bump version to 0.2.4 and tiny BTW doc fix (#3115)

* update guide for cluster size

* bump version to 0.2.4

* remove ALPHA

* update sample source link

* fix snapshot

* done

Co-authored-by: renmingu <40223865+renmingu@users.noreply.github.com>

backend/src/apiserver/config/sample_config.json

@@ -6,7 +6,7 @@
   },
   {
     "name": "[Demo] TFX - Taxi Tip Prediction Model Trainer",
-    "description": "[source code](https://console.cloud.google.com/mlengine/notebooks/deploy-notebook?q=download_url%3Dhttps%253A%252F%252Fraw.githubusercontent.com%252Fkubeflow%252Fpipelines%252Fmaster%252Fsamples%252Fcore%252Fparameterized_tfx_oss%252Ftaxi_pipeline_notebook.ipynb) [GCP Permission requirements](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/parameterized_tfx_oss#permission). Example pipeline that does classification with model analysis based on a public tax cab dataset.",
+    "description": "[source code](https://github.com/kubeflow/pipelines/tree/master/samples/core/parameterized_tfx_oss) [GCP Permission requirements](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/parameterized_tfx_oss#permission). Example pipeline that does classification with model analysis based on a public tax cab dataset.",
     "file": "/samples/core/parameterized_tfx_oss/parameterized_tfx_oss.py.yaml"
   },
   {

frontend/src/pages/GettingStarted.tsx

@@ -14,18 +14,18 @@
  * limitations under the License.
  */
 
-import * as React from 'react';
-import Buttons from '../lib/Buttons';
-import { Page } from './Page';
-import { ToolbarProps } from '../components/Toolbar';
 import Markdown from 'markdown-to-jsx';
+import * as React from 'react';
+import { classes, cssRaw } from 'typestyle';
+import { ApiFilter, PredicateOp } from '../apis/filter/api';
 import { AutoLink } from '../atoms/ExternalLink';
-import { cssRaw, classes } from 'typestyle';
+import { RoutePageFactory } from '../components/Router';
+import { ToolbarProps } from '../components/Toolbar';
+import SAMPLE_CONFIG from '../config/sample_config_from_backend.json';
 import { commonCss, padding } from '../Css';
 import { Apis } from '../lib/Apis';
-import { ApiFilter, PredicateOp } from '../apis/filter/api';
-import { RoutePageFactory } from '../components/Router';
-import SAMPLE_CONFIG from '../config/sample_config_from_backend.json';
+import Buttons from '../lib/Buttons';
+import { Page } from './Page';
 
 const DEMO_PIPELINES: string[] = SAMPLE_CONFIG.slice(0, 4);
 const DEMO_PIPELINES_ID_MAP = {
@@ -50,7 +50,7 @@ const PAGE_CONTENT_MD = ({
 
 ## Build your own pipeline
 
-Build an end-to-end ML pipeline with TensorFlow Extended (TFX) [**Start Here!**](https://console.cloud.google.com/mlengine/notebooks/deploy-notebook?q=download_url%3Dhttps%253A%252F%252Fraw.githubusercontent.com%252Ftensorflow%252Ftfx%252Fmaster%252Fdocs%252Ftutorials%252Ftfx%252Ftemplate.ipynb) (Alpha)
+Build an end-to-end ML pipeline with TensorFlow Extended (TFX) [**Start Here!**](https://console.cloud.google.com/mlengine/notebooks/deploy-notebook?q=download_url%3Dhttps%253A%252F%252Fraw.githubusercontent.com%252Ftensorflow%252Ftfx%252Fmaster%252Fdocs%252Ftutorials%252Ftfx%252Ftemplate.ipynb)
 
 <br/>
 

frontend/src/pages/__snapshots__/GettingStarted.test.tsx.snap

@@ -5,7 +5,7 @@ exports[`GettingStarted page fallbacks to show pipeline list page if request fai
 - First value
 + Second value
 
-@@ -70,11 +70,13 @@
+@@ -69,11 +69,13 @@
               >
             </li>
           </ul>
@@ -46,7 +46,6 @@ exports[`GettingStarted page initially renders documentation 1`] = `
             Start Here!
           </strong>
         </a>
-         (Alpha)
       </p>
       <br />
       <h2
@@ -260,7 +259,7 @@ exports[`GettingStarted page renders documentation with pipeline deep link after
 - First value
 + Second value
 
