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Run E2E tests for codeflare operator #40441

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Run E2E tests for codeflare operator #40441

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66 changes: 66 additions & 0 deletions ...onfig/project-codeflare/codeflare-operator/project-codeflare-codeflare-operator-main.yaml
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Expand Up @@ -28,6 +28,72 @@ tests:
requests:
cpu: 100m
memory: 200Mi
- as: codeflare-operator-e2e
commands: |
podman run -d -p 5000:5000 --name registry registry:2.8.1
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If possible, we'd want to rely on the OpenShift internal container image registry. So instead of starting a local one, we'd only need to expose and login to the internal registry, e.g.:

$ oc patch configs.imageregistry.operator.openshift.io/cluster --patch '{"spec":{"defaultRoute":true}}' --type=merge
$ podman login -u kubeadmin -p $(oc whoami -t) $(oc registry info)

export REGISTRY_ADDRESS=$(hostname -i):5000

KUBERAY_VERSION=$(make get-kuberay-version)
echo Deploying KubeRay ${KUBERAY_VERSION}
kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}&timeout=90s"
echo Deploying CodeFlare operator
IMG="${REGISTRY_ADDRESS}"/codeflare-operator
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A nitpick, can you put some version to the image tag?

make image-push -e IMG="${IMG}"
make deploy -e IMG="${IMG}"
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If we rely on the OpenShift internal container registry, it'd need to be the internally addressable image.

kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
echo Deploying MCAD controller
kubectl create ns codeflare-system
cat <<EOF | kubectl apply -n codeflare-system -f -
apiVersion: codeflare.codeflare.dev/v1alpha1
kind: MCAD
metadata:
name: mcad
spec:
controllerResources: {}
EOF
cat <<EOF | kubectl apply -n codeflare-system -f -
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: mcad-controller-rayclusters
rules:
- apiGroups:
- ray.io
resources:
- rayclusters
- rayclusters/finalizers
- rayclusters/status
verbs:
- get
- list
- watch
- create
- update
- patch
- delete
EOF
cat <<EOF | kubectl apply -n codeflare-system -f -
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: mcad-controller-rayclusters
subjects:
- kind: ServiceAccount
name: mcad-controller-mcad
namespace: codeflare-system
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: mcad-controller-rayclusters
EOF
kubectl wait --timeout=120s --for=condition=Available=true deployment -n codeflare-system mcad-controller-mcad

GOFLAGS="" make test-e2e
from: test-bin
resources:
requests:
cpu: 100m
memory: 200Mi
Comment on lines +95 to +96
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Based on https://github.com/opendatahub-io/distributed-workloads/blob/main/Quick-Start.md I would consider 4 CPU and at least 4Gi memory (considering just MCAD, Ray and CodeFlare operator now).

workflow: hypershift-hostedcluster-workflow
zz_generated_metadata:
branch: main
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