Refactor OPC class to remove unused opc_servers attribute and streamline initialization. Update OpcRepository to consolidate server name tags for metric emissions.
SIENTIAPDE-1325 Add OPC server name configuration and update repository handling
- Introduced a new environment variable `OPC_SERVER_NAME` in values.yaml with a default value.
- Updated the OPC class to include `server_name` when initializing OpcRepository.
- Enhanced the configuration builder to retrieve `OPC_SERVER_NAME` from the environment.
- Adjusted OpcRepository to store and utilize the `server_name` for metric emissions.
Refactor OPC metrics in metrics.py and opc_repository.py
- Replaced existing OPC connection metrics with new metrics for total connections and failed connections.
- Updated the OPC connection status metric to include server URL and adjusted corresponding metric emissions in opc_repository.py.
Update image tag and enhance OPC metrics tracking
- Bumped image tag from "1.0.1" to "1.1.0" in values.yaml.
- Added new metrics for OPC connection count and error count in metrics.py.
- Refactored OPC connection handling in opc_repository.py to emit new metrics for connection status, count, and errors.
Refactor monitoring and metrics integration across various components
- Removed coverage options from `pyproject.toml`.
- Updated prediction metrics in `README.md` to replace `pipeline_name` with `workflow_name`.
- Upgraded `sientia-dataops-library` dependency version in `requirements-light.txt` and `requirements.txt`.
- Enhanced metrics handling in `laborious` activities, including `Activities`, `Gates`, `MLFlow`, and `OPC`, to utilize a new `MetricsController`.
- Refactored metric emission methods to improve clarity and consistency across the codebase.
- Updated tests to reflect changes in metrics handling and ensure proper functionality.
Enhance thread safety in MLFlowRepository model caching
- Introduced a reentrant lock to synchronize access to the model cache, ensuring thread safety during cache checks and updates.
- Updated the cache management logic to acquire the lock when checking for existing models and when updating the cache after downloading a new model.
- Reduced the maximum cached workflows in the worker configuration for improved resource management.
Refactor model cache deletion in MLFlowRepository
- Updated the model cache deletion logic to remove the entire 'target' key instead of just the 'model' subkey, streamlining the cache management process.
Refactor OPC Class and Enhance Testing for Output Management
- Removed the unused 'success' parameter from the manage_output_tags method in the OPC class to streamline its signature.
- Commented out the error handling logic in the OpcRepository for clarity and future reference.
- Added new tests for manage_output_tags to validate successful and failed write operations, ensuring accurate assertions for output data and metrics.
Refactor OPC Write Method and Comment Out Error Handling Logic
- Reformatted the `write_opc_data` method for improved readability by adjusting its signature.
- Commented out the error handling logic in `OpcRepository` related to disconnection due to multiple errors, preserving the original functionality for future reference.
- Updated tests to reflect the commented-out error handling, ensuring clarity in the testing process.
Enhance OPC Metrics Handling and Refactor Write Operations
- Updated the OPC class to return response times for write operations, improving metrics tracking.
- Refactored the Gates activity to incorporate OPC metrics into the metrics writing process.
- Adjusted the manage_output_tags method in OpcRepository to return response times for each tag written.
- Modified tests to validate the new metrics structure and ensure correct behavior of the updated methods.
Refactor error handling in MLFlowRepository and update tests
- Improved error message formatting in MLFlowRepository for better readability.
- Updated test assertions to ensure correct calls to MLflow methods during experiment retrieval and creation.
Update tests notebook execution count and bump image tag in values.yaml
- Incremented the execution count in the tests notebook for accurate tracking.
- Updated the image tag in values.yaml from "0.0.3" to "1.0.1" for versioning consistency.
- Enhanced error handling in model_repository.py to ensure proper retrieval of created experiments.
Refactor OPC handling by removing pod_id from initialization and updating logging format
- Removed pod_id parameter from OPC class and repository initialization to streamline connection management.
- Updated logging statements for improved readability during disconnection attempts and error handling.
Enhance OpcRepository client initialization and error handling
- Updated the Client instantiation in OpcRepository to include a timeout and watchdog interval for improved connection management.
- Added a disconnection call in the exception handling block to ensure proper resource cleanup during connection failures.
Update dependencies and enhance OpcRepository functionality
- Updated sientia-dataops-library version to 1.4.7 in requirements files.
- Modified GITHUB_BRANCH in values.yaml for improved pipeline management.
- Refactored OpcRepository class to inherit from BaseActivity, adding enhanced logging and error handling during disconnection.
- Implemented a disconnection fallback mechanism to ensure graceful handling of OPC server disconnections.
Enhance README and repository utilities for clarity and functionality
- Updated README.md to improve descriptions and structure, adding detailed sections for features, workflows, and architecture.
- Enhanced MinioRepository with comprehensive docstrings for methods and class attributes, improving usability and documentation.
- Refined MLFlowRepository with clearer method descriptions and improved logging for better observability and maintainability.
Refactor tests and update model_repository.py for clarity and consistency
- Added a blank line in model_repository.py for improved readability.
- Adjusted formatting in test_mlflow.py to streamline assertions.
- Updated ensure_bucket_exists method tests in test_minio_repository.py to reflect the new return value of None instead of True.
