SIENTIAPDE-1646

Update dependencies and refactor MLFlow activities

- Replaced direct GitHub dependencies in `requirements.txt` with specific versioned packages for `sientia_do` and `sientia_model`.
- Refactored imports in `activities.py` to streamline the code structure.
- Enhanced the `MLFlow` class in `mlflow.py` by introducing a method to resolve model aliases, improving flexibility in model lookups.
- Simplified shutdown logic in `worker.py` for better readability.
- Added new tests for MLFlow activities and improved existing test coverage for data handling and model retraining processes.

tests/laborious/workflows/subworkflows/test_prediction_process.py

@@ -1,6 +1,6 @@
 from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
 
-from pytest import fixture, mark
+from pytest import fixture, mark, raises
 
 from laborious.activities.activities import Activities
 from laborious.workflows.sub_workflows.prediction_process import PredictionProcess
@@ -840,3 +840,27 @@ async def test_run_with_cleanup_prefixes(workflow_mock, prediction_process):
         retry_policy=ANY,
         start_to_close_timeout=ANY,
     )
+
+
+@mark.asyncio
+@patch('laborious.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
+async def test_run_always_cleans_up_on_pipeline_exception(workflow_mock, prediction_process):
+    input_data = {
+        'metadata': metadata,
+        'data': {'last_timestamp': '2024-01-01'},
+        'model_id': 1,
+        'model_name': 'm',
+        'model_config': {},
+        'save_transform': False,
+    }
+    prediction_process._run_prediction_pipeline = AsyncMock(side_effect=RuntimeError('boom'))
+
+    with raises(RuntimeError):
+        await prediction_process.run(input_data)
+
+    workflow_mock.execute_activity_method.assert_called_once_with(
+        Activities.cleanup_minio_objects_expired,
+        {**metadata, 'data': input_data['data']},
+        retry_policy=ANY,
+        start_to_close_timeout=ANY,
+    )