def update_run_info(self, run_id, run_status, end_time):
_validate_run_id(run_id)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
new_info = run_info._copy_with_overrides(run_status, end_time)
self._overwrite_run_info(new_info)
return new_info
def delete_run(self, run_id):
run_info = self._get_run_info(run_id)
if run_info is None:
raise MlflowException(
"Run '%s' metadata is in invalid state." % run_id,
databricks_pb2.INVALID_STATE)
check_run_is_active(run_info)
new_info = run_info._copy_with_overrides(
lifecycle_stage=LifecycleStage.DELETED)
self._overwrite_run_info(new_info)
def log_batch(self, run_id, metrics, params, tags):
_validate_run_id(run_id)
_validate_batch_log_data(metrics, params, tags)
_validate_batch_log_limits(metrics, params, tags)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
try:
for param in params:
self._log_run_param(run_info, param)
for metric in metrics:
self._log_run_metric(run_info, metric)
for tag in tags:
self._set_run_tag(run_info, tag)
except Exception as e:
raise MlflowException(e, INTERNAL_ERROR)
def delete_tag(self, run_id, key):
"""
Delete a tag from a run. This is irreversible.
:param run_id: String ID of the run
:param key: Name of the tag
"""
_validate_run_id(run_id)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
tag_path = self._get_tag_path(run_info.experiment_id, run_id, key)
if not exists(tag_path):
raise MlflowException(
"No tag with name: {} in run with id {}".format(key, run_id),
error_code=RESOURCE_DOES_NOT_EXIST)
os.remove(tag_path)
def record_logged_model(self, run_id, mlflow_model):
if not isinstance(mlflow_model, Model):
raise TypeError(
"Argument 'mlflow_model' should be mlflow.models.Model, got '{}'"
.format(type(mlflow_model)))
_validate_run_id(run_id)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
model_dict = mlflow_model.to_dict()
run_info = self._get_run_info(run_id)
path = self._get_tag_path(run_info.experiment_id, run_info.run_id,
MLFLOW_LOGGED_MODELS)
if os.path.exists(path):
with open(path, "r") as f:
model_list = json.loads(f.read())
else:
model_list = []
tag = RunTag(MLFLOW_LOGGED_MODELS,
json.dumps(model_list + [model_dict]))
try:
self._set_run_tag(run_info, tag)
except Exception as e:
raise MlflowException(e, INTERNAL_ERROR)
def set_tag(self, run_id, tag):
_validate_run_id(run_id)
_validate_tag_name(tag.key)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
self._set_run_tag(run_info, tag)
def log_param(self, run_id, param):
_validate_run_id(run_id)
_validate_param_name(param.key)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
self._log_run_param(run_info, param)
def log_metric(self, run_id, metric):
_validate_run_id(run_id)
_validate_metric_name(metric.key)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info)
self._log_run_metric(run_info, metric)
