Add kfp-tekton 1.3.0 release template (#1011)

Makefile

@@ -16,7 +16,7 @@
 #  - The help target was derived from https://stackoverflow.com/a/35730328/5601796
 
 VENV ?= .venv
-KFP_TEKTON_RELEASE ?= v1.2.1
+KFP_TEKTON_RELEASE ?= v1.3.0
 export VIRTUAL_ENV := $(abspath ${VENV})
 export PATH := ${VIRTUAL_ENV}/bin:${PATH}
 DOCKER_REGISTRY ?= aipipeline

VERSION

@@ -1 +1 @@
-1.2.1
+1.3.0

guides/kfp_tekton_install.md

@@ -53,15 +53,15 @@ To install the standalone Kubeflow Pipelines with Tekton, run the following step
          -p '{"data":{"enable-custom-tasks": "true"}}'
    ```
 
-3. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.2.1` [custom resource definitions](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/)(CRDs).
+3. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.3.0` [custom resource definitions](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/)(CRDs).
    > Note: You can ignore the error `no matches for kind "Application" in version "app.k8s.io/v1beta1"` since it's a warning saying `application` CRD is not yet ready.
     ```shell
-    kubectl apply --selector kubeflow/crd-install=true -f install/v1.2.1/kfp-tekton.yaml
+    kubectl apply --selector kubeflow/crd-install=true -f install/v1.3.0/kfp-tekton.yaml
     ```
 
-4. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.2.1` deployment
+4. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.3.0` deployment
     ```shell
-    kubectl apply -f install/v1.2.1/kfp-tekton.yaml
+    kubectl apply -f install/v1.3.0/kfp-tekton.yaml
     ```
 
 5. Then, if you want to expose the Kubeflow Pipelines endpoint outside the cluster, run the following commands:
@@ -91,7 +91,7 @@ To install the standalone Kubeflow Pipelines with Tekton, run the following step
 
 1. Follow the [Kubeflow install instructions](https://www.kubeflow.org/docs/ibm/deploy/install-kubeflow-on-iks/#kubeflow-installation)
    to install the entire Kubeflow stack with `kfp-tekton`.
-   Kubeflow `v1.5.0` uses Tekton `v0.30.0` and `kfp-tekton` `v1.1.1`. <!-- TODO update-->
+   Kubeflow `v1.6.0` uses Tekton `v0.31.4` and `kfp-tekton` `v1.2.1`. <!-- TODO update-->
 
 2. Visit [KFP Tekton User Guide](/guides/kfp-user-guide) and start learning how to use Kubeflow pipeline.
 

manifests/kustomize/base/cache/kustomization.yaml

@@ -11,4 +11,4 @@ commonLabels:
 images:
   - name: gcr.io/ml-pipeline/cache-server
     newName: docker.io/aipipeline/cache-server
-    newTag: 1.2.1
+    newTag: 1.3.0

manifests/kustomize/base/pipeline/kustomization.yaml

@@ -43,20 +43,20 @@ patchesStrategicMerge:
 images:
   - name: gcr.io/ml-pipeline/api-server
     newName: docker.io/aipipeline/api-server
-    newTag: 1.2.1
+    newTag: 1.3.0
   - name: gcr.io/ml-pipeline/persistenceagent
     newName: docker.io/aipipeline/persistenceagent
-    newTag: 1.2.1
+    newTag: 1.3.0
   - name: gcr.io/ml-pipeline/scheduledworkflow
     newName: docker.io/aipipeline/scheduledworkflow
-    newTag: 1.2.1
+    newTag: 1.3.0
   - name: gcr.io/ml-pipeline/frontend
     newName: docker.io/aipipeline/frontend
-    newTag: 1.2.1
+    newTag: 1.3.0
   - name: gcr.io/ml-pipeline/viewer-crd-controller
     newTag: 1.8.1
   - name: gcr.io/ml-pipeline/visualization-server
     newTag: 1.8.1
   - name: gcr.io/ml-pipeline/metadata-writer
     newName: docker.io/aipipeline/metadata-writer
-    newTag: 1.2.1
+    newTag: 1.3.0

manifests/kustomize/third-party/tekton-custom-task/kustomization.yaml

@@ -8,6 +8,6 @@ namespace: tekton-pipelines
 
 images:
   - name: docker.io/aipipeline/pipelineloop-controller
-    newTag: 1.2.1
+    newTag: 1.3.0
   - name: docker.io/aipipeline/pipelineloop-webhook
-    newTag: 1.2.1
+    newTag: 1.3.0

sdk/python/README.md

@@ -34,9 +34,9 @@ can be found in the [SDK README](/sdk/README.md)
 ## Development Prerequisites
 
