def test_kedro_mlflow_config_experiment_was_deleted(kedro_project_with_mlflow_conf): # create an experiment with the same name and then delete it mlflow_tracking_uri = (kedro_project_with_mlflow_conf / "mlruns").as_uri() mlflow_client = MlflowClient(mlflow_tracking_uri) mlflow_client.create_experiment("exp1") mlflow_client.delete_experiment( mlflow_client.get_experiment_by_name("exp1").experiment_id ) # the config must restore properly the experiment config = KedroMlflowConfig( project_path=kedro_project_with_mlflow_conf, mlflow_tracking_uri="mlruns", experiment_opts=dict(name="exp1"), ) project_metadata = _get_project_metadata(kedro_project_with_mlflow_conf) _add_src_to_path(project_metadata.source_dir, kedro_project_with_mlflow_conf) configure_project(project_metadata.package_name) with KedroSession.create( "fake_project", project_path=kedro_project_with_mlflow_conf ): config.setup() assert "exp1" in [exp.name for exp in config.mlflow_client.list_experiments()]
def test_kedro_mlflow_config_experiment_was_deleted(mocker, tmp_path): # create a ".kedro.yml" file to identify "tmp_path" as the root of a kedro project mocker.patch("kedro_mlflow.utils._is_kedro_project", lambda x: True) # create an experiment with the same name and then delete it mlflow_tracking_uri = (tmp_path / "mlruns").as_uri() mlflow_client = MlflowClient(mlflow_tracking_uri) mlflow_client.create_experiment("exp1") mlflow_client.delete_experiment( mlflow_client.get_experiment_by_name("exp1").experiment_id) # the config must restore properly the experiment config = KedroMlflowConfig( project_path=tmp_path, mlflow_tracking_uri="mlruns", experiment_opts=dict(name="exp1"), ) assert "exp1" in [ exp.name for exp in config.mlflow_client.list_experiments() ]
def test_kedro_mlflow_config_experiment_was_deleted(kedro_project_with_mlflow_conf): # create an experiment with the same name and then delete it mlflow_tracking_uri = (kedro_project_with_mlflow_conf / "mlruns").as_uri() mlflow_client = MlflowClient(mlflow_tracking_uri) mlflow_client.create_experiment("exp1") mlflow_client.delete_experiment( mlflow_client.get_experiment_by_name("exp1").experiment_id ) # the config must restore properly the experiment config = KedroMlflowConfig( server=dict(mlflow_tracking_uri="mlruns"), tracking=dict(experiment=dict(name="exp1")), ) bootstrap_project(kedro_project_with_mlflow_conf) with KedroSession.create(project_path=kedro_project_with_mlflow_conf) as session: context = session.load_context() # setup config config.setup(context) assert "exp1" in [ exp.name for exp in config.server._mlflow_client.list_experiments() ]
from mlflow.tracking import MlflowClient from mlflow.entities import ViewType if __name__ == "__main__": def print_experiment_info(experiments): for e in experiments: print("- experiment_id: {}, name: {}, lifecycle_stage: {}" .format(e.experiment_id, e.name, e.lifecycle_stage)) client = MlflowClient() for name in ["Experiment 1", "Experiment 2"]: exp_id = client.create_experiment(name) # Delete the last experiment client.delete_experiment(exp_id) # Fetch experiments by view type print("Active experiments:") print_experiment_info(client.list_experiments(view_type=ViewType.ACTIVE_ONLY)) print("Deleted experiments:") print_experiment_info(client.list_experiments(view_type=ViewType.DELETED_ONLY)) print("All experiments:") print_experiment_info(client.list_experiments(view_type=ViewType.ALL))