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operator: update usage guidelines

Browse Source 
As the operator container image is available from a registry, we should
guide users to use it rather than build and deploy it locally.

Further, drop (un)deploy-operator targets in favor of simply using
kubectl for deployment.

Signed-off-by: Mikko Ylinen <mikko.ylinen@intel.com>
This commit is contained in:
Mikko Ylinen 2021-03-30 08:28:29 +03:00
parent 60f23f40f0
commit 97bcecda04
4 changed files with 36 additions and 34 deletions
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12
DEVEL.md
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@ -179,7 +179,17 @@ $ make test
and fix all new compilation issues.
## How to publish a new version of the Intel Device Plugins operator
## How to work with Intel Device Plugins operator modifications
There are few useful steps when working with changes to Device Plugins CRDs and controllers:
1. Install controller-gen: `GO111MODULE=on go get -u sigs.k8s.io/controller-tools/cmd/controller-gen@<release ver>, e.g, v0.4.1`
2. Generate CRD and Webhook artifacts: `make generate`
3. Test local changes using [envtest](https://book.kubebuilder.io/reference/envtest.html): `make envtest`
4. Build a custom operator image: `make intel-deviceplugin-operator`
5. (Un)deploy operator: `kubectl [apply|delete] -k deployments/operator/default`
## How to publish a new version of the Intel Device Plugins operator to operatorhub.io
Generate package manifests with:
```

11
Makefile
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@ -99,15 +99,6 @@ $(cmds):
build: $(cmds)
deploy-operator: operator generate
kubectl apply -k deployments/operator/default
undeploy-operator:
kubectl delete -k deployments/operator/default
run-operator: deploy-operator
./cmd/operator/operator
.PHONY: bundle
bundle:
$(OPERATOR_SDK) generate kustomize manifests -q --input-dir $(OLM_MANIFESTS) --output-dir $(OLM_MANIFESTS) --apis-dir pkg/apis
@ -173,7 +164,7 @@ check-github-actions:
jq -e '$(images_json) - .jobs.image.strategy.matrix.image == []' > /dev/null || \
(echo "Make sure all images are listed in .github/workflows/ci.yaml"; exit 1)
.PHONY: all format test lint build images $(cmds) $(images) lock-images vendor pre-pull set-version check-github-actions run-operator envtest deploy-operator undeploy-operator
.PHONY: all format test lint build images $(cmds) $(images) lock-images vendor pre-pull set-version check-github-actions envtest
SPHINXOPTS =
SPHINXBUILD = sphinx-build

28
README.md
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@ -173,30 +173,14 @@ The [DSA device plugin](cmd/dsa_plugin/README.md) supports acceleration using th
## Device Plugins Operator
Currently the operator has support for the QAT, GPU, FPGA, SGX and DSA device plugins:
it validates container image references and extends reported statuses.
To simplify the deployment of the device plugins, a unified device plugins operator is implemented.
To run an operator instance in the container run
Currently the operator has support for the QAT, GPU, FPGA, SGX and DSA device plugins. Each
device plugin has its own custom resource definition (CRD) and the corresponding controller that
watches CRUD operations to those custom resources.
```bash
$ kubectl apply -f https://github.com/jetstack/cert-manager/releases/download/v1.2.0/cert-manager.yaml
$ make deploy-operator
```
Then deploy your device plugin by applying its custom resource, e.g.
`GpuDevicePlugin` with
```bash
$ kubectl apply -f ./deployments/operator/samples/deviceplugin_v1_gpudeviceplugin.yaml
```
Observe it is up and running:
```bash
$ kubectl get GpuDevicePlugin
NAME DESIRED READY NODE SELECTOR AGE
gpudeviceplugin-sample 1 1 5s
```
The [Device plugins operator README](cmd/operator/README.md) gives the installation and usage
details. The operator is also available via [operatorhub.io](https://operatorhub.io/operator/intel-device-plugins-operator).
## Demos

19
cmd/operator/README.md
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@ -75,12 +75,29 @@ $ export no_proxy=$no_proxy,.svc,.svc.cluster.local
Finally deploy the operator itself:
```
$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/operator/default?ref=main
$ 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](/deployments/operator/samples/).
## Usage
Deploy your device plugin by applying its custom resource, e.g.
`GpuDevicePlugin` with
```bash
$ kubectl apply -f https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/operator/samples/deviceplugin_v1_gpudeviceplugin.yaml
```
Observe it is up and running:
```bash
$ kubectl get GpuDevicePlugin
NAME DESIRED READY NODE SELECTOR AGE
gpudeviceplugin-sample 1 1 5s
```
## Known issues
When the operator is run with leader election enabled, that is with the option