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))