def create_run(self, experiment_id, user_id, start_time, tags):
with self.ManagedSessionMaker() as session:
experiment = self.get_experiment(experiment_id)
self._check_experiment_is_active(experiment)
run_id = uuid.uuid4().hex
artifact_location = posixpath.join(
experiment.artifact_location, run_id,
SqlAlchemyStore.ARTIFACTS_FOLDER_NAME)
run = SqlRun(name="",
artifact_uri=artifact_location,
run_uuid=run_id,
experiment_id=experiment_id,
source_type=SourceType.to_string(SourceType.UNKNOWN),
source_name="",
entry_point_name="",
user_id=user_id,
status=RunStatus.to_string(RunStatus.RUNNING),
start_time=start_time,
end_time=None,
source_version="",
lifecycle_stage=LifecycleStage.ACTIVE)
tags_dict = {}
for tag in tags:
tags_dict[tag.key] = tag.value
run.tags = [
SqlTag(key=key, value=value)
for key, value in tags_dict.items()
]
self._save_to_db(objs=run, session=session)
return run.to_mlflow_entity()
def create_run(self, experiment_id, user_id, run_name, source_type, source_name,
entry_point_name, start_time, source_version, tags, parent_run_id):
experiment = self.get_experiment(experiment_id)
if experiment.lifecycle_stage != LifecycleStage.ACTIVE:
raise MlflowException('Experiment id={} must be active'.format(experiment_id),
INVALID_STATE)
run_uuid = uuid.uuid4().hex
artifact_location = build_path(experiment.artifact_location, run_uuid,
SqlAlchemyStore.ARTIFACTS_FOLDER_NAME)
run = SqlRun(name=run_name or "", artifact_uri=artifact_location, run_uuid=run_uuid,
experiment_id=experiment_id, source_type=SourceType.to_string(source_type),
source_name=source_name, entry_point_name=entry_point_name,
user_id=user_id, status=RunStatus.to_string(RunStatus.RUNNING),
start_time=start_time, end_time=None,
source_version=source_version, lifecycle_stage=LifecycleStage.ACTIVE)
for tag in tags:
run.tags.append(SqlTag(key=tag.key, value=tag.value))
if parent_run_id:
run.tags.append(SqlTag(key=MLFLOW_PARENT_RUN_ID, value=parent_run_id))
if run_name:
run.tags.append(SqlTag(key=MLFLOW_RUN_NAME, value=run_name))
self._save_to_db([run])
return run.to_mlflow_entity()
def create_run(self, experiment_id, user_id, run_name, source_type,
source_name, entry_point_name, start_time, source_version,
tags, parent_run_id):
with self.ManagedSessionMaker() as session:
experiment = self.get_experiment(experiment_id)
if experiment.lifecycle_stage != LifecycleStage.ACTIVE:
raise MlflowException(
'Experiment id={} must be active'.format(experiment_id),
INVALID_STATE)
run_uuid = uuid.uuid4().hex
artifact_location = build_path(
experiment.artifact_location, run_uuid,
SqlAlchemyStore.ARTIFACTS_FOLDER_NAME)
run = SqlRun(name=run_name or "",
artifact_uri=artifact_location,
run_uuid=run_uuid,
experiment_id=experiment_id,
source_type=SourceType.to_string(source_type),
source_name=source_name,
entry_point_name=entry_point_name,
user_id=user_id,
status=RunStatus.to_string(RunStatus.RUNNING),
start_time=start_time,
end_time=None,
source_version=source_version,
lifecycle_stage=LifecycleStage.ACTIVE)
tags_dict = {}
for tag in tags:
tags_dict[tag.key] = tag.value
if parent_run_id:
tags_dict[MLFLOW_PARENT_RUN_ID] = parent_run_id
if run_name:
tags_dict[MLFLOW_RUN_NAME] = run_name
run.tags = [
SqlTag(key=key, value=value)
for key, value in tags_dict.items()
]
self._save_to_db(objs=run, session=session)
return run.to_mlflow_entity()
def create_run(self, experiment_id, user_id, run_name, source_type, source_name,
entry_point_name, start_time, source_version, tags, parent_run_id):
_ = parent_run_id
experiment = self.get_experiment(experiment_id)
if experiment.lifecycle_stage != Experiment.ACTIVE_LIFECYCLE:
raise MlflowException('Experiment id={} must be active'.format(experiment_id),
INVALID_STATE)
status = RunStatus.to_string(RunStatus.RUNNING)
run_uuid = uuid.uuid4().hex
run = SqlRun(name=run_name, artifact_uri=None, run_uuid=run_uuid,
experiment_id=experiment_id, source_type=source_type,
source_name=source_name, entry_point_name=entry_point_name,
user_id=user_id, status=status, start_time=start_time, end_time=None,
source_version=source_version, lifecycle_stage=RunInfo.ACTIVE_LIFECYCLE)
for tag in tags:
run.tags.append(SqlTag(key=tag.key, value=tag.value))
self._save_to_db([run])
return run.to_mlflow_entity()