Table of Contents
- Introduction
- Modes and Configuration Options
- Installation
- Testing and Demos
- Issues with media workloads on multi-GPU setups
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.
| 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
/sys/class/drm/card0/device/uevent:DRIVER=i915
/sys/class/drm/card1/device/uevent:DRIVER=i915
i915 GPU driver DKMS1 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:
https://dgpu-docs.intel.com/installation-guides/index.html
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:
https://www.kernel.org/doc/html/latest/gpu/rfc/index.html
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: https://gitlab.freedesktop.org/mesa/mesa/-/blob/main/include/pci_ids/iris_pci_ids.h
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:
https://git.kernel.org/pub/scm/linux/kernel/git/firmware/linux-firmware.git/tree/i915
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: https://dgpu-docs.intel.com/installation-guides/index.html
Example container is listed in Testing and demos.
Validation status against upstream kernel is listed in the user-space drivers release notes:
- Media driver: https://github.com/intel/media-driver/releases
- Compute driver: https://github.com/intel/compute-runtime/releases
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 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.
Simplest option to enable use of Intel GPUs in Kubernetes Pods.
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin?ref=<RELEASE_VERSION>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 https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd?ref=<RELEASE_VERSION>
# Create NodeFeatureRules for detecting GPUs on nodes
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd/overlays/node-feature-rules?ref=<RELEASE_VERSION>
# Create GPU plugin daemonset
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin/overlays/nfd_labeled_nodes?ref=<RELEASE_VERSION>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 https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd/overlays/gpu?ref=<RELEASE_VERSION>
# Create NodeFeatureRules for detecting GPUs on nodes
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd/overlays/node-feature-rules?ref=<RELEASE_VERSION>
# Create GPU plugin daemonset
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin/overlays/fractional_resources?ref=<RELEASE_VERSION>Usage of these fractional GPU resources requires that the cluster has node
extended resources with the name prefix gpu.intel.com/. 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
securityContextmust be configured with appropriaterunAsUser/runAsGroup.
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[*]}{.metadata.name}{'\n'}{' i915: '}{.status.allocatable.gpu\.intel\.com/i915}{'\n'}"
master
i915: 1The 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).
-
Make the image available to the cluster:
Build image:
$ make intel-opencl-icd
Tag and push the
intel-opencl-icdimage to a repository available in the cluster. Then modify theintelgpu-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 -n=k8s.io images import $IMAGE_NAME $ rm $IMAGE_NAME
-
Create a job:
$ kubectl apply -f ${INTEL_DEVICE_PLUGINS_SRC}/demo/intelgpu-job.yaml job.batch/intelgpu-demo-job created -
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
Pendingstatus:$ 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 ... Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning FailedScheduling <unknown> default-scheduler 0/1 nodes are available: 1 Insufficient gpu.intel.com/i915.
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: https://trac.ffmpeg.org/wiki/Hardware/VAAPI
- QSV: https://github.com/Intel-Media-SDK/MediaSDK/wiki/FFmpeg-QSV-Multi-GPU-Selection-on-Linux
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: ["render-device.sh", "vainfo", "--display", "drm", "--device"]
=> /usr/bin/vainfo --display drm --device /dev/dri/renderDXXXOr inline, like this:
command: ["/bin/sh", "-c",
"vainfo --device $(render-device.sh 1) --display drm"
]If device file name is needed for multiple commands, one can use shell variable:
command: ["/bin/sh", "-c",
"dev=$(render-device.sh 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.