Intel GPU device plugin for Kubernetes

Table of Contents


Intel GPU plugin facilitates Kubernetes workload offloading by providing access to discrete (including Intel® Data Center GPU Flex Series) and integrated Intel GPU devices supported by the host kernel.

Use cases include, but are not limited to:

  • Media transcode

  • Media analytics

  • Cloud gaming

  • High performance computing

  • AI training and inference

For example containers with Intel media driver (and components using that), can offload video transcoding operations, and containers with the Intel OpenCL / oneAPI Level Zero backend libraries can offload compute operations to GPU.

Modes and Configuration Options

Flag Argument Default Meaning
-enable-monitoring - disabled Enable 'i915_monitoring' resource that provides access to all Intel GPU devices on the node
-resource-manager - disabled Enable fractional resource management, see also dependencies
-shared-dev-num int 1 Number of containers that can share the same GPU device
-allocation-policy string none 3 possible values: balanced, packed, none. It is meaningful when shared-dev-num > 1, balanced mode is suitable for workload balance among GPU devices, packed mode is suitable for making full use of each GPU device, none mode is the default. Allocation policy does not have effect when resource manager is enabled.

The plugin also accepts a number of other arguments (common to all plugins) related to logging. Please use the -h option to see the complete list of logging related options.


The following sections detail how to obtain, build, deploy and test the GPU device plugin.

Examples are provided showing how to deploy the plugin either using a DaemonSet or by hand on a per-node basis.


Access to a GPU device requires firmware, kernel and user-space drivers supporting it. Firmware and kernel driver need to be on the host, user-space drivers in the GPU workload containers.

Intel GPU devices supported by the current kernel can be listed with:

$ grep i915 /sys/class/drm/card?/device/uevent

Drivers for discrete GPUs

Kernel driver
Intel DKMS packages

i915 GPU driver DKMS^dkms package is recommended until Intel discrete GPU support in upstream is complete. It can be installed from Intel package repositories for a subset of older kernel versions used in enterprise / LTS distributions:

Upstream kernel

With upstream 6.x kernels, discrete GPU support needs to be enabled using kernel i915.force_probe=<PCI_ID> command line option until relevant kernel driver features have been completed also in upstream:

PCI IDs for the Intel GPUs on given host can be listed with:

$ lspci | grep -e VGA -e Display | grep Intel
88:00.0 Display controller: Intel Corporation Device 56c1 (rev 05)
8d:00.0 Display controller: Intel Corporation Device 56c1 (rev 05)

(lspci lists GPUs with display support as “VGA compatible controller”, and server GPUs without display support, as “Display controller”.)

Mesa “Iris” 3D driver header provides a mapping between GPU PCI IDs and their Intel brand names:

If your kernel build does not find the correct firmware version for a given GPU from the host (see dmesg | grep i915 output), latest firmware versions are available in upstream:

User-space drivers

Until new enough user-space drivers (supporting also discrete GPUs) are available directly from distribution package repositories, they can be installed to containers from Intel package repositories. See:

Example container is listed in Testing and demos.

Validation status against upstream kernel is listed in the user-space drivers release notes:

  • Media driver:

  • Compute driver:

Drivers for older (integrated) GPUs

For the older (integrated) GPUs, new enough firmware and kernel driver are typically included already with the host OS, and new enough user-space drivers (for the GPU containers) are in the host OS repositories.

Pre-built Images

Pre-built images of this component are available on the Docker hub. These images are automatically built and uploaded to the hub from the latest main branch of this repository.

Release tagged images of the components are also available on the Docker hub, tagged with their release version numbers in the format x.y.z, corresponding to the branches and releases in this repository. Thus the easiest way to deploy the plugin in your cluster is to run this command

Note: Replace <RELEASE_VERSION> with the desired release tag or main to get devel images.

See the development guide for details if you want to deploy a customized version of the plugin.

Install to all nodes

Simplest option to enable use of Intel GPUs in Kubernetes Pods.

