This repository contains a framework for developing plugins for the Kubernetes device plugins framework, along with a number of device plugin implementations utilising that framework.
The v0.24 release is the latest feature release with its documentation available here.
Table of Contents
- Prerequisites
- Plugins
- Device Plugins Operator
- Demos
- Workload Authors
- Developers
- Running e2e Tests
- Supported Kubernetes versions
- Pre-built plugin images
- License
- Security
- Related code
- Helm charts
Prerequisites for building and running these device plugins include:
- Appropriate hardware
- A fully configured Kubernetes cluster
- A working Go environment, of at least version v1.16.
The below sections detail existing plugins developed using the framework.
The GPU device plugin provides access to Intel discrete (Xe) and integrated GPU HW device files.
The demo subdirectory contains both a GPU plugin demo video
and an OpenCL sample deployment (intelgpu-job.yaml).
The FPGA device plugin supports FPGA passthrough for the following hardware:
- Intel Arria 10
- Intel Stratix 10
The FPGA plugin comes as three parts.
- the device plugin
- the admission controller
- the CRIO-O prestart hook
Refer to each individual sub-components documentation for more details. Brief overviews of the sub-components are below.
The demo subdirectory contains a video showing deployment and use of the FPGA plugin. Sources relating to the demo can be found in the opae-nlb-demo subdirectory.
The FPGA device plugin is responsible for discovering and reporting FPGA
devices to kubelet.
The FPGA admission controller webhook is responsible for performing mapping from user-friendly function IDs to the Interface ID and Bitstream ID that are required for FPGA programming. It also implements access control by namespacing FPGA configuration information.
The FPGA prestart CRI-O hook performs discovery of the requested FPGA function bitstream and programs FPGA devices based on the environment variables in the workload description.
QAT device plugin
The QAT plugin supports device plugin for Intel QAT adapters, and includes code showing deployment via DPDK.
The demo subdirectory includes details of both a QAT DPDK demo and a QAT OpenSSL demo. Source for the OpenSSL demo can be found in the relevant subdirectory.
Details for integrating the QAT device plugin into Kata Containers can be found in the Kata Containers documentation repository.
The VPU device plugin supports Intel VCAC-A card (https://www.intel.com/content/dam/www/public/us/en/documents/datasheets/media-analytics-vcac-a-accelerator-card-by-celestica-datasheet.pdf) the card has:
- 1 Intel Core i3-7100U processor
- 12 MyriadX VPUs
- 8GB DDR4 memory
The demo subdirectory includes details of a OpenVINO deployment and use of the VPU plugin. Sources can be found in openvino-demo
The SGX device plugin allows workloads to use Intel SGX on platforms with SGX Flexible Launch Control enabled, e.g.,:
- 3rd Generation Intel® Xeon® Scalable Platform, code-named “Ice Lake”
- Intel® Xeon® E3
- Intel® NUC Kit NUC7CJYH
The SGX plugin comes in three parts.
The demo subdirectory contains a video showing the deployment and use of the SGX device plugin. Sources relating to the demo can be found in the sgx-sdk-demo and sgx-aesmd-demo subdirectories.
Brief overviews of the SGX sub-components are given below.
The SGX device plugin is responsible for discovering and reporting SGX
device nodes to kubelet.
Containers requesting SGX resources in the cluster should not use the device plugins resources directly.
The SGX admission webhook is responsible for performing Pod mutations based on the sgx.intel.com/quote-provider
pod annotation set by the user. The purpose of the webhook is to hide the details of setting the necessary
device resources and volume mounts for using SGX remote attestation in the cluster. Furthermore,
the SGX admission webhook is responsible for writing a pod/sandbox sgx.intel.com/epc annotation that is used by
Kata Containers to dynamically adjust its virtualized SGX encrypted page cache (EPC) bank(s) size.
The SGX admission webhook is available as part of Intel Device Plugin Operator or as a standalone SGX Admission webhook image.
The SGX EPC memory available on each node is registered as a Kubernetes extended resource using node-feature-discovery (NFD). A custom NFD source hook is installed as part of SGX device plugin operator deployment and NFD is configured to register the SGX EPC memory extended resource reported by the hook.
Containers requesting SGX EPC resources in the cluster use sgx.intel.com/epc resource which is of
type memory.
The DSA device plugin supports acceleration using the Intel Data Streaming accelerator(DSA).
The DLB device plugin supports Intel Dynamic Load Balancer accelerator(DLB).
The IAA device plugin supports acceleration using the Intel Analytics accelerator(IAA).
To simplify the deployment of the device plugins, a unified device plugins operator is implemented.
Currently the operator has support for the DSA, DLB, FPGA, GPU, IAA, QAT, SGX device plugins. Each device plugin has its own custom resource definition (CRD) and the corresponding controller that watches CRUD operations to those custom resources.
