- Added a new fixture to manage runtime report artifacts in a writable temp directory during E2E tests, addressing permission issues in local CI/dev environments.
- Updated `conftest.py` to include a requirements.txt file in the model packaging path for training activities.
- Refactored existing fixtures to use `pytest.fixture` instead of `pytest_asyncio.fixture` for better compatibility.
- Enhanced the `Reports` class to include a target alias for report metrics, ensuring compatibility with Evidently's reporting requirements.
- Introduced new test scenarios to validate the handling of missing and whitespace-only `date_column` inputs in the training workflow.
These changes improve the robustness of the E2E testing framework and enhance the clarity of model reporting metrics.
- Made `date_column` a required field in `TrainModelParams`, ensuring it must be present in the input data.
- Updated related documentation in `input-sample.md`, `README.md`, and various test scenarios to reflect the change in requirement.
- Adjusted the handling of `date_format` to default to `yyyy-MM-dd HH:mm:ss` if omitted, enhancing usability.
- Refined test scenarios to include new examples and ensure compliance with the updated parameter structure.
These changes improve the robustness of the model training workflow and clarify the expectations for input data.
- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact 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.
- 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.
- Updated `Activities` class to improve garbage collection handling.
- Enhanced error messaging in `ExperimentTracking` for better clarity on update failures.
- Refactored `Training` class to streamline exception handling and improve type hints.
- Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata.
- Added functionality to extract model equations in `DataManagerRepository` for linear regression models.
- Introduced a new activity to load model metadata from the model store.
- Refactored training logic to utilize new model metadata and improved parameter handling.
- Updated the `TrainModelParams` class to include additional fields for model configuration.
- Replaced deprecated utility functions with a custom train-test split implementation.
- Removed unused utility functions and cleaned up the data manager repository.
- Adjusted experiment tracking to include model-specific metadata in notifications.
This commit refactors the training workflow and associated activities to raise exceptions on failure instead of returning success/failure dictionaries. This allows the Temporal workflow to handle errors more effectively and ensures that the workflow stops when a critical error occurs.
Key changes:
- The train_model workflow is introduced to orchestrate the entire training process, including parameter validation, data download, model training, and model saving.
- The validate_train_params activity is added to validate and convert training parameters.
- The train_model and save_model activities are updated to raise exceptions on failure.
- The ExperimentStatus enum is updated to include a new status for orchestrator validation errors.
- The tests are updated to reflect the new exception-based error handling.
- The activities now return the TrainModelResult directly instead of a dictionary.
This commit introduces the 'Training' activity and 'TrainingRepository' for handling ML model training operations within the Model Manager system.
- Added model_manager/activities/training.py for the Training activity, which extends BaseActivity and integrates with Temporal workflows.
- Added model_manager/utils/repository/training_repository.py for the TrainingRepository, which encapsulates the core training logic.
- Updated model_manager/activities/activities.py to include the Training activity in the main activities orchestrator.
- Updated README.md to document the new 'Training' component.
- Added unit tests for the new activity and repository.
