def gc(backend_store_uri, run_ids):
"""
Permanently delete runs in the `deleted` lifecycle stage from the specified backend store.
This command deletes all artifacts and metadata associated with the specified runs.
"""
backend_store = _get_store(backend_store_uri, None)
if not hasattr(backend_store, '_hard_delete_run'):
raise MlflowException(
"This cli can only be used with a backend that allows hard-deleting runs"
)
if not run_ids:
run_ids = backend_store._get_deleted_runs()
else:
run_ids = run_ids.split(',')
for run_id in run_ids:
run = backend_store.get_run(run_id)
if run.info.lifecycle_stage != LifecycleStage.DELETED:
raise MlflowException(
'Run {} is not in `deleted` lifecycle stage. Only runs in '
'`deleted` lifecycle stage can be deleted.'.format(run_id))
artifact_repo = get_artifact_repository(run.info.artifact_uri)
artifact_repo.delete_artifacts()
backend_store._hard_delete_run(run_id)
print("Run with ID %s has been permanently deleted." % str(run_id))
def describe_run(run_id):
"""
All of run details will print to the stdout as JSON format.
"""
store = _get_store()
run = store.get_run(run_id)
json_run = json.dumps(run.to_dictionary(), indent=4)
print(json_run)
def restore_run(run_id):
"""
Restore a deleted run.
Returns an error if the run is active or has been permanently deleted.
"""
store = _get_store()
store.restore_run(run_id)
print("Run with id %s has been restored." % str(run_id))
def delete_run(run_id):
"""
Mark a run for deletion. Return an error if the run does not exist or
is already marked. You can restore a marked run with ``restore_run``,
or permanently delete a run in the backend store.
"""
store = _get_store()
store.delete_run(run_id)
print("Run with ID %s has been deleted." % str(run_id))
def list_artifacts(run_id, artifact_path):
"""
Return all the artifacts directly under run's root artifact directory,
or a sub-directory. The output is a JSON-formatted list.
"""
artifact_path = artifact_path if artifact_path is not None else ""
store = _get_store()
artifact_uri = store.get_run(run_id).info.artifact_uri
artifact_repo = get_artifact_repository(artifact_uri)
file_infos = artifact_repo.list_artifacts(artifact_path)
print(_file_infos_to_json(file_infos))
def log_artifacts(local_dir, run_id, artifact_path):
"""
Log the files within a local directory as an artifact of a run, optionally
within a run-specific artifact path. Run artifacts can be organized into
directories, so you can place the artifact in a directory this way.
"""
store = _get_store()
artifact_uri = store.get_run(run_id).info.artifact_uri
artifact_repo = get_artifact_repository(artifact_uri)
artifact_repo.log_artifacts(local_dir, artifact_path)
_logger.info("Logged artifact from local dir %s to artifact_path=%s",
local_dir, artifact_path)
def list_run(experiment_id, view):
"""
List all runs of the specified experiment in the configured tracking server.
"""
store = _get_store()
view_type = ViewType.from_string(view) if view else ViewType.ACTIVE_ONLY
runs = store.search_runs([experiment_id], None, view_type)
table = []
for run in runs:
tags = {k: v for k, v in run.data.tags.items()}
run_name = tags.get(MLFLOW_RUN_NAME, "")
table.append([conv_longdate_to_str(run.info.start_time), run_name, run.info.run_id])
print(tabulate(sorted(table, reverse=True), headers=["Date", "Name", "ID"]))
def download_artifacts(run_id, artifact_path, artifact_uri):
"""
Download an artifact file or directory to a local directory.
The output is the name of the file or directory on the local disk.
Either ``--run-id`` or ``--artifact-uri`` must be provided.
"""
if run_id is None and artifact_uri is None:
_logger.error(
"Either ``--run-id`` or ``--artifact-uri`` must be provided.")
sys.exit(1)
if artifact_uri is not None:
print(_download_artifact_from_uri(artifact_uri))
return
artifact_path = artifact_path if artifact_path is not None else ""
store = _get_store()
artifact_uri = store.get_run(run_id).info.artifact_uri
artifact_repo = get_artifact_repository(artifact_uri)
artifact_location = artifact_repo.download_artifacts(artifact_path)
print(artifact_location)