Intel Device Plugins Operator
Table of Contents
Intel Device Plugins Operator is a Kubernetes custom controller whose goal is to serve the installation and lifecycle management of Intel device plugins for Kubernetes. It provides a single point of control for GPU, QAT, SGX, FPGA, DSA and DLB devices to a cluster administrators.
The default operator deployment depends on NFD and cert-manager. Those components have to be installed to the cluster before the operator can be deployed.
Note: Operator can also be installed via Helm charts. See INSTALL.md for details.
Install NFD (if it’s not already installed) and node labelling rules (requires NFD v0.13+):
# deploy NFD
$ kubectl apply -k 'https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd?ref=<RELEASE_VERSION>'
# deploy NodeFeatureRules
$ kubectl apply -k 'https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/nfd/overlays/node-feature-rules?ref=<RELEASE_VERSION>'
Make sure both NFD master and worker pods are running:
$ kubectl get pods -n node-feature-discovery
NAME READY STATUS RESTARTS AGE
nfd-master-599c58dffc-9wql4 1/1 Running 0 25h
nfd-worker-qqq4h 1/1 Running 0 25h
Note that labelling is not performed immediately. Give NFD 1 minute to pick up the rules and label nodes.
As a result all found devices should have correspondent labels, e.g. for Intel DLB devices the label is
$ kubectl get no -o json | jq .items.metadata.labels |grep intel.feature.node.kubernetes.io/dlb
Full list of labels can be found in the deployments/operator/samples directory:
$ grep -r feature.node.kubernetes.io/ deployments/operator/samples/
deployments/operator/samples/deviceplugin_v1_dlbdeviceplugin.yaml: intel.feature.node.kubernetes.io/dlb: 'true'
deployments/operator/samples/deviceplugin_v1_qatdeviceplugin.yaml: intel.feature.node.kubernetes.io/qat: 'true'
deployments/operator/samples/deviceplugin_v1_sgxdeviceplugin.yaml: intel.feature.node.kubernetes.io/sgx: 'true'
deployments/operator/samples/deviceplugin_v1_gpudeviceplugin.yaml: intel.feature.node.kubernetes.io/gpu: "true"
deployments/operator/samples/deviceplugin_v1_fpgadeviceplugin.yaml: intel.feature.node.kubernetes.io/fpga-arria10: 'true'
deployments/operator/samples/deviceplugin_v1_dsadeviceplugin.yaml: intel.feature.node.kubernetes.io/dsa: 'true'
Make sure all the pods in the
cert-manager namespace are up and running:
$ kubectl get pods -n cert-manager
NAME READY STATUS RESTARTS AGE
cert-manager-7747db9d88-bd2nl 1/1 Running 0 21d
cert-manager-cainjector-87c85c6ff-59sb5 1/1 Running 0 21d
cert-manager-webhook-64dc9fff44-29cfc 1/1 Running 0 21d
Device Plugin Operator
Finally deploy the operator itself:
$ kubectl apply -k 'https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/operator/default?ref=<RELEASE_VERSION>'
Now you can deploy the device plugins by creating corresponding custom resources. The samples for them are available here.
Device Plugin Custom Resource
Deploy your device plugin by applying its custom resource, e.g.
$ kubectl apply -f https://raw.githubusercontent.com/intel/intel-device-plugins-for-kubernetes/main/deployments/operator/samples/deviceplugin_v1_gpudeviceplugin.yaml
Observe it is up and running:
$ kubectl get GpuDevicePlugin
NAME DESIRED READY NODE SELECTOR AGE
gpudeviceplugin-sample 1 1 5s
NOTE: Intel Device Plugin Operator supports multiple custom resources per Kind (QAT, DSA, etc.). With multiple custom resources and different
nodeSelectors, it is possible to customize device plugin configuration per node or per group of nodes. See also known issues.
The upgrade of the deployed plugins can be done by simply installing a new release of the operator.
The operator auto-upgrades operator-managed plugins (CR images and thus corresponding deployed daemonsets) to the current release of the operator.
0.28.0 release, each version of the operator can have a set of images in
deployments/operator/manager/manager.yaml as env variables.
When env variables are set for specific plugins (and their initcontainers), plugins are upgraded to the images set as env variables and all user input is ignored.
The name of env variables is capitalized image with ‘_SHA’ ending (e.g. in case of the image for
intel-sgx-plugin, the env variable is
The value of env variables is the full path of the image (e.g.
Limiting Supported Devices
In order to limit the deployment to a specific device type,
use one of kustomizations under
For example, to limit the deployment to FPGA, use:
$ kubectl apply -k deployments/operator/device/fpga
Operator also supports deployments with multiple selected device types.
In this case, create a new kustomization with the necessary resources
that passes the desired device types to the operator using
command line argument multiple times.
Multiple Custom Resources
With multiple custom resources,
nodeSelector has to be carefully set to avoid device plugin DaemonSet getting deployed multiple times on the same node, as operator does not check or prevent this. Multiple plugins managing same resource on a node can cause invalid behavior and/or duplicate device resources on node.
Cluster behind a proxy
If your cluster operates behind a corporate proxy make sure that the API server is configured not to send requests to cluster services through the proxy. You can check that with the following command:
$ kubectl describe pod kube-apiserver --namespace kube-system | grep -i no_proxy | grep "\.svc"
In case there’s no output and your cluster was deployed with
/etc/kubernetes/manifests/kube-apiserver.yaml at the control plane nodes and
.svc.cluster.local to the
no_proxy environment variable:
- name: http_proxy
- name: https_proxy
- name: no_proxy
Note: To build clusters using
kubeadm with the right
no_proxy settings from the very beginning,
set the cluster service names to
$ export no_proxy=$no_proxy,.svc,.svc.cluster.local
Leader election enabled
When the operator is run with leader election enabled, that is with the option
--leader-elect, make sure the cluster is not overloaded with excessive
number of pods. Otherwise a heart beat used by the leader election code may trigger
a timeout and crash. We are going to use different clients for the controller and
leader election code to alleviate the issue. See more details in
In case the deployment is limited to specific device type(s), the CRDs for other device types are still created, but no controllers for them are registered.