async def sample_cancel_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CancelPipelineJobRequest(name="name_value", )

    # Make the request
    await client.cancel_pipeline_job(request=request)
Exemplo n.º 2
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async def sample_get_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetPipelineJobRequest(name="name_value", )

    # Make the request
    response = await client.get_pipeline_job(request=request)

    # Handle the response
    print(response)
Exemplo n.º 3
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async def sample_create_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreatePipelineJobRequest(
        parent="parent_value", )

    # Make the request
    response = await client.create_pipeline_job(request=request)

    # Handle the response
    print(response)
async def sample_list_training_pipelines():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListTrainingPipelinesRequest(
        parent="parent_value", )

    # Make the request
    page_result = client.list_training_pipelines(request=request)

    # Handle the response
    async for response in page_result:
        print(response)
async def sample_delete_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeletePipelineJobRequest(name="name_value", )

    # Make the request
    operation = client.delete_pipeline_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)
Exemplo n.º 6
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async def sample_create_training_pipeline():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceAsyncClient()

    # Initialize request argument(s)
    training_pipeline = aiplatform_v1beta1.TrainingPipeline()
    training_pipeline.display_name = "display_name_value"
    training_pipeline.training_task_definition = "training_task_definition_value"
    training_pipeline.training_task_inputs.null_value = "NULL_VALUE"

    request = aiplatform_v1beta1.CreateTrainingPipelineRequest(
        parent="parent_value",
        training_pipeline=training_pipeline,
    )

    # Make the request
    response = await client.create_training_pipeline(request=request)

    # Handle the response
    print(response)