$ kubectl apply -k '<RELEASE_VERSION>'

Install to nodes with Intel GPUs with NFD

Deploying GPU plugin to only nodes that have Intel GPU attached. Node Feature Discovery is required to detect the presence of Intel GPUs.

# Start NFD - if your cluster doesn't have NFD installed yet
$ kubectl apply -k '<RELEASE_VERSION>'

# Create NodeFeatureRules for detecting GPUs on nodes
$ kubectl apply -k '<RELEASE_VERSION>'

# Create GPU plugin daemonset
$ kubectl apply -k '<RELEASE_VERSION>'

Install to nodes with NFD, Monitoring and Shared-dev

Same as above, but configures GPU plugin with logging, monitoring and shared-dev features enabled. This option is useful when there is a desire to retrieve GPU metrics from nodes. For example with XPU-Manager or collectd.

# Start NFD - if your cluster doesn't have NFD installed yet
$ kubectl apply -k '<RELEASE_VERSION>'

# Create NodeFeatureRules for detecting GPUs on nodes
$ kubectl apply -k '<RELEASE_VERSION>'

# Create GPU plugin daemonset
$ kubectl apply -k '<RELEASE_VERSION>'

Install to nodes with Intel GPUs with Fractional resources

With the experimental fractional resource feature you can use additional kubernetes extended resources, such as GPU memory, which can then be consumed by deployments. PODs will then only deploy to nodes where there are sufficient amounts of the extended resources for the containers.

(For this to work properly, all GPUs in a given node should provide equal amount of resources i.e. heteregenous GPU nodes are not supported.)

Enabling the fractional resource feature isn’t quite as simple as just enabling the related command line flag. The DaemonSet needs additional RBAC-permissions and access to the kubelet podresources gRPC service, plus there are other dependencies to take care of, which are explained below. For the RBAC-permissions, gRPC service access and the flag enabling, it is recommended to use kustomization by running:

# Start NFD with GPU related configuration changes
$ kubectl apply -k '<RELEASE_VERSION>'

# Create NodeFeatureRules for detecting GPUs on nodes
$ kubectl apply -k '<RELEASE_VERSION>'

# Create GPU plugin daemonset
$ kubectl apply -k '<RELEASE_VERSION>'
Fractional resources details

Usage of these fractional GPU resources requires that the cluster has node extended resources with the name prefix Those can be created with NFD by running the hook installed by the plugin initcontainer. When fractional resources are enabled, the plugin lets a scheduler extender do card selection decisions based on resource availability and the amount of extended resources requested in the pod spec.

The scheduler extender then needs to annotate the pod objects with unique increasing numeric timestamps in the annotation gas-ts and container card selections in gas-container-cards annotation. The latter has container separator ‘|’ and card separator ‘,’. Example for a pod with two containers and both containers getting two cards: gas-container-cards:card0,card1|card2,card3. Enabling the fractional-resource support in the plugin without running such an annotation adding scheduler extender in the cluster will only slow down GPU-deployments, so do not enable this feature unnecessarily.

In multi-tile systems, containers can request individual tiles to improve GPU resource usage. Tiles targeted for containers are specified to pod via gas-container-tiles annotation where the the annotation value describes a set of card and tile combinations. For example in a two container pod, the annotation could be gas-container-tiles:card0:gt0+gt1|card1:gt1,card2:gt0. Similarly to gas-container-cards, the container details are split via |. In the example above, the first container gets tiles 0 and 1 from card 0, and the second container gets tile 1 from card 1 and tile 0 from card 2.

Note: It is also possible to run the GPU device plugin using a non-root user. To do this, the nodes’ DAC rules must be configured to device plugin socket creation and kubelet registration. Furthermore, the deployments securityContext must be configured with appropriate runAsUser/runAsGroup.