The Device plugins operator README gives the installation and usage details. The operator is also available via operatorhub.io and on Red Hat OpenShift Container Platform.
The demo subdirectory contains a number of demonstrations for a variety of the available plugins.
For workloads to get accesss to devices managed by the plugins, the
Pod spec must specify the hardware resources needed:
spec:
containers:
- name: demo-container
image: <registry>/<image>:<version>
resources:
limits:
<device namespace>/<resource>: X
The summary of resources available via plugins in this repository is given in a table below.
| Device Namespace | Registered Resource(s) | Example(s) |
|---|---|---|
dlb.intel.com |
pf or vf |
dlb-libdlb-demo-pod.yaml |
dsa.intel.com |
wq-user-[shared or dedicated] |
dsa-accel-config-demo-pod.yaml |
fpga.intel.com |
custom, see mappings | intelfpga-job.yaml |
gpu.intel.com |
i915 |
intelgpu-job.yaml |
iaa.intel.com |
wq-user-[shared or dedicated] |
iaa-qpl-demo-pod.yaml |
qat.intel.com |
generic or cy/dc/sym/asym |
crypto-perf-dpdk-pod-requesting-qat.yaml |
sgx.intel.com |
epc |
intelsgx-job.yaml |
vpu.intel.com |
hddl |
intelvpu-job.yaml |
For information on how to develop a new plugin using the framework, see the Developers Guide and the code in the device plugins pkg directory.
Currently the E2E tests require having a Kubernetes cluster already configured on the nodes with the hardware required by the device plugins. Also all the container images with the executables under test must be available in the cluster. Given these two conditions are satisfied one can run the tests with
$ go test -v ./test/e2e/...In case you want to run only certain tests, e.g. QAT ones, then run
$ go test -v ./test/e2e/... -args -ginkgo.focus "QAT"If you need to specify paths to your custom kubeconfig containing
embedded authentication info then add the -kubeconfig argument:
$ go test -v ./test/e2e/... -args -kubeconfig /path/to/kubeconfigThe full list of available options can be obtained with
$ go test ./test/e2e/... -args -helpAlso it is possible to run the tests which don't depend on hardware without a pre-configured Kubernetes cluster. Just make sure you have Kind installed on your host and run
$ make test-with-kind
The controller-runtime library provides a package for integration testing by
starting a local control plane. The package is called
envtest. The
operator uses this package for its integration testing.
Please have a look at envtest's documentation to set up it properly. But basically
you just need to have etcd and kube-apiserver binaries available on your
host. By default they are expected to be located at /usr/local/kubebuilder/bin.
But you can have it stored anywhere by setting the KUBEBUILDER_ASSETS
environment variable. So, given you have the binaries copied to
${HOME}/work/kubebuilder-assets to run the tests just enter
$ KUBEBUILDER_ASSETS=${HOME}/work/kubebuilder-assets make envtestReleases are made under the github releases area. Supported releases and matching Kubernetes versions are listed below:
| Branch | Kubernetes branch/version | Status |
|---|---|---|
| release-0.24 | Kubernetes 1.24 branch v1.24.x | supported |
| release-0.23 | Kubernetes 1.23 branch v1.23.x | supported |
| release-0.22 | Kubernetes 1.22 branch v1.22.x | supported |
| release-0.21 | Kubernetes 1.21 branch v1.21.x | unsupported |
| release-0.20 | Kubernetes 1.20 branch v1.20.x | unsupported |
| release-0.19 | Kubernetes 1.19 branch v1.19.x | unsupported |
| release-0.18 | Kubernetes 1.18 branch v1.18.x | unsupported |
| release-0.17 | Kubernetes 1.17 branch v1.17.x | unsupported |
| release-0.15 | Kubernetes 1.15 branch v1.15.x | unsupported |
| release-0.11 | Kubernetes 1.11 branch v1.11.x | unsupported |
Pre-built images of the plugins 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.
Note: the default deployment files and operators are configured with imagePullPolicy IfNotPresent and can be changed with scripts/set-image-pull-policy.sh.
All of the source code required to build intel-device-plugins-for-kubernetes
is available under Open Source licenses. The source code files identify external Go
modules used. Binaries are distributed as container images on
DockerHub. Those images contain license texts and source code under /licenses.
Reporting a Potential Security Vulnerability: If you have discovered potential security vulnerability in this project, please send an e-mail to [email protected]. Encrypt sensitive information using our PGP public key.
Please provide as much information as possible, including:
- The projects and versions affected
- Detailed description of the vulnerability
- Information on known exploits
A member of the Intel Product Security Team will review your e-mail and contact you to collaborate on resolving the issue. For more information on how Intel works to resolve security issues, see Vulnerability Handling Guidelines.
A related Intel SRIOV network device plugin can be found in this repository
The helm charts is one way of distributing Kubernetes resources of the device plugins framework.