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SIENTIAPDE-1717: Remove MinIO cleanup functionality and associated components. This change streamlines the cleanup workflow to focus solely on local temporary directories, removes the ModelTrainingError exception, and updates related configurations, documentation, and tests.

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This commit is contained in:
Bruno Domingues
2026-03-30 14:14:10 -03:00
parent 63a94dae0a
commit 7a0961f29d
20 changed files with 56 additions and 778 deletions
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71
model_manager/workflows/train_model.py
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@@ -20,7 +20,6 @@ with workflow.unsafe.imports_passed_through():
from model_manager.activities.activities import Activities
from model_manager.activities.experiment_tracking import UpdateType
from model_manager.utils.exceptions import ModelTrainingError
from model_manager.utils.models.experiment_status import ExperimentStatus
from model_manager.utils.models.train_model_params import TrainModelParams
@@ -117,10 +116,7 @@ class TrainModel:
)
await self._cleanup_resources(
experiment_run_id=experiment_run_id,
run_dir=(train_result.get('run_dir') or ''),
bucket_name=train_params.bucket_name,
file_name=train_params.file_name,
metadata=metadata,
)
@@ -246,27 +242,17 @@ class TrainModel:
metadata=metadata,
experiment_run_id=experiment_run_id,
update_type=UpdateType.MODEL_SAVED,
status=ExperimentStatus.TRACKING_SENT,
status=ExperimentStatus.TRAINING_SUCCESS,
run_name=train_result.get('run_name'),
)
return train_result
except Exception as e:
# Mapear flags -> status
# False/False: erro no treino
# True/False: erro ao salvar (MLflow)
# False/True: estado inconsistente, tratar como erro de treino
# True/True: não deveria cair aqui; tratar como erro genérico de treino
status = ExperimentStatus.TRAINING_ERROR
if isinstance(e, ModelTrainingError) and (e.model_trained and not e.model_saved):
status = ExperimentStatus.TRACKING_SEND_ERROR
await self._update_experiment_run(
metadata=metadata,
experiment_run_id=experiment_run_id,
update_type=UpdateType.STATUS_WITH_ERROR,
status=status,
status=ExperimentStatus.TRAINING_ERROR,
error_message=self._extract_error_message(e),
)
@@ -274,56 +260,27 @@ class TrainModel:
async def _cleanup_resources(
self,
experiment_run_id: int,
run_dir: str,
bucket_name: str,
file_name: str,
metadata: dict[str, Any],
) -> None:
"""
Cleanup resources and delete file from MinIO.
Cleanup resources.
This method removes the temporary run directory via activity and deletes
the training file from MinIO. On success, updates DB status to FILE_DELETED.
On error, updates DB status to FILE_DELETE_ERROR.
This method removes the temporary run directory via activity.
Args:
saved_result: TrainModelResult with run_dir and params information
experiment_run_id: Validated experiment run ID
run_dir: Temporary directory to remove
metadata: Workflow execution metadata
Raises:
Exception: If cleanup fails (after updating DB status)
"""
try:
await workflow.execute_activity_method(
Activities.cleanup_resources,
{
**metadata,
'run_dir': run_dir,
'bucket_name': bucket_name,
'file_name': file_name,
},
retry_policy=network_retry_policy,
start_to_close_timeout=timedelta(seconds=TIMEOUT_DELETE_FILE),
)
await self._update_experiment_run(
metadata=metadata,
experiment_run_id=experiment_run_id,
update_type=UpdateType.STATUS,
status=ExperimentStatus.FILE_DELETED,
)
except Exception as e:
await self._update_experiment_run(
metadata=metadata,
experiment_run_id=experiment_run_id,
update_type=UpdateType.STATUS_WITH_ERROR,
status=ExperimentStatus.FILE_DELETE_ERROR,
error_message=self._extract_error_message(e),
)
raise
await workflow.execute_activity_method(
Activities.cleanup_resources,
{
**metadata,
'run_dir': run_dir,
},
retry_policy=network_retry_policy,
start_to_close_timeout=timedelta(seconds=TIMEOUT_DELETE_FILE),
)
async def _update_experiment_run(
self,