- Added new entries to .gitignore to exclude temporary files and training input datasets, ensuring a cleaner repository.
- Included type stubs for psycopg2 in requirements-dev.txt to enhance type checking support for database interactions.
- Refactored the run_training_test.py script to implement a structured approach for loading and validating training input JSON files, improving the robustness of the training workflow.
- Renamed columns in `input_dataset.csv` from `feature_a`, `feature_b`, and `target` to `Counter`, `Rollout`, and `Square` for better clarity.
- Updated the `run_training_test.py` script to reflect the new column names in the workflow input, ensuring consistency in data processing.
- Added `date_column` parameter to the workflow input for improved data handling.
- 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.
- 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.
- Modified `.env.example` to set local defaults for PostgreSQL, MLflow, and MinIO configurations.
- Added MongoDB configuration parameters to the environment setup.
- Updated `README.md` to reflect changes in workflow input parameters and task queue naming conventions.
- Removed the `ModelServing` class to streamline the codebase, as it was deemed unnecessary.
- Adjusted `connectors_config.py` to align with new environment variable names and improve clarity.
- Updated tests to reflect changes in configuration handling and removed tests related to the deleted `ModelServing` class.
- 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.
- Introduce --all argument to run_training_test.py to execute all available scenarios sequentially.
- Refactor single scenario execution into a run_single_scenario function for better modularity and error handling.
- Implement a print_summary function to report results for multiple scenario runs.
