def create_experiment(self, name, artifact_location=None):
if name is None or name == "":
raise MlflowException("Invalid experiment name",
INVALID_PARAMETER_VALUE)
with self.ManagedSessionMaker() as session:
try:
experiment = SqlExperiment(
name=name,
lifecycle_stage=LifecycleStage.ACTIVE,
artifact_location=artifact_location,
)
session.add(experiment)
if not artifact_location:
# this requires a double write. The first one to generate an autoincrement-ed ID
eid = session.query(SqlExperiment).filter_by(
name=name).first().experiment_id
experiment.artifact_location = self._get_artifact_location(
eid)
except sqlalchemy.exc.IntegrityError as e:
raise MlflowException(
"Experiment(name={}) already exists. "
"Error: {}".format(name, str(e)),
RESOURCE_ALREADY_EXISTS,
)
session.flush()
return str(experiment.experiment_id)
def test_list_experiments(view_type, tmpdir):
sqlite_uri = "sqlite:///" + os.path.join(tmpdir.strpath, "test.db")
store = SqlAlchemyStore(sqlite_uri, default_artifact_root=tmpdir.strpath)
num_experiments = SEARCH_MAX_RESULTS_DEFAULT + 1
if view_type == ViewType.DELETED_ONLY:
# Delete the default experiment
mlflow.tracking.MlflowClient(sqlite_uri).delete_experiment("0")
# This is a bit hacky but much faster than creating experiments one by one with
# `mlflow.create_experiment`
with store.ManagedSessionMaker() as session:
lifecycle_stages = LifecycleStage.view_type_to_stages(view_type)
experiments = [
SqlExperiment(
name=f"exp_{i + 1}",
lifecycle_stage=random.choice(lifecycle_stages),
artifact_location=tmpdir.strpath,
) for i in range(num_experiments - 1)
]
session.add_all(experiments)
try:
url, process = _init_server(sqlite_uri,
root_artifact_uri=tmpdir.strpath)
print("In process %s", process)
mlflow.set_tracking_uri(url)
# `max_results` is unspecified
assert len(mlflow.list_experiments(view_type)) == num_experiments
# `max_results` is larger than the number of experiments in the database
assert len(mlflow.list_experiments(view_type, num_experiments +
1)) == num_experiments
# `max_results` is equal to the number of experiments in the database
assert len(mlflow.list_experiments(view_type,
num_experiments)) == num_experiments
# `max_results` is smaller than the number of experiments in the database
assert len(mlflow.list_experiments(view_type, num_experiments -
1)) == num_experiments - 1
finally:
process.terminate()
