Add pipeline run to experiment

Google Cloud console

1. In the Google Cloud console, in the Vertex AI section, go to the Pipelines page.
   Go to Pipelines
2. In the Region drop-down list, select the region that you want to create a pipeline run in.
3. Click add_box Create run to open the Create pipeline run pane.
4. Specify the following Run details.
   • In the File field, click Choose to open the file selector. Navigate to the compiled pipeline JSON file that you want to run, select the pipeline, and click Open.
   • The Pipeline name defaults to the name that you specified in the pipeline definition. Optionally, specify a different Pipeline name.
   • Specify a Run name to uniquely identify this pipeline run.
5. To specify that this pipeline run uses a custom service account, a customer-managed encryption key, or a peered VPC network, click Advanced options (Optional).
   Use the following instructions to configure advanced options such as a custom service account.
   • To specify a service account, select a service account from the Service account drop-down list.
     If you do not specify a service account, Vertex AI Pipelines runs your pipeline using the default Compute Engine service account.
     Learn more about configuring a service account for use with Vertex AI Pipelines.
   • To use a customer managed encryption key (CMEK), select Use a customer-managed encryption key. The Select a customer-managed key drop-down list appears. In the Select a customer-managed key drop-down list, select the key that you want to use.
   • To use a peered VPC network in this pipeline run, enter the VPC network name in the Peered VPC network box.
6. Click Continue.
   The Cloud Storage location and Pipeline parameters pane appears.
• Required: Enter the Cloud Storage output directory, for example: gs://location_of_directory.
• Optional: Specify the parameters that you want to use for this pipeline run.
7. Click Submit to create your pipeline run.
8. After the Pipeline is submitted, it appears in the Pipeline's Google Cloud console table.
9. In the row associated with your pipeline click more_vert View more > Add to Experiment
• Select an existing Experiment or create a new one.
• Optional: If Experiment runs are associated with the Experiment, they show up in the drop-down. Select an existing Experiment run.
10. Click Save.

Compare a pipeline run with experiment runs using the Google Cloud console

1. In the Google Cloud console, go to the Experiments page.
   Go to Experiments.
   A list of experiments appears in the Experiments page.
2. Select the experiment you want to add your pipeline run to.
   A list of runs appears.
3. Select the runs you want to compare, then click Compare
   
4. Click the Add run button. A list of runs appears
5. Select the pipeline run you want to add. The run is added.

Vertex AI SDK for Python {:#sdk-add-pipeline-run}

The following samples use the PipelineJob API.

def log_pipeline_job_to_experiment_sample(
    experiment_name: str,
    pipeline_job_display_name: str,
    template_path: str,
    pipeline_root: str,
    project: str,
    location: str,
    parameter_values: Optional[Dict[str, Any]] = None,
):
    aiplatform.init(project=project, location=location)

    pipeline_job = aiplatform.PipelineJob(
        display_name=pipeline_job_display_name,
        template_path=template_path,
        pipeline_root=pipeline_root,
        parameter_values=parameter_values,
    )

    pipeline_job.submit(experiment=experiment_name)

def log_pipeline_job_sample(
    experiment_name: str,
    run_name: str,
    pipeline_job: aiplatform.PipelineJob,
    project: str,
    location: str,
):
    aiplatform.init(experiment=experiment_name, project=project, location=location)

    aiplatform.start_run(run=run_name, resume=True)

    aiplatform.log(pipeline_job=pipeline_job)