 1. [`Python`](https://www.python.org/downloads/): version `3.7` or later (new code must maintain compatibility with `3.7`)
-2. [`Kubernetes` Cluster](https://kubernetes.io/): version `1.20` ([required by Kubeflow](https://www.kubeflow.org/docs/started/kubeflow-overview/) and Tekton `0.30`)
+2. [`Kubernetes` Cluster](https://kubernetes.io/): version `1.21` ([required by Kubeflow](https://www.kubeflow.org/docs/started/kubeflow-overview/) and Tekton `0.36`)
 3. [`kubectl` CLI](https://kubernetes.io/docs/tasks/tools/install-kubectl/): required to deploy Tekton pipelines to Kubernetes cluster
-4. [`Tekton` Deployment](https://github.com/tektoncd/pipeline/releases/tag/v0.30.0/): version `0.30.0` or greater, required for end-to-end testing
+4. [`Tekton` Deployment](https://github.com/tektoncd/pipeline/releases/tag/v0.36.0/): version `0.36.0` or greater, required for end-to-end testing
 5. [`tkn` CLI](https://github.com/tektoncd/cli#installing-tkn): version `0.17.2` or greater, required for end-to-end testing of Tekton pipelines
 6. [`Kubeflow Pipelines` Deployment](https://www.kubeflow.org/docs/pipelines/installation/overview/): required for some end-to-end tests
 
@@ -108,7 +108,7 @@ the Tekton YAML instead of Argo YAML. Since the KFP SDK was not designed and imp
 _monkey-patching_ was used to replace non-class methods and functions at runtime.
 
 In order for the _monkey patch_ to work properly, the `kfp-tekton` compiler source code has to be aligned with a
-specific version of the `kfp` SDK compiler. As of now the `kfp-tekton` SDK version is `1.2.3` which is aligned with KFP
+specific version of the `kfp` SDK compiler. As of now the `kfp-tekton` SDK version is `1.3.0` which is aligned with KFP
 SDK version [`1.8.13`](https://pypi.org/project/kfp/1.8.13/).
 
 

sdk/python/kfp_tekton/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '1.2.3'
+__version__ = '1.3.0'
 
 from ._client import TektonClient  # noqa F401
 from .k8s_client_helper import env_from_secret  # noqa F401

sdk/python/setup.py

@@ -20,14 +20,14 @@
 #
 # To create a distribution for PyPi run:
 #
-#    $ export KFP_TEKTON_VERSION=1.2.3-rc1
+#    $ export KFP_TEKTON_VERSION=1.3.0-rc1
 #    $ python3 setup.py sdist
 #    $ twine check dist/kfp-tekton-${KFP_TEKTON_VERSION/-rc/rc}.tar.gz
 #    $ twine upload --repository pypi dist/kfp-tekton-${KFP_TEKTON_VERSION/-rc/rc}.tar.gz
 #
 #   ... or:
 #
-#    $ make distribution KFP_TEKTON_VERSION=1.2.3-rc1
+#    $ make distribution KFP_TEKTON_VERSION=1.3.0-rc1
 #
 # =============================================================================
 

sdk/python/tests/compiler/testdata/resourceop_basic.yaml

@@ -65,7 +65,7 @@ spec:
             is failure.
           name: failure-condition
           type: string
-        - default: aipipeline/kubectl-wrapper:1.2.3
+        - default: aipipeline/kubectl-wrapper:1.3.0
           description: Kubectl wrapper image
           name: image
           type: string

sdk/python/tests/compiler/testdata/volume_op.yaml

@@ -75,7 +75,7 @@ spec:
             is failure.
           name: failure-condition
           type: string
-        - default: aipipeline/kubectl-wrapper:1.2.3
+        - default: aipipeline/kubectl-wrapper:1.3.0
           description: Kubectl wrapper image
           name: image
           type: string