
nvdp/nvidia-device-plugin
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-0.9.0.tgz
Enabling compatibility with the CPUManager
and running with a request for
100ms of CPU time and a limit of 512MB of memory.
$ helm install \
--generate-name \
--set compatWithCPUManager=true \
--set resources.requests.cpu=100m \
--set resources.limits.memory=512Mi \
https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.9.0.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-0.9.0.tgz
Enabling compatibility with the CPUManager
and the mixed
migStrategy
$ helm install \
--generate-name \
--set compatWithCPUManager=true \
--set migStrategy=mixed \
https://nvidia.github.com/k8s-device-plugin/stable/nvidia-device-plugin-0.9.0.tgz
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. v0.9.0
), but can
easily be modified to work with any available tag or branch.
Option 1, pull the prebuilt image from Docker Hub:
$ docker pull nvcr.io/nvidia/k8s-device-plugin:v0.9.0
$ docker tag nvcr.io/nvidia/k8s-device-plugin:v0.9.0 nvcr.io/nvidia/k8s-device-plugin:devel
Option 2, build without cloning the repository:
$ docker build \
-t nvcr.io/nvidia/k8s-device-plugin:devel \
-f docker/Dockerfile.ubuntu20.04 \
https://github.com/NVIDIA/k8s-device-plugin.git#v0.9.0
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 nvcr.io/nvidia/k8s-device-plugin:devel \
-f docker/Dockerfile.ubuntu20.04 \
.
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 \
nvcr.io/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 \
nvcr.io/nvidia/k8s-device-plugin:devel --pass-device-specs
$ C_INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build
Without compatibility for the CPUManager
static policy:
$ ./k8s-device-plugin
With compatibility for the CPUManager
static policy:
$ ./k8s-device-plugin --pass-device-specs
- Fix bug when using CPUManager and the device plugin MIG mode not set to "none"
- Allow passing list of GPUs by device index instead of uuid
- Move to urfave/cli to build the CLI
- Support setting command line flags via environment variables
- Update all dockerhub references to nvcr.io
- Fix permission error when using NewDevice instead of NewDeviceLite when constructing MIG device map
- Raise an error if a device has migEnabled=true but has no MIG devices
- Allow mig.strategy=single on nodes with non-MIG gpus
- Update vendoring to include bug fix for
nvmlEventSetWait_v2
- Fix bug in dockfiles for ubi8 and centos using CMD not ENTRYPOINT
- Update all Dockerfiles to point to latest cuda-base on nvcr.io
- Promote v0.7.0-rc.8 to v0.7.0
- Permit configuration of alternative container registry through environment variables.
- Add an alternate set of gitlab-ci directives under .nvidia-ci.yml
- Update all k8s dependencies to v1.19.1
- Update vendoring for NVML Go bindings
- Move restart loop to force recreate of plugins on SIGHUP
- Fix bug which only allowed running the plugin on machines with CUDA 10.2+ installed
- Add logic to skip / error out when unsupported MIG device encountered
- Fix bug treating memory as multiple of 1000 instead of 1024
- Switch to using CUDA base images
- Add a set of standard tests to the .gitlab-ci.yml file
- Add deviceListStrategyFlag to allow device list passing as volume mounts
- Allow one to override selector.matchLabels in the helm chart
- Allow one to override the udateStrategy in the helm chart
- Fail the plugin if NVML cannot be loaded
- Update logging to print to stderr on error
- Add best effort removal of socket file before serving
- Add logic to implement GetPreferredAllocation() call from kubelet
- Add the ability to set 'resources' as part of a helm install
- Add overrides for name and fullname in helm chart
- Add ability to override image related parameters helm chart
- Add conditional support for overriding secutiryContext in helm chart
- Added
migStrategy
as a parameter to select the MIG strategy to the helm chart - Add support for MIG with different strategies {none, single, mixed}
- Update vendored NVML bindings to latest (to include MIG APIs)
- Add license in UBI image
- Update UBI image with certification requirements
- 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
- 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.