feat: update training workflow and repository management

- Replaced synchronous MinIO repository calls with asynchronous counterparts in the Training class for improved performance.
- Enhanced logging throughout the training process to provide better insights into model metadata loading, parameter validation, and training execution.
- Updated the train_test_split function to enforce DataFrame input type, ensuring consistency in data handling.
- Removed the deprecated model_repository.py file to streamline the codebase.
- Adjusted cleanup schedule logic to improve error handling and logging during schedule reconciliation.
- Updated tests to reflect changes in the training workflow and repository interactions.

tests/activities/test_training.py

@@ -93,7 +93,7 @@ def test_train_model_download_fails_notifies(training):
             'model_metadata': {'schemas': {'components': {'schemas': {}}}},
         }
     )
-    training.minio_repository.download_file_sync = MagicMock(side_effect=OSError('minio'))
+    training.minio_repository.download_file = MagicMock(side_effect=OSError('minio'))
     training.send_notification = MagicMock()
     with pytest.raises(OSError, match='minio'):
         training.train_model({'metadata': {'pod': 'x'}, 'train_params': tp.to_dict()})
@@ -129,7 +129,7 @@ def test_train_model_success_serializes_result(mock_mlflow, training):
     val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
     tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
 
-    training.minio_repository.download_file_sync = MagicMock(return_value=b'csv')
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
         side_effect=lambda x, _w: setattr(x, 'mse_val', 0.1) or x
@@ -185,7 +185,7 @@ def test_train_model_train_params_as_dict(mock_mlflow, training):
     tp = TrainModelParams.from_dict(d)
     tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
 
-    training.minio_repository.download_file_sync = MagicMock(return_value=b'csv')
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
         side_effect=lambda x, _w: x
@@ -240,7 +240,7 @@ def test_train_model_downloads_validation_file_when_set(mock_mlflow, training):
             return b'val'
         raise AssertionError(object_name)
 
-    training.minio_repository.download_file_sync = MagicMock(side_effect=_dl)
+    training.minio_repository.download_file = MagicMock(side_effect=_dl)
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
         side_effect=lambda x, _w: x
@@ -272,7 +272,7 @@ def test_train_model_downloads_validation_file_when_set(mock_mlflow, training):
     training.mlflow_repository.start_run = _run_ctx
 
     training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
-    assert training.minio_repository.download_file_sync.call_count == 2
+    assert training.minio_repository.download_file.call_count == 2
     mock_mlflow.log_artifact.assert_called()
 
 
@@ -288,7 +288,7 @@ def test_train_model_value_error_when_paths_missing_after_report(training):
     val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
     tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
 
-    training.minio_repository.download_file_sync = MagicMock(return_value=b'x')
+    training.minio_repository.download_file = MagicMock(return_value=b'x')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
         side_effect=lambda x, _w: x