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Aiden010200andGitHub
c5be708d45
* Upload examples of kfp v2 * Upload run experiment example. * Upload batch prediction job sample. * Update recycling of computing resources Recycling computing resources after predictions. * Upload missing file Add delete endpoint func to recycle resources.
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
from kfp.v2 import dsl
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@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
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def deploy_model(
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model_id: str,
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endpoint_id: str,
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machine_type: str,
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min_replica_count: int,
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max_replica_count: int,
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):
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import json
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from google.cloud import aiplatform
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model = aiplatform.Model(model_id)
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endpoint = aiplatform.Endpoint(endpoint_id)
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endpoint = model.deploy(
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endpoint=endpoint,
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machine_type=machine_type,
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min_replica_count=min_replica_count,
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max_replica_count=max_replica_count,
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)
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@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
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def delete_endpoint(
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endpoint_id: str,
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):
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from google.cloud import aiplatform
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endpoint = aiplatform.Endpoint(endpoint_id)
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endpoint.undeploy_all()
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endpoint.delete()
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@dsl.pipeline(name='deploy-model')
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def pipeline_deploy_model():
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project = "projects/990000000009/locations/us-west1"
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model_id = project + "/models/1100000000000000001"
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endpoint_id = project + "/endpoints/2200000000000000002"
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deploy_model(model_id, endpoint_id, "n1-standard-2", 1, 1)
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# After serving predictions, recycling computing resources
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delete_endpoint(endpoint_id)
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if __name__ == "__main__":
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from kfp.v2 import compiler
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compiler.Compiler().compile(
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pipeline_func=pipeline_deploy_model,
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package_path='deploy_model.json')
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