Refactor MinioRepository and MLFlowRepository for improved functionality and error handling
- Updated `ensure_bucket_exists` method in MinioRepository to return None instead of a boolean, streamlining bucket existence checks.
- Replaced hardcoded error messages in MLFlowRepository with a constant for better maintainability.
- Adjusted column assignment in MLFlowRepository to use pd.Index for improved clarity.
Enhance validation script and refactor code in various modules
- Updated the validation script to include automatic code formatting and linting fixes using Ruff.
- Removed the `clean_tmp_files` method from the Gates class to streamline functionality.
- Simplified conditional checks in the OpcRepository for better clarity and error handling.
- Added model ID to the minimal retrain workflow for improved tracking.
- Introduced new test cases for error handling in MLFlow and storage operations, ensuring robustness in repository interactions.
Update .gitignore and refactor metrics.py for improved logging and consistency
- Added coverage.xml to .gitignore to prevent tracking of coverage reports.
- Refactored metric labels in metrics.py for consistency in string formatting and improved readability.
- Enhanced logging messages in various activities to ensure uniformity in message formatting.
Refactor `force_memory_release` function in model_repository.py to improve logging
- Updated the `force_memory_release` function to remove metadata parameter and enhance logging by using `logger.info()` instead of `logger.custom_info()`.
- Adjusted calls to `force_memory_release` in the MLFlowRepository to pass the logger instance, ensuring consistent logging during memory management operations.
Refactor memory management in MLFlowRepository to use `force_memory_release` function
- Replaced direct calls to `gc.collect()` with `force_memory_release()` for improved memory optimization after model deletion.
- This change enhances memory management during model operations, ensuring more efficient resource handling.
Add memory management function to model_repository.py
- Introduced `force_memory_release` function to enhance memory management by triggering garbage collection and attempting to release unused memory.
- Utilized `ctypes` to call `malloc_trim` for further memory optimization, improving overall performance during model operations.
Enhance MLFlowRepository memory management by adding garbage collection and logging for model deletion
- Introduced garbage collection after model deletion to optimize memory usage.
- Added logging to inform when a model is deleted from memory, improving traceability during predictions.
Update values.yaml and model_repository.py for configuration and error handling improvements
- Increased replicaCount from 1 to 2 in values.yaml for enhanced scalability.
- Updated image tag from "0.0.2" to "0.0.3" in values.yaml to reflect the latest version.
- Enhanced error messaging in model_repository.py to include the actual index type when raising ValueError for index type validation.
Comment out CSV export lines in MLFlowRepository to prevent temporary file creation during model operations. This change enhances data handling by avoiding unnecessary file writes while maintaining logging functionality.
Enhance Gates and MLFlowRepository with new functionalities and improvements
- Added a new method `clean_tmp_files` in the Gates class to remove temporary files associated with model retraining.
- Updated MLFlowRepository methods to improve experiment handling, including dynamic parameter logging and model retrieval.
- Refactored model loading methods to streamline the process and enhance error handling.
- Improved logging for model operations and added support for model parameter retrieval.
- Adjusted minimal_retrain workflow to extend timeouts for activities and ensure proper model configuration handling.
Enhance MinIO integration and update environment configurations
- Added MinIO configuration parameters to .env.example and values.yaml for improved storage management.
- Updated requirements.txt to include necessary libraries for MinIO support.
- Refactored Activities class to utilize Storage for MinIO interactions.
- Enhanced MLFlow class to integrate MinIO for data retrieval during model retraining.
- Introduced build_minio_config function to streamline MinIO configuration setup.
- Updated minimal_retrain workflow to support data storage in MinIO.
Update .gitignore, values.yaml, and model_repository.py for improved data handling and logging
- Added 'catboost_info/' to .gitignore to prevent tracking of additional temporary files.
- Updated GITHUB_BRANCH in values.yaml to reflect the current branch for model retraining.
- Enhanced model_repository.py to drop duplicate timestamps in treated data and streamline attribute logging during model retraining.
Enhance MLFlow and MLFlowRepository with improved data handling and logging
- Refactored MLFlow class to sort data by 'created_at' and drop duplicates for better input preparation.
- Updated MLFlowRepository methods to include detailed logging for artifact downloads and model predictions.
- Introduced LzmaPayloadCodec for efficient payload compression in the worker, optimizing data handling for large payloads.
- Enhanced timestamp handling in treated data to ensure compatibility with model expectations.
Update model retraining and logging enhancements
- Changed the GITHUB_BRANCH value in values.yaml to 'main' for consistency.
- Refactored MLFlow class to improve timestamp handling and error messaging during model retraining.
- Enhanced MLFlowRepository methods to include metadata logging and improved model version retrieval.
- Updated minimal_retrain workflow to support extended timeout for activities and include model configuration in input data.
Update model retraining and reporting functionality
- Changed the GITHUB_BRANCH value in values.yaml to reflect the latest adjustments for retraining the courier.
- Enhanced the Gates class with a new method `format_retrain_report` to format retraining report data according to storage policies.
- Refactored the MLFlow class to improve error handling during model retraining and return structured output.
- Updated the model_repository to utilize the latest MLFlow API for retrieving model versions and improved logging.
- Modified the minimal_retrain workflow to conditionally update the production model based on retraining success.
SIENTIAPDE-1222
Enhance MLFlