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Intel GPU device plugin for Kubernetes

Table of Contents

Introduction

The GPU device plugin for Kubernetes supports acceleration using the following Intel GPU hardware families:

  • Integrated GPUs within Intel Core processors
  • Integrated GPUs within Intel Xeon processors
  • Intel Visual Compute Accelerator (Intel VCA)

The GPU plugin facilitates offloading the processing of computation intensive workloads to GPU hardware. There are two primary use cases:

  • hardware vendor-independent acceleration using the Intel Media SDK
  • OpenCL code tuned for high end Intel devices.

For example, the Intel Media SDK can offload video transcoding operations, and the OpenCL libraries can provide computation acceleration for Intel GPUs

The device plugin can also be used with GVT-d device passthrough and acceleration.

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 master 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}

Verify node kubelet config

Every node that will be running the gpu plugin must have the kubelet device-plugins configured. For each node, check that the kubelet device plugin socket exists:

$ ls /var/lib/kubelet/device-plugins/kubelet.sock
/var/lib/kubelet/device-plugins/kubelet.sock

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

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.