integration.kubeflow

initialize_comet_logger¶

comet_ml.integration.kubeflow.initialize_comet_logger(experiment,
    workflow_uid, pod_name)

Logs the Kubeflow task identifiers needed to track your pipeline status in Comet.ml. You need to call this function from every components you want to track with Comet.ml.

Args:

• experiment: An already created Experiment object.
• workflow_uid: string. The Kubeflow workflow uid, see below to get it automatically from Kubeflow.
• pod_name: string. The Kubeflow pod name, see below to get it automatically from Kubeflow.

For example:

def my_component() -> None:
    import comet_ml.integration.kubeflow

    experiment = comet_ml.Experiment()
    workflow_uid = "{{workflow.uid}}"
    pod_name = "{{pod_name}}"

    comet_ml.integration.kubeflow.initialize_comet_logger(experiment, workflow_uid, pod_name)

comet_logger_component¶

comet_ml.integration.kubeflow.comet_logger_component(api_key=None,
    project_name=None, workspace=None, packages_to_install=None,
    base_image=None)

Inject the Comet Logger component which continuously track and report the current pipeline status to Comet.ml.

Args:

• api_key: string, optional. Your Comet API Key, if not provided, the value set in the configuration system will be used.

• project_name: string, optional. The project name where all of the pipeline tasks are logged. If not provided, the value set in the configuration system will be used.

• workspace: string, optional. The workspace name where all of the pipeline tasks are logged. If not provided, the value set in the configuration system will be used.

Example:

@dsl.pipeline(name='ML training pipeline')
def ml_training_pipeline():
    import comet_ml.integration.kubeflow

    comet_ml.integration.kubeflow.comet_logger_component()