Verify Plugin Registration

You can verify the plugin has been registered with the expected nodes by searching for the relevant resource allocation status on the nodes:

$ kubectl get nodes -o=jsonpath="{range .items[*]}{}{'\n'}{' i915: '}{.status.allocatable.gpu\.intel\.com/i915}{'\n'}"
 i915: 1

Testing and Demos

The GPU plugin functionality can be verified by deploying an OpenCL image which runs clinfo outputting the GPU capabilities (detected by driver installed to the image).

  1. Make the image available to the cluster:

    Build image:

    $ make intel-opencl-icd

    Tag and push the intel-opencl-icd image to a repository available in the cluster. Then modify the intelgpu-job.yaml’s image location accordingly:

    $ docker tag intel/intel-opencl-icd:devel <repository>/intel/intel-opencl-icd:latest
    $ docker push <repository>/intel/intel-opencl-icd:latest
    $ $EDITOR ${INTEL_DEVICE_PLUGINS_SRC}/demo/intelgpu-job.yaml

    If you are running the demo on a single node cluster, and do not have your own registry, you can add image to node image cache instead. For example, to import docker image to containerd cache:

    $ IMAGE_NAME=opencl-icd.tar
    $ docker save -o $IMAGE_NAME intel/intel-opencl-icd:devel
    $ ctr images import $IMAGE_NAME
    $ rm $IMAGE_NAME
  2. Create a job:

    $ kubectl apply -f ${INTEL_DEVICE_PLUGINS_SRC}/demo/intelgpu-job.yaml
    job.batch/intelgpu-demo-job created
  3. Review the job’s logs:

    $ kubectl get pods | fgrep intelgpu
    # substitute the 'xxxxx' below for the pod name listed in the above
    $ kubectl logs intelgpu-demo-job-xxxxx
    <log output>

    If the pod did not successfully launch, possibly because it could not obtain the requested GPU resource, it will be stuck in the Pending status:

    $ kubectl get pods
    NAME                      READY   STATUS    RESTARTS   AGE
    intelgpu-demo-job-xxxxx   0/1     Pending   0          8s

    This can be verified by checking the Events of the pod:

    $ kubectl describe pod intelgpu-demo-job-xxxxx
      Type     Reason            Age        From               Message
      ----     ------            ----       ----               -------
      Warning  FailedScheduling  <unknown>  default-scheduler  0/1 nodes are available: 1 Insufficient

Issues with media workloads on multi-GPU setups

Unlike with 3D & compute, and OneVPL media API, QSV (MediaSDK) & VA-API media APIs do not offer device discovery functionality for applications. There is nothing (e.g. environment variable) with which the default device could be overridden either.

As result, most (all?) media applications using VA-API or QSV, fail to locate the correct GPU device file unless it is the first (”renderD128”) one, or device file name is explictly specified with an application option.

Kubernetes device plugins expose only requested number of device files, and their naming matches host device file names (for several reasons unrelated to media). Therefore, on multi-GPU hosts, the only GPU device file mapped to the media container can be some other one than “renderD128”, and media applications using VA-API or QSV need to be explicitly told which one to use.

These options differ from application to application. Relevant FFmpeg options are documented here:

  • VA-API:

  • QSV:

Workaround for QSV and VA-API

Render device shell script locates and outputs the correct device file name. It can be added to the container and used to give device file name for the application.

Use it either from another script invoking the application, or directly from the Pod YAML command line. In latter case, it can be used either to add the device file name to the end of given command line, like this:

command: ["", "vainfo", "--display", "drm", "--device"]

=> /usr/bin/vainfo --display drm --device /dev/dri/renderDXXX

Or inline, like this:

command: ["/bin/sh", "-c",
          "vainfo --device $( 1) --display drm"

If device file name is needed for multiple commands, one can use shell variable:

command: ["/bin/sh", "-c",
          "dev=$( 1) && vainfo --device $dev && <more commands>"

With argument N, script outputs name of the Nth suitable GPU device file, which can be used when more than one GPU resource was requested.