Merge branch 'main' into issue/424

.github/workflows/sync_dashboard_crds.yaml

@@ -1,29 +1,41 @@
-# Opens a PR in the opendatahub-operator repo when crds are updated in the dashboard repo
-name: Sync Dashboard CRDs
+# Opens a PR in the opendatahub-operator repo to sync crds from the dashboard repo
+name: Sync Dashboard CRDs for Operator v2.x
 
 # Await dispatch from dashboard repo that crds have been modified
 on:
-  workflow_dispatch
+  # Triggers the workflow every Sat at 22pm UTC am 0 22 * * 6
+  schedule:
+    - cron: "0 22 * * 6"
+  workflow_dispatch:
+    inputs:
+      dashboard_label:
+        default: "main"
+        description: "Label from odh-dashboard to sync CRD from, default to use main branch"
+      source_branch:
+        default: "main"
+        description: "branch of opendatahub-operator to checkout from"
 
 jobs:
   dashboard-sync:
     runs-on: ubuntu-latest
     steps:
-      - name: Git checkout
+      - name: Checkout operator source code on master branch
         uses: actions/checkout@v3
         with:
-          fetch-depth: '0'
+          fetch-depth: '1'
+          ref: ${{ github.event.inputs.source_branch }}
       - name: Gather files
         shell: bash
         run: |
-          cd ${{ github.workspace }}/config/crd/dashboard-crds
-          svn export --force https://github.com/opendatahub-io/odh-dashboard/branches/main/manifests/crd .
+          cd ${{ github.workspace }}/bundle/manifests
+          svn export --force "https://github.com/opendatahub-io/odh-dashboard/tags/${{ github.event.inputs.dashboard_label }}/manifests/crd" .
           rm kustomization.yaml
       - name: Create Pull Request
         uses: peter-evans/create-pull-request@v4.2.3
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
-          branch: dashboard-sync
-          commit-message: Automated Change
-          title: Sync operator crds with dashboard crds
-          body: This is an automated pull request to sync the operator crds with dashboard crds.
+          commit-message: Automated Change by GitHub Action 'Sync Dashboard CRDs for Operator v2.x'
+          delete-branch: true
+          title: Sync operator v2.X dashboard CRDs from 'odh-dashboard' manifests CRDs
+          body: This is an automated PR to sync the operator CRDs with dashboard CRDs.
+          signoff: true

Makefile

@@ -4,7 +4,7 @@
 # - use the VERSION as arg of the bundle target (e.g make bundle VERSION=0.0.2)
 # - use environment variables to overwrite this value (e.g export VERSION=0.0.2)
 IMAGE_OWNER ?= opendatahub
-VERSION ?= 2.0.0
+VERSION ?= 2.1.0
 # IMAGE_TAG_BASE defines the opendatahub.io namespace and part of the image name for remote images.
 # This variable is used to construct full image tags for bundle and catalog images.
 #

config/manifests/bases/opendatahub-operator.clusterserviceversion.yaml

@@ -3,11 +3,52 @@ kind: ClusterServiceVersion
 metadata:
   annotations:
     alm-examples: '[]'
-    capabilities: Basic Install
+    capabilities: Full Lifecycle
+    categories: AI/Machine Learning, Big Data
+    certified: "False"
+    containerImage: quay.io/opendatahub/opendatahub-operator:v2.1.0
+    createdAt: "2023-8-23T00:00:00Z"
     olm.skipRange: '>=1.0.0 <2.0.0'
-    operatorframework.io/initialization-resource: "{ \n  \"apiVersion\": \"datasciencecluster.opendatahub.io/v1alpha1\",\n
-      \ \"kind\": \"DataScienceCluster\", \n  \"metadata\":{ \n    \"name\": \"default\"\n
-      \ },\n  \"spec\": {}\n}"
+    operatorframework.io/initialization-resource: |-
+      {
+        "apiVersion": "datasciencecluster.opendatahub.io/v1alpha1",
+        "kind": "DataScienceCluster",
+        "metadata": {
+          "name": "default",
+          "labels": {
+            "app.kubernetes.io/name": "datasciencecluster",
+            "app.kubernetes.io/instance": "default",
+            "app.kubernetes.io/part-of": "opendatahub-operator",
+            "app.kubernetes.io/managed-by": "kustomize",
+            "app.kubernetes.io/created-by": "opendatahub-operator"
+          }
+        },
+        "spec": {
+          "components": {
+            "codeflare": {
+              "enabled": false
+            },
+            "dashboard": {
+              "enabled": true
+            },
+            "datasciencepipelines": {
+              "enabled": true
+            },
+            "kserve": {
+              "enabled": false
+            },
+            "modelmeshserving": {
+              "enabled": true
+            },
+            "ray": {
+              "enabled": false
+            },
+            "workbenches": {
+              "enabled": true
+            }
+          }
+        }
+      }
     operators.operatorframework.io/internal-objects: '[dscinitialization.opendatahub.io]'
     repository: https://github.com/opendatahub-io/opendatahub-operator
   name: opendatahub-operator.v0.0.0
@@ -76,9 +117,18 @@ spec:
   - kserve
   - distributed-workloads
   links:
-  - name: Opendatahub Operator
-    url: https://opendatahub.io/
+  - name: Open Data Hub
+    url: https://opendatahub.io
+  - name: Open Data Hub Community
+    url: https://github.com/opendatahub-io/opendatahub-community
+  maintainers:
+  - email: opendatahub@redhat.com
+    name: Open Data Hub Maintainers
   maturity: alpha
+  minKubeVersion: 1.22.0
   provider:
     name: ODH
+  selector:
+    matchLabels:
+      component: opendatahub-operator
   version: 0.0.1

config/manifests/description-patch.yml

@@ -15,5 +15,6 @@ spec:
     * Jupyter Notebooks - JupyterLab notebook that provide Python support for GPU workloads
     * Data Science Pipelines - Pipeline solution for end to end MLOps workflows that support the Kubeflow Pipelines SDK and Tekton
     * Model Mesh - ModelMesh Serving is the Controller for managing ModelMesh, a general-purpose model serving management/routing layer
-    * Distributed Workloads(Incubation) - Stack built to make managing distributed compute infrastructure in the cloud easy and intuitive for Data Scientists
+    * Distributed Workloads(Incubation) - Stack built to make managing distributed compute infrastructure in the cloud easy and intuitive for Data Scientists. This stack consists of two components 
+                                          Codeflare and KubeRay.
     * Kserve (Incubation) - Kserve is the Controller for for serving machine learning (ML) models on arbitrary frameworks

config/samples/datasciencecluster_v1alpha1_datasciencecluster.yaml

@@ -13,14 +13,14 @@ spec:
     codeflare:
       enabled: false
     dashboard:
-      enabled: false
+      enabled: true
     datasciencepipelines:
-      enabled: false
+      enabled: true
     kserve:
       enabled: false
     modelmeshserving:
-      enabled: false
+      enabled: true
     ray:
       enabled: false
     workbenches:
-      enabled: false
+      enabled: true