gpu_plugin

README

Intel GPU device plugin for Kubernetes

Table of Contents

• Introduction
  • Configuration options
• Installation
  • Deploy with pre-built container image
  • Getting the source code
  • Deploying as a DaemonSet
    • Build the plugin image
    • Deploy plugin DaemonSet
    • Fractional resources
  • Deploy by hand
    • Build the plugin
    • Run the plugin as administrator
  • Verify plugin registration
  • Testing the plugin

Introduction

Intel GPU plugin facilitates Kubernetes workload offloading by providing access to Intel discrete (Xe) and integrated GPU HW device files.

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.

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

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.

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.

Deploy with pre-built container image

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

$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin?ref=<RELEASE_VERSION>
daemonset.apps/intel-gpu-plugin created

Where <RELEASE_VERSION> needs to be substituted with the desired release version, e.g. v0.18.0.

Alternatively, if your cluster runs Node Feature Discovery, you can deploy the device plugin only on nodes with Intel GPU. The nfd_labeled_nodes kustomization adds the nodeSelector to the DaemonSet:

$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/gpu_plugin/overlays/nfd_labeled_nodes?ref=<RELEASE_VERSION>
daemonset.apps/intel-gpu-plugin created

Nothing else is needed. But if you want to deploy a customized version of the plugin read further.

Getting the source code

$ export INTEL_DEVICE_PLUGINS_SRC=/path/to/intel-device-plugins-for-kubernetes
$ git clone https://github.com/intel/intel-device-plugins-for-kubernetes ${INTEL_DEVICE_PLUGINS_SRC}

Deploying as a DaemonSet

To deploy the gpu plugin as a daemonset, you first need to build a container image for the plugin and ensure that is visible to your nodes.

Build the plugin image

The following will use docker to build a local container image called intel/intel-gpu-plugin with the tag devel.

The image build tool can be changed from the default docker by setting the BUILDER argument to the Makefile.

$ cd ${INTEL_DEVICE_PLUGINS_SRC}
$ make intel-gpu-plugin
...
Successfully tagged intel/intel-gpu-plugin:devel

Deploy plugin DaemonSet

You can then use the example DaemonSet YAML file provided to deploy the plugin. The default kustomization that deploys the YAML as is:

$ kubectl apply -k deployments/gpu_plugin
daemonset.apps/intel-gpu-plugin created

Alternatively, if your cluster runs Node Feature Discovery, you can deploy the device plugin only on nodes with Intel GPU. The nfd_labeled_nodes kustomization adds the nodeSelector to the DaemonSet:

$ kubectl apply -k deployments/gpu_plugin/overlays/nfd_labeled_nodes
daemonset.apps/intel-gpu-plugin created

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.

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:

$ kubectl apply -k deployments/gpu_plugin/overlays/fractional_resources
serviceaccount/resource-reader-sa created
clusterrole.rbac.authorization.k8s.io/resource-reader created
clusterrolebinding.rbac.authorization.k8s.io/resource-reader-rb created
daemonset.apps/intel-gpu-plugin created

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.

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.

Deploy by hand

For development purposes, it is sometimes convenient to deploy the plugin 'by hand' on a node. In this case, you do not need to build the complete container image, and can build just the plugin.

Build the plugin

First we build the plugin:

$ cd ${INTEL_DEVICE_PLUGINS_SRC}
$ make gpu_plugin

Run the plugin as administrator

Now we can run the plugin directly on the node:

$ sudo -E ${INTEL_DEVICE_PLUGINS_SRC}/cmd/gpu_plugin/gpu_plugin
device-plugin start server at: /var/lib/kubelet/device-plugins/gpu.intel.com-i915.sock
device-plugin registered

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 the plugin

We can test the plugin is working by deploying the provided example OpenCL image with FFT offload enabled.

1. Build a Docker image with an example program offloading FFT computations to GPU:

   $ cd ${INTEL_DEVICE_PLUGINS_SRC}/demo
   $ ./build-image.sh ubuntu-demo-opencl
   ...
   Successfully tagged ubuntu-demo-opencl:devel
   

2. Create a job running unit tests off the local Docker image:

   $ 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
   + WORK_DIR=/root/6-1/fft
   + cd /root/6-1/fft
   + ./fft
   + uprightdiff --format json output.pgm /expected.pgm diff.pgm
   + cat diff.json
   + jq .modifiedArea
   + MODIFIED_AREA=0
   + [ 0 -gt 10 ]
   + echo Success
   Success
   

   If the pod did not successfully launch, possibly because it could not obtain the 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.