CODEWITHSUNDEEP
pythongoogle-ai-platformgoogle-cloud-vertex-aigoogle-cloud-ai-platform-pipelines
Pentium10
Pentium10

Reputation: 208022

How to set the run name of a PipelineJob

I have this code to start a VertexAI pipeline job:

import google.cloud.aiplatform as vertexai

vertexai.init(project=PROJECT_ID,staging_bucket=PIPELINE_ROOT)

job = vertexai.PipelineJob(
    display_name='pipeline-test-1',
    template_path='xgb_pipe.json'
)

job.run()

which works nicely, but the run name label is a random number. How can I specify the run name?

enter image description here

Upvotes: 0

Views: 1040

Answers (1)

Ricco D
Ricco D

Reputation: 7287

You can change the value shown in "Run Name" by defining name when defining the pipeline.

@kfp.dsl.pipeline(name="automl-image-training-v2")

When defining the name using @kfp.dsl.pipeline, it will automatically append the date and time when the pipeline is ran. Proceed with compiling and running the pipeline to see the change in "Run Name".

enter image description here

I used the code in Vertex AI pipeline examples. See pipeline code:

@kfp.dsl.pipeline(name="automl-image-training-v2")
def pipeline(project: str = PROJECT_ID, region: str = REGION):
    ds_op = gcc_aip.ImageDatasetCreateOp(
        project=project,
        display_name="flowers",
        gcs_source="gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv",
        import_schema_uri=aip.schema.dataset.ioformat.image.single_label_classification,
    )

    training_job_run_op = gcc_aip.AutoMLImageTrainingJobRunOp(
        project=project,
        display_name="train-automl-flowers",
        prediction_type="classification",
        model_type="CLOUD",
        base_model=None,
        dataset=ds_op.outputs["dataset"],
        model_display_name="train-automl-flowers",
        training_fraction_split=0.6,
        validation_fraction_split=0.2,
        test_fraction_split=0.2,
        budget_milli_node_hours=8000,
    )

    endpoint_op = gcc_aip.EndpointCreateOp(
        project=project,
        location=region,
        display_name="train-automl-flowers",
    )

    gcc_aip.ModelDeployOp(
        model=training_job_run_op.outputs["model"],
        endpoint=endpoint_op.outputs["endpoint"],
        automatic_resources_min_replica_count=1,
        automatic_resources_max_replica_count=1,
    )

Upvotes: 1

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