Upload examples of kfp v2 (#2627)

community-content/vertex_pipeline_kfpv2/README.md

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+# Kubeflow pipeline on Vertex
+
+## Overview
+Theses examples show the codes of KFP v2 with aiplatform SDK
+* create endpoint
+* train model
+* deploy model
+
+## Requirements
+* Python 3.8
+* Vertex on Google Cloud Platform
+
+## Quickstart
+* Compiler
+  * use python to compiler example.py to example.json
+* Run
+  * Open Vertex on GCP
+  * Click 'Pipelines' and create run
+  * Click 'Upload file' to upload example.json
+  * Select bucket then submit
+
+## Cleanup
+Following are optional to cleanup
+* endpoint
+  * Click 'Online prediction'
+  * Click 'Actions' to delete endpoint
+* model
+  * Click 'Model Registry'
+  * Click 'More' to delete model

community-content/vertex_pipeline_kfpv2/create-endpoint.py

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+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
+def create_endpoint(
+    endpoint_name: str,
+    project_id: str,
+    location: str,
+):
+    import json
+    from google.cloud import aiplatform
+
+    aiplatform.init(
+        project=project_id,
+        location=location,
+    )
+    endpoint = aiplatform.Endpoint.create(display_name=endpoint_name)
+
+
+@dsl.pipeline(name='create-endpoint')
+def pipeline_create_endpoint():
+    create_endpoint("auto_endpoint", "990000000009", "us-west1")
+
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_create_endpoint,
+        package_path='create_endpoint.json')

community-content/vertex_pipeline_kfpv2/deploy-model.py

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+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
+def deploy_model(
+    model_id: str,
+    endpoint_id: str,
+    machine_type: str,
+    min_replica_count: int,
+    max_replica_count: int,
+):
+    import json
+    from google.cloud import aiplatform
+
+    model = aiplatform.Model(model_id)
+    endpoint = aiplatform.Endpoint(endpoint_id)
+
+    endpoint = model.deploy(
+        endpoint=endpoint,
+        machine_type=machine_type,
+        min_replica_count=min_replica_count,
+        max_replica_count=max_replica_count,
+    )
+
+
+@dsl.pipeline(name='deploy-model')
+def pipeline_deploy_model():
+    project = "projects/990000000009/locations/us-west1"
+    model_id = project + "/models/1100000000000000001"
+    endpoint_id = project + "/endpoints/2200000000000000002"
+    deploy_model(model_id, endpoint_id, "n1-standard-2", 1, 1)
+
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_deploy_model,
+        package_path='deploy_model.json')

community-content/vertex_pipeline_kfpv2/train-model.py

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+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0,scikit-learn==1.3.2'])
+def train_model(
+    model_name: str,
+    project_id: str,
+    location: str,
+):
+    from google.cloud import aiplatform
+    from sklearn.linear_model import LinearRegression
+
+    aiplatform.init(
+        project=project_id, 
+        location=location, 
+        staging_bucket=f"gs://{shared_state['model-bucket']}",
+    )
+    model = LinearRegression()
+    aiplatform.save_model(model, model_name)
+
+
+@dsl.pipeline(name='train-model')
+def pipeline_train_model():
+    train_model("lr-model", "990000000009", "us-west1")
+
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_train_model,
+        package_path='train_model.json')