-@@ -17,11 +17,13 @@
+@@ -16,11 +16,13 @@
       <h2 id=\\"demonstrations-and-tutorials\\">Demonstrations and Tutorials</h2>
       <p>This section contains demo and tutorial pipelines.</p>
       <p><strong>Demos</strong> - Try an end-to-end demonstration pipeline.</p>
@@ -275,7 +274,7 @@ exports[`GettingStarted page renders documentation with pipeline deep link after
               Classification pipeline with model analysis, based on a public
               BigQuery dataset of taxicab trips. Learn how to
               <a
-@@ -33,11 +35,13 @@
+@@ -32,11 +34,13 @@
               >
             </li>
           </ul>
@@ -290,7 +289,7 @@ exports[`GettingStarted page renders documentation with pipeline deep link after
               An example of end-to-end distributed training for an XGBoost model.
               <a
                 href=\\"https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm\\"
-@@ -55,11 +59,13 @@
+@@ -54,11 +58,13 @@
         <strong>Tutorials</strong> - Learn pipeline concepts by following a
         tutorial.
       </p>
@@ -305,7 +304,7 @@ exports[`GettingStarted page renders documentation with pipeline deep link after
               Shows how to pass data between python components.
               <a
                 href=\\"https://github.com/kubeflow/pipelines/tree/master/samples/tutorials/Data%20passing%20in%20python%20components\\"
-@@ -70,11 +76,13 @@
+@@ -69,11 +75,13 @@
               >
             </li>
           </ul>

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

@@ -12,7 +12,7 @@ metadata:
 spec:
   descriptor:
     type: Kubeflow Pipelines
-    version: '0.2.3'
+    version: '0.2.4'
     description: |-
       Reusable end-to-end ML workflow
     maintainers:

manifests/gcp_marketplace/guide.md

@@ -14,7 +14,16 @@ Once you have deployed Kubeflow Pipelines instances, you can view and manage the
 
 ## Cluster
 
-You can deploy Kubeflow Pipelines to a new cluster or an existing cluster. New clusters aren't customizable, so if you need that, you should use the [Google Kubernetes Engine](https://console.cloud.google.com/kubernetes/list) to create a cluster that meets your requirements and then deploy to that.
+You can deploy Kubeflow Pipelines to a new cluster or an existing cluster. New clusters aren't customizable, so if you need that, you should use the [Google Kubernetes Engine](https://console.cloud.google.com/kubernetes/list) to create a cluster that meets your requirements and then deploy to that. The required cluster profile is 3 nodes with 2 CPUs. You also can create the cluster via gcloud.
+
+Here is a sample which will create a cluster with 3x2 CPUs and can access all GCP APIs without setting up secrets.
+
+```
+gcloud container clusters create $CLUSTER_NAME \
+    --zone $ZONE \
+    --machine-type n1-standard-2 \
+    --scopes cloud-platform
+```
 
 You can only deploy one Kubeflow Pipelines into a given cluster.
 

manifests/gcp_marketplace/schema.yaml

@@ -1,10 +1,10 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '0.2.3'
+  publishedVersion: '0.2.4'
   publishedVersionMetadata:
     releaseNote: >-
-      Based on 0.2.3 version.
+      Based on 0.2.4 version.
     releaseTypes:
       - Feature
     recommended: false

manifests/kustomize/README.md

@@ -6,7 +6,7 @@ This folder contains Kubeflow Pipelines Kustomize manifests for a light weight d
 
 Deploy latest version of Kubeflow Pipelines
 ```
-export PIPELINE_VERSION=0.2.3
+export PIPELINE_VERSION=0.2.4
 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

manifests/kustomize/base/kustomization.yaml

@@ -13,18 +13,18 @@ resources:
 
 images:
 - name: gcr.io/ml-pipeline/api-server
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/persistenceagent
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/scheduledworkflow
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/frontend
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/viewer-crd-controller
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/inverse-proxy-agent
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/visualization-server
-  newTag: 0.2.3
+  newTag: 0.2.4
 - name: gcr.io/ml-pipeline/metadata-writer
-  newTag: 0.2.3
+  newTag: 0.2.4

manifests/kustomize/base/pipeline-application.yaml

@@ -12,7 +12,7 @@ spec:
     matchLabels:
       application-crd-id: kubeflow-pipelines
   descriptor:
-    version: 0.2.3
+    version: 0.2.4
     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__ = '0.2.3'
+__version__ = '0.2.4'
 
 from . import components
 from . import containers