SIENTIAPDE-1255: Implement object destructors for Activities and ExperimentTracking to prevent AttributeError during garbage collection, and mock workflow logger in tests to avoid RuntimeWarning.

model_manager/activities/activities.py

@@ -102,6 +102,26 @@ class Activities(ExperimentTracking, MLFlow, MinIO, Training):
 
         Training.__init__(self, logger=logger, notification_handler=notification_handler)
 
+    def __del__(self):
+        """
+        Destructor to safely handle cleanup during garbage collection.
+
+        This prevents AttributeError when the parent Postgres.__del__ tries to access
+        self.engine in objects with multiple inheritance. Only attempts cleanup if
+        the engine attribute exists.
+        """
+        # Only call parent __del__ if engine attribute exists
+        # This prevents AttributeError in multiple inheritance scenarios
+        if hasattr(self, 'engine'):
+            try:
+                # Call parent class __del__ if it exists
+                if hasattr(super(), '__del__'):
+                    super().__del__()
+            except Exception:  # noqa: S110, BLE001
+                # Silently ignore errors during garbage collection
+                # Logging here could cause issues if logger is already destroyed
+                pass
+
     async def shutdown(self):
         """
         Gracefully shutdown all activities and clean up resources.
@@ -112,5 +132,7 @@ class Activities(ExperimentTracking, MLFlow, MinIO, Training):
 
         The method should be called before the application terminates to ensure
         proper resource cleanup and prevent resource leaks.
+
+        Prefer calling this method explicitly rather than relying on __del__.
         """
         ExperimentTracking.close(self)

model_manager/activities/experiment_tracking.py

@@ -88,6 +88,23 @@ class ExperimentTracking(Postgres):
         self.logger = logger
         self.notification_handler = notification_handler
 
+    def __del__(self):
+        """
+        Destructor to safely handle cleanup during garbage collection.
+
+        This prevents AttributeError when used in multiple inheritance scenarios
+        where the parent Postgres.__del__ might be called on objects without
+        the engine attribute.
+        """
+        # Only call parent __del__ if engine attribute exists
+        if hasattr(self, 'engine'):
+            try:
+                if hasattr(super(), '__del__'):
+                    super().__del__()
+            except Exception:  # noqa: S110, BLE001
+                # Silently ignore errors during garbage collection
+                pass
+
     @activity.defn(name='update_experiment_run')
     async def update_experiment_run(self, input_data: dict[str, Any]) -> None:
         """

tests/workflows/test_train_model.py

@@ -89,6 +89,9 @@ async def test_run_success_complete_flow(
         'removed_intervals': [],
     }
 
+    # Mock workflow.logger to avoid RuntimeWarning about unawaited coroutines
+    workflow_mock.logger = MagicMock()
+
     # Mock activity responses
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
@@ -264,6 +267,9 @@ async def test_validate_training_parameters_validation_error(
     input_data = {'experiment_run_id': 123}
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    # Mock workflow.logger to avoid RuntimeWarning about unawaited coroutines
+    workflow_mock.logger = MagicMock()
+
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             ValueError('Missing required field'),  # validate_train_params fails
@@ -318,6 +324,9 @@ async def test_download_and_train_model_download_error(
     """Test download error is handled correctly."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    # Mock workflow.logger to avoid RuntimeWarning about unawaited coroutines
+    workflow_mock.logger = MagicMock()
+
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             Exception('MinIO connection failed'),  # fetch_file_from_minio fails
@@ -342,6 +351,9 @@ async def test_download_and_train_model_training_error(
     """Test training error is handled correctly."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    # Mock workflow.logger to avoid RuntimeWarning about unawaited coroutines
+    workflow_mock.logger = MagicMock()
+
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             b'file_content',  # fetch_file_from_minio succeeds
@@ -396,6 +408,7 @@ async def test_download_and_train_model_closes_bytesio_on_error(
     """Test that BytesIO is closed in finally block even on error."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     # Create a mock BytesIO with close method
     mock_file = MagicMock()
     mock_file.close = MagicMock()
@@ -425,6 +438,7 @@ async def test_download_and_train_model_handles_file_without_close(
     """Test that workflow handles file objects without close method gracefully."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     # Create a mock file without close method
     mock_file = MagicMock(spec=[])
 
@@ -457,6 +471,7 @@ async def test_save_model_to_mlflow_success(
     """Test successful model saving to MLFlow."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             mock_train_result,  # save_model
@@ -480,6 +495,7 @@ async def test_save_model_to_mlflow_error(
     """Test MLFlow save error is handled correctly."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             Exception('MLFlow connection failed'),  # save_model fails
@@ -511,6 +527,7 @@ async def test_cleanup_resources_success(
     """Test successful resource cleanup."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             None,  # cleanup_run_directory
@@ -537,6 +554,7 @@ async def test_cleanup_resources_delete_error(
     """Test cleanup handles delete errors correctly."""
     metadata = {'metadata': {'experiment_run_id': 123, 'workflow_name': 'train_model'}}
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(
         side_effect=[
             None,  # cleanup_run_directory succeeds