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.

tests/workflows/test_train_model.py

@@ -243,7 +243,9 @@ async def test_run_validation_error(mock_wf, sample_input_data):
 
 @pytest.mark.asyncio
 @patch('model_manager.workflows.train_model.workflow')
-async def test_run_cleanup_failure_does_not_fail_workflow(mock_wf, sample_input_data, mock_train_params):
+async def test_run_cleanup_failure_does_not_fail_workflow(
+    mock_wf, sample_input_data, mock_train_params
+):
     """After successful training, cleanup failure is logged, workflow still returns result."""
     from model_manager.workflows.train_model import TrainModel
 
@@ -267,7 +269,9 @@ async def test_run_cleanup_failure_does_not_fail_workflow(mock_wf, sample_input_
 
 @pytest.mark.asyncio
 @patch('model_manager.workflows.train_model.workflow')
-async def test_run_training_failure_skips_cleanup_activity(mock_wf, sample_input_data, mock_train_params):
+async def test_run_training_failure_skips_cleanup_activity(
+    mock_wf, sample_input_data, mock_train_params
+):
     """When train_model raises, train_result stays None and cleanup activity is not scheduled."""
     from model_manager.workflows.train_model import TrainModel
 
@@ -297,7 +301,6 @@ def test_module_constants():
     from model_manager.workflows.train_model import (
         TIMEOUT_DELETE_FILE,
         TIMEOUT_TRAIN_MODEL,
-        TIMEOUT_UPDATE_DATABASE,
         TIMEOUT_VALIDATE_PARAMS,
         database_retry_policy,
         network_retry_policy,