feat: enhance configuration and error handling in project setup

- Added new ignore rule for Ruff to allow temporary paths in tests.
- Introduced MyPy overrides for specific modules to ignore errors.
- Refactored `Cleanup` and `ExperimentTracking` classes to remove async keywords from methods, improving consistency in method signatures.
- Updated `Training` class methods to handle synchronous operations, enhancing performance and clarity.
- Adjusted `requirements.txt` to remove unnecessary Git dependency, streamlining project setup.

model_manager/workflows/train_model.py

@@ -21,7 +21,6 @@ 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.models.experiment_status import ExperimentStatus
-    from model_manager.utils.models.train_model_params import TrainModelParams
 
     # Activity timeouts (seconds). Tune per environment (large uploads, long training).
     # Training uses no_retry_policy: extend TIMEOUT_TRAIN_MODEL instead of adding retries
@@ -54,7 +53,6 @@ with workflow.unsafe.imports_passed_through():
     )
 
 
-
 @workflow.defn(name='train_model')
 class TrainModel:
     """
@@ -133,7 +131,7 @@ class TrainModel:
                     )
                 else:
                     pass
-            except Exception:
+            except Exception:  # noqa: BLE001
                 # If cleanup fails after training failed, there is nothing extra to log (DB not committed).
                 if training_succeeded:  # pragma: no branch
                     workflow.logger.warning(
@@ -179,7 +177,7 @@ class TrainModel:
         input_data: dict[str, Any],
         experiment_run_id: int,
         metadata: dict[str, Any],
-    ) -> TrainModelParams:
+    ) -> dict[str, Any]:
         """
         Validate and convert training parameters from dict to TrainModelParams.
 
@@ -193,7 +191,7 @@ class TrainModel:
             metadata: Workflow execution metadata
 
         Returns:
-            TrainModelParams: Validated training parameters object
+            dict[str, Any]: Validated training parameters
 
         Raises:
             Exception: If validation fails (after updating DB status)
@@ -220,7 +218,7 @@ class TrainModel:
             )
 
             await self._update_experiment_run(
-                metadata=metadata,  
+                metadata=metadata,
                 experiment_run_id=experiment_run_id,
                 update_type=UpdateType.STATUS,
                 status=ExperimentStatus.ORCHESTRATOR_WAITING_PROC,
@@ -236,7 +234,7 @@ class TrainModel:
                     status=ExperimentStatus.ORCHESTRATOR_VALIDATION_ERROR,
                     error_message=self._extract_error_message(e),
                 )
-            except Exception as secondary:
+            except Exception as secondary:  # noqa: BLE001
                 workflow.logger.warning(
                     'Failed to persist ORCHESTRATOR_VALIDATION_ERROR to experiment_run: %s',
                     secondary,
@@ -245,7 +243,7 @@ class TrainModel:
 
     async def _train_model(
         self,
-        train_params: TrainModelParams,
+        train_params: dict[str, Any],
         experiment_run_id: int,
         metadata: dict[str, Any],
     ) -> dict[str, Any]:
@@ -288,7 +286,6 @@ class TrainModel:
 
             return train_result
         except Exception as e:
-
             try:
                 await self._update_experiment_run(
                     metadata=metadata,
@@ -297,7 +294,7 @@ class TrainModel:
                     status=ExperimentStatus.TRAINING_ERROR,
                     error_message=self._extract_error_message(e),
                 )
-            except Exception as secondary:
+            except Exception as secondary:  # noqa: BLE001
                 workflow.logger.warning(
                     'Failed to persist TRAINING_ERROR status to experiment_run: %s',
                     secondary,