integration.vertex
initialize_comet_logger¶
comet_ml.integration.vertex.initialize_comet_logger(experiment,
pipeline_job_name, pipeline_task_name, pipeline_task_uuid)
Logs the Vertex task identifiers needed to track your pipeline status in Comet.ml. You need to call this function from every component you want to track with Comet.ml.
Args:
- experiment: An already created Experiment object.
- pipeline_job_name: string. The Vertex pipeline job name, see below how to get it automatically from Vertex.
- pipeline_task_name: string. The Vertex task name, see below how to get it automatically from Vertex.
- pipeline_task_uuid: string. The Vertex task unique id, see below how to get it automatically from Vertex.
For example:
@kfp.dsl.v2.component
def my_component() -> None:
import comet_ml.integration.vertex
experiment = comet_ml.Experiment()
pipeline_run_name = "{{$.pipeline_job_name}}"
pipeline_task_name = "{{$.pipeline_task_name}}"
pipeline_task_id = "{{$.pipeline_task_uuid}}"
comet_ml.integration.Vertex.initialize_comet_logger(experiment, pipeline_run_name, pipeline_task_name, pipeline_task_id)
comet_logger_component¶
comet_ml.integration.vertex.comet_logger_component(api_key=None,
project_name=None, workspace=None, packages_to_install=None,
base_image=None, custom_experiment=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 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 pipeline tasks are logged. If not provided, the value set in the configuration system will be used.
packages_to_install: List of string, optional. Which packages to install, given directly to
kfp.components.create_component_from_func
. Default is ["google-cloud-aiplatform", "comet_ml"].base_image: string, optional. Which docker image to use. If not provided, the default Kubeflow base image will be used.
custom_experiment: Experiment, optional. The Comet Experiment with custom configuration which you can provide to be used instead of Experiment which would be implicitly created with default options.
Example:
@dsl.pipeline(name='ML training pipeline')
def ml_training_pipeline():
import comet_ml.integration.vertex
comet_ml.integration.vertex.comet_logger_component()