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.

model_manager/activities/training.py

@@ -19,7 +19,6 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.observability.sientia_monitoring import SientiaMonitoring
 
     from model_manager.metrics import ACTIVITY_EXECUTION_TOTAL, WORKFLOW_EXECUTION_TOTAL
-    from model_manager.utils.exceptions import ModelTrainingError
     from model_manager.utils.models.train_model_params import TrainModelParams
     from model_manager.utils.repository.model_repository import ModelRepository
     from model_manager.utils.repository.storage_repository import StorageRepository
@@ -143,8 +142,6 @@ class Training(SientiaMonitoring):
             train_params = TrainModelParams.from_dict(train_params)
         # type: ignore[assignment]
 
-        model_trained = False
-        model_saved = False
         metrics_status = 'success'
 
         try:
@@ -157,9 +154,7 @@ class Training(SientiaMonitoring):
                     train_params, train_result
                 )
 
-                model_trained = True
                 train_result = self.model_repository.save_model(train_result)
-                model_saved = True
 
                 return {
                     'run_name': train_result.run_name,
@@ -168,11 +163,7 @@ class Training(SientiaMonitoring):
         except Exception as e:  # noqa: BLE001
             metrics_status = 'error'
 
-            error_msg = (
-                'Error training model - '
-                f'model_trained={model_trained}, model_saved={model_saved}, '
-                f'error: {str(e)}'
-            )
+            error_msg = f'Error training model - error: {str(e)}'
 
             trace = traceback.format_exc()
 
@@ -185,10 +176,7 @@ class Training(SientiaMonitoring):
                 attachment_content=trace,
             )
 
-            raise ModelTrainingError(
-                model_trained=model_trained,
-                model_saved=model_saved,
-            ) from e
+            raise e
         finally:
             await self._emit_metrics(
                 metadata=metadata,
@@ -206,28 +194,20 @@ class Training(SientiaMonitoring):
             input_data: Cleanup configuration containing:
                 - metadata (dict): Workflow execution metadata.
                 - run_dir (str): Temporary directory to remove.
-                - bucket_name (str): MinIO bucket of the uploaded file.
-                - file_name (str): MinIO object key to delete.
 
         Raises:
             Exception: If cleanup fails (after sending notification).
         """
         metadata = input_data.get('metadata', {})
         run_dir = input_data.get('run_dir', '')
-        bucket_name = input_data.get('bucket_name', '')
-        file_name = input_data.get('file_name', '')
         metrics_status = 'success'
 
         try:
             self.model_repository.cleanup_run_directory(run_dir)
-            self.storage_repository.delete_file(bucket_name, file_name)
         except Exception as e:  # noqa: BLE001
             metrics_status = 'error'
 
-            error_msg = (
-                f'Error cleaning up resources - Run directory: {run_dir}, '
-                f'File: {bucket_name}/{file_name}, Error: {str(e)}'
-            )
+            error_msg = f'Error cleaning up resources - Run directory: {run_dir}, Error: {str(e)}'
 
             trace = traceback.format_exc()