gpu_plugin

README

Intel GPU device plugin for Kubernetes

Table of Contents

• Introduction
• Modes and Configuration Options
• Operation modes for different workload types
• Installation
  • Prerequisites
    • Drivers for discrete GPUs
      • Kernel driver
        • Intel DKMS packages
        • Upstream kernel
      • GPU Version
      • GPU Firmware
      • User-space drivers
    • Drivers for older (integrated) GPUs
  • Pre-built Images
    • Install to all nodes
    • Install to nodes with Intel GPUs with NFD
    • Install to nodes with NFD, Monitoring and Shared-dev
    • Install to nodes with Intel GPUs with Fractional resources
      • Fractional resources details
  • Verify Plugin Registration
• Testing and Demos
• Labels created by GPU plugin
• SR-IOV use with the plugin
• Issues with media workloads on multi-GPU setups
  • Workaround for QSV and VA-API

Introduction

Intel GPU plugin facilitates Kubernetes workload offloading by providing access to discrete (including Intel® Data Center GPU Flex & Max 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. For shared-dev-num > 1: balanced mode spreads workloads among GPU devices, packed mode fills one GPU fully before moving to next, and none selects first available device from kubelet. Default is none. Allocation policy does not have an 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.

Operation modes for different workload types

Intel GPU-plugin supports a few different operation modes. Depending on the workloads the cluster is running, some modes make more sense than others. Below is a table that explains the differences between the modes and suggests workload types for each mode. Mode selection applies to the whole GPU plugin deployment, so it is a cluster wide decision.

Mode	Sharing	Intended workloads	Suitable for time critical workloads
shared-dev-num == 1	No, 1 container per GPU	Workloads using all GPU capacity, e.g. AI training	Yes
shared-dev-num > 1	Yes, >1 containers per GPU	(Batch) workloads using only part of GPU resources, e.g. inference, media transcode/analytics, or CPU bound GPU workloads	No
shared-dev-num > 1 && resource-management	Yes and no, 1>= containers per GPU	Any. For best results, all workloads should declare their expected GPU resource usage (memory, millicores). Requires GAS. See also fractional use	Yes. 1000 millicores = exclusive GPU usage. See note below.

Note: Exclusive GPU usage with >=1000 millicores requires that also all other GPU containers specify (non-zero) millicores resource usage.

Installation

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.

Prerequisites

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

Drivers for discrete GPUs

Note: Kernel (on host) and user-space drivers (in containers) should be installed from the same repository as there are some differences between DKMS and upstream GPU driver uAPI.

Kernel driver

Intel DKMS packages

i915 GPU driver DKMS^dkms package is recommended for Intel discrete GPUs, until their support in upstream is complete. DKMS package(s) 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

Upstream kernel

Upstream Linux kernel 6.2 or newer is needed for Intel discrete GPU support. For now, upstream kernel is still missing support for a few of the features available in DKMS kernels (e.g. Level-Zero Sysman API GPU error counters).

GPU Version

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

GPU Firmware

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

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: 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

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.

Note: Add --dry-run=client -o yaml to the kubectl commands below to visualize the yaml content being applied.

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 'https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin?ref=<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 '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>'

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 '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/monitoring_shared-dev_nfd/?ref=<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 - 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/fractional_resources?ref=<RELEASE_VERSION>'

Fractional resources details

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 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[*]}{.metadata.name}{'\n'}{' i915: '}{.status.allocatable.gpu\.intel\.com/i915}{'\n'}"
master
 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 -n=k8s.io 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
   ...
   Events:
     Type     Reason            Age        From               Message
     ----     ------            ----       ----               -------
     Warning  FailedScheduling  <unknown>  default-scheduler  0/1 nodes are available: 1 Insufficient gpu.intel.com/i915.
   

Labels created by GPU plugin

If installed with NFD and started with resource-management, plugin will export a set of labels for the node. For detailed info, see labeling documentation.

SR-IOV use with the plugin

GPU plugin does not setup SR-IOV. It has to be configured by the cluster admin.

GPU plugin does however support provisioning Virtual Functions (VFs) to containers for a SR-IOV enabled GPU. When the plugin detects a GPU with SR-IOV VFs configured, it will only provision the VFs and leaves the PF device on the host.

Issues with media workloads on multi-GPU setups

OneVPL media API, 3D and compute APIs provide device discovery functionality for applications and work fine in multi-GPU setups. VA-API and legacy QSV (MediaSDK) media APIs do not, and do not provide (e.g. environment variable) override for their default device file.

As result, 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 differ from "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

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: ["render-device.sh", "vainfo", "--display", "drm", "--device"]

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

Or 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.