Bump version to 0.3.0 (kubeflow#3329)

* Bump version to 0.3.0

* Fix formatting

* More formatting fixes

* More formatting fixes

* update requirements.txt

* update version

manifests/gcp_marketplace/chart/kubeflow-pipelines/templates/application.yaml

@@ -12,7 +12,7 @@ metadata:
 spec:
   descriptor:
     type: Kubeflow Pipelines
-    version: '0.2.5'
+    version: '0.3.0'
     description: |-
       Reusable end-to-end ML workflow
     maintainers:

manifests/gcp_marketplace/schema.yaml

@@ -1,10 +1,10 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '0.2.5'
+  publishedVersion: '0.3.0'
   publishedVersionMetadata:
     releaseNote: >-
-      Based on 0.2.5 version.
+      Based on 0.3.0 version.
     releaseTypes:
       - Feature
     recommended: false

manifests/kustomize/README.md

@@ -1,97 +1,134 @@
 # Install Kubeflow Pipelines
-This folder contains Kubeflow Pipelines Kustomize manifests for a light weight deployment. You can follow the instruction and deploy Kubeflow Pipelines in an existing cluster.
 
+This folder contains Kubeflow Pipelines Kustomize manifests for a light weight
+deployment. You can follow the instruction and deploy Kubeflow Pipelines in an
+existing cluster.
 
 ## TL;DR
 
 Deploy latest version of Kubeflow Pipelines
+
 ```
-export PIPELINE_VERSION=0.2.5
+export PIPELINE_VERSION=0.3.0
 kubectl apply -f https://storage.googleapis.com/ml-pipeline/pipeline-lite/$PIPELINE_VERSION/crd.yaml
-kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
-kubectl apply -f https://storage.googleapis.com/ml-pipeline/pipeline-lite/$PIPELINE_VERSION/namespaced-install.yaml
+kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io kubectl apply -f https://storage.googleapis.com/ml-pipeline/pipeline-lite/$PIPELINE_VERSION/namespaced-install.yaml
 ```
 
 Then get the Pipeline URL
+
 ```
 kubectl describe configmap inverse-proxy-config -n kubeflow | grep googleusercontent.com
 ```
 
 ## Customization
-Customization can be done through Kustomize [Overlay](https://github.com/kubernetes-sigs/kustomize/blob/master/docs/glossary.md#overlay).
 
-Note - The instruction below assume you installed kubectl v1.14.0 or later, which has native support of kustomize.
-To get latest kubectl, visit [here](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
+Customization can be done through Kustomize
+[Overlay](https://github.com/kubernetes-sigs/kustomize/blob/master/docs/glossary.md#overlay).
+
+Note - The instruction below assume you installed kubectl v1.14.0 or later,
+which has native support of kustomize. To get latest kubectl, visit
+[here](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
 
 ### Deploy on GCP with CloudSQL and GCS
+
 See [here](env/gcp/README.md) for more details.
 
 ### Change deploy namespace
+
 To deploy Kubeflow Pipelines in namespace FOO,
-- Edit [dev/kustomization.yaml](env/dev/kustomization.yaml) or [gcp/kustomization.yaml](env/gcp/kustomization.yaml) namespace section to FOO
-- Then run
+
+-   Edit [dev/kustomization.yaml](env/dev/kustomization.yaml) or
+    [gcp/kustomization.yaml](env/gcp/kustomization.yaml) namespace section to
+    FOO
+-   Then run
+
 ```
 kubectl kustomize base/crds | kubectl apply -f -
+
 # then
+
 kubectl kustomize env/dev | kubectl apply -f -
+
 # or
+
 kubectl kustomize env/gcp | kubectl apply -f -
 ```
 
 ### Disable the public endpoint
-By default, the deployment install an [invert proxy agent](https://github.com/google/inverting-proxy) that exposes a public URL. If you want to skip installing it,
-- Comment out the proxy component in the [kustomization.yaml](base/kustomization.yaml).
-- Then run
+
+By default, the deployment install an
+[invert proxy agent](https://github.com/google/inverting-proxy) that exposes a
+public URL. If you want to skip installing it:
+
+-   Comment out the proxy component in the
+    [kustomization.yaml](base/kustomization.yaml).
+-   Then run
+
 ```
 kubectl kustomize . | kubectl apply -f -
 ```
 
-The UI is still accessible by port-forwarding
-```
-kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
-```
-and open http://localhost:8080/
+The UI is still accessible by port-forwarding `kubectl port-forward -n kubeflow
+svc/ml-pipeline-ui 8080:80` and open http://localhost:8080/
 
 ### Deploy on AWS (with S3 buckets as artifact store)
 
 [https://github.com/e2fyi/kubeflow-aws](https://github.com/e2fyi/kubeflow-aws/tree/master/pipelines)
 provides a community-maintained manifest for deploying kubeflow pipelines on AWS
-(with S3 as artifact store instead of minio). More details can be found in the repo.
+(with S3 as artifact store instead of minio). More details can be found in the
+repo.
 
 ## Uninstall
+
 You can uninstall Kubeflow Pipelines by running
+
 ```
 export PIPELINE_VERSION=0.1.38
 kubectl delete -f https://storage.googleapis.com/ml-pipeline/pipeline-lite/$PIPELINE_VERSION/namespaced-install.yaml
 kubectl delete -f https://storage.googleapis.com/ml-pipeline/pipeline-lite/$PIPELINE_VERSION/crd.yaml
 ```
 
 Or if you deploy through kustomize
+
 ```
 kubectl kustomize env/dev | kubectl delete -f -
+
 # or
+
 kubectl kustomize env/gcp | kubectl delete -f -
+
 # then
-kubectl kustomize base/crds | kubectl delete -f -
 
+kubectl kustomize base/crds | kubectl delete -f -
 ```
 
 ## Troubleshooting
 
 ### Permission error installing Kubeflow Pipelines to a cluster
+
 Run
+
 ```
-kubectl create clusterrolebinding your-binding --clusterrole=cluster-admin --user=[your-user-name]
+kubectl create clusterrolebinding your-binding --clusterrole=cluster-admin
+--user=[your-user-name]
 ```
 
 ### Samples requires "user-gcp-sa" secret
+
 If sample code requires a "user-gcp-sa" secret, you could create one by
-- First download the GCE VM service account token [Document](https://cloud.google.com/iam/docs/creating-managing-service-account-keys#creating_service_account_keys)
+
+-   First download the GCE VM service account token
+    [Document](https://cloud.google.com/iam/docs/creating-managing-service-account-keys#creating_service_account_keys)
+
 ```
 gcloud iam service-accounts keys create application_default_credentials.json \
-  --iam-account [SA-NAME]@[PROJECT-ID].iam.gserviceaccount.com
+--iam-account [SA-NAME]@[PROJECT-ID].iam.gserviceaccount.com
 ```
-- Run
+
+-   Run
+
 ```
-kubectl create secret -n [your-namespace] generic user-gcp-sa --from-file=user-gcp-sa.json=application_default_credentials.json
+kubectl
+create secret -n [your-namespace] generic user-gcp-sa
+--from-file=user-gcp-sa.json=application_default_credentials.json`
 ```

manifests/kustomize/base/kustomization.yaml

@@ -13,18 +13,18 @@ resources:
 
 images:
 - name: gcr.io/ml-pipeline/api-server
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/persistenceagent
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/scheduledworkflow
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/frontend
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/viewer-crd-controller
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/inverse-proxy-agent
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/visualization-server
-  newTag: 0.2.5
+  newTag: 0.3.0
 - name: gcr.io/ml-pipeline/metadata-writer
-  newTag: 0.2.5
+  newTag: 0.3.0

manifests/kustomize/base/pipeline-application.yaml

@@ -12,7 +12,7 @@ spec:
     matchLabels:
       application-crd-id: kubeflow-pipelines
   descriptor:
-    version: 0.2.5
+    version: 0.3.0
     type: Kubeflow Pipelines
     description: |-
       Reusable end-to-end ML workflow

sdk/python/kfp/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '0.2.5'
+__version__ = '0.3.0'
 
 from . import components
 from . import containers

sdk/python/requirements.txt

@@ -10,7 +10,7 @@ cryptography>=2.4.2
 google-auth>=1.6.1
 requests_toolbelt>=0.8.0
 cloudpickle==1.1.1
-kfp-server-api==0.2.5
+kfp-server-api==0.3.0
 argo-models == 2.2.1a
 jsonschema >= 3.0.1
 tabulate == 0.8.3

sdk/python/setup.py

@@ -32,7 +32,11 @@
     'google-auth>=1.6.1',
     'requests_toolbelt>=0.8.0',
     'cloudpickle==1.1.1',
-    'kfp-server-api==0.2.5',  #Update the upper version whenever a new version of the kfp-server-api package is released. Update the lower version when there is a breaking change in kfp-server-api, or kfp sdk depends on new api changes in kfp-server-api.
+    # Update the upper version whenever a new major version of the
+    # kfp-server-api package is released. Update the lower version when there is
+    # a breaking change in kfp-server-api, or kfp sdk depends on new api changes
+    # in kfp-server-api.
+    'kfp-server-api>=0.2.5, <0.4.0',
     'argo-models == 2.2.1a',  #2.2.1a is equivalent to argo 2.2.1
     'jsonschema >= 3.0.1',
     'tabulate == 0.8.3',
@@ -41,24 +45,26 @@
     'strip-hints',
 ]
 
+
 def find_version(*file_path_parts):
-    here = os.path.abspath(os.path.dirname(__file__))
-    with open(os.path.join(here, *file_path_parts), 'r') as fp:
-        version_file_text = fp.read()
+  here = os.path.abspath(os.path.dirname(__file__))
+  with open(os.path.join(here, *file_path_parts), 'r') as fp:
+    version_file_text = fp.read()
+
+  version_match = re.search(
+      r"^__version__ = ['\"]([^'\"]*)['\"]",
+      version_file_text,
+      re.M,
+  )
+  if version_match:
+    return version_match.group(1)
 
-    version_match = re.search(
-        r"^__version__ = ['\"]([^'\"]*)['\"]",
-        version_file_text,
-        re.M,
-    )
-    if version_match:
-        return version_match.group(1)
+  raise RuntimeError('Unable to find version string.')
 
-    raise RuntimeError("Unable to find version string.")
 
 setup(
     name=NAME,
-    version=find_version("kfp", "__init__.py"),
+    version=find_version('kfp', '__init__.py'),
     description='KubeFlow Pipelines SDK',
     author='google',
     install_requires=REQUIRES,