- Introduced experiment_name parameter in TrainModelResult to enhance tracking of training experiments.
- Updated the Training class to utilize run_name and experiment_name for improved MLflow run management.
- Added debug logging for data preparation, transformation, and prediction steps in the Training class to improve traceability.
- Updated compute_regression_metrics method to include metadata for better debugging and validation of index alignment between true and predicted values.
- Updated image tag in values.yaml from "1.0.0" to "1.0.2" for the latest version.
- Modified STORE_BASE_URL to include port 3000 for proper service access.
- Changed import from MinioRepository to MinioRepositorySync for synchronization support.
- Updated import from Postgres to PostgresSync to enhance experiment tracking capabilities.
- Renamed TRAIN_TASK_QUEUE from "train_model-single-queue" to "train_model-basic-queue" for clarity.
- Changed project name in values.yaml from "sientia-dataops-model-manager" to "sientia-model-manager".
- Added new environment variables for GitHub repository and branch configuration.
- Refactored cleanup paths to use a centralized REPORTS_TEMP_DIR constant for consistency.
- Updated runtime configurations and adjusted volume mounts for better resource management.
- Enabled SSH access for the model manager and disabled Grafana dashboard creation.
- Updated tests to reflect changes in directory paths and environment variable usage.
- 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.
- Updated `.env.example` to include new environment variables for MinIO and PyPI configuration.
- Refactored `create_cleanup_schedule` to utilize runtime-specific task queues and improve schedule reconciliation logic.
- Enhanced `Activities` class to require a default bucket in MinIO configuration.
- Adjusted `requirements.txt` to specify version for `evidently`.
- Updated tests to reflect changes in schedule creation and configuration handling.
- 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.
- Added `evidently` to requirements for improved model evaluation.
- Introduced `TrainModelResult` class with a `to_dict` method for better result handling.
- Updated `train_model` method to return a comprehensive training result, including run details.
- Enhanced `cleanup_run_directory` method in `DataManagerRepository` for improved resource management.
- Adjusted type hints in `TrainModel` for clarity and consistency.
- Added functionality to store run name and ID in the training results.
- Implemented report generation in the `DataManagerRepository`, including methods to create a run directory and generate comprehensive reports.
- Updated `TrainModelResult` to include `run_id` and `run_dir` attributes for better tracking of training sessions.
- Eliminated MinIO repository dependencies from the `Activities`, `Cleanup`, and `Training` classes.
- Updated the `cleanup_resources` method to focus on removing local temporary directories instead of handling MinIO file deletions.
- Adjusted the `TrainModel` class to pass the run directory for cleanup, enhancing resource management in the training workflow.
- Updated the `Training` class to raise `ModelTrainingError` on training failures for better error management.
- Enhanced the `run` method in `TrainModel` to return training results and ensure proper resource cleanup, including validation files.
- Refactored exception handling to prevent silent failures during resource cleanup and experiment run updates.
- Adjusted type hints for improved clarity and consistency in method signatures.
- 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.
- Added PluginStore integration for model management.
- Replaced StorageRepository with MinIORepository in Activities, Cleanup, and Training classes.
- Updated training logic to handle validation files and improved data management.
- Enhanced configuration for MinIO and PluginStore in connectors.
- Removed deprecated model repository and storage repository files.
- Updated environment variable handling for new configurations.
This commit removes redundant logging statements from training and experiment tracking activities, preventing duplicate log entries. It also introduces a logger helper to disable log propagation, further addressing the duplicate logs issue. Additionally, the Makefile, run_coverage.sh, setup_port_forwards.sh, and simulator/Dockerfile files were removed as they are no longer needed.
This commit refactors the MLFlow activities to focus on model training rather than prediction operations. It removes prediction-related activities and metrics, and updates the MLFlow activity descriptions to reflect the change in focus. The README is also updated to reflect these changes.
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.
This commit introduces MinIO integration for object storage within the Model Manager system. It includes:
- Added MinIO activity class for file operations (fetch, delete).
- Updated Activities orchestrator to include MinIO activities.
- Added MinIO configuration builder to utils/connectors_config.py.
- Added environment variables for MinIO configuration in .env.example.
- Added boto3 and botocore dependencies to requirements.txt.
- Added unit tests for MinIO activities.
This commit includes several changes:
- Reorganized imports and class inheritance in activities.py, gates.py and mlflow.py for better readability and maintainability.
- Improved error handling and logging in gates.py and mlflow.py.
- Added input validation and filtering in gates.py to ensure data quality.
- Enhanced prediction formatting and storage policy management in gates.py.
- Updated metrics.py to use consistent naming conventions and labels.
- Refactored connectors_config.py to use type hints and improve code clarity.
- Updated conditional and MLFlow filters for better data quality checks.
- Improved model repository logic for retraining and updating models.
- Enhanced worker.py to include SDK metrics and improved error handling.
- Refactored workflows for better modularity and error handling.
- Updated tests to reflect the changes and improve test coverage.
This commit removes the OPC server integration from the Model Manager, including related activities, repositories, metrics, and configuration. It also adds code quality tools such as Ruff (linting/formatting), mypy (type checking), and Bandit (security analysis) along with a validation script and CI/CD integration for automated code validation. The README has been updated to reflect these changes.
This commit renames the 'laborious' package to 'model_manager' across the entire project. This includes renaming directories, modules, references in code, configuration files, and documentation to reflect the new package name. This change improves clarity and consistency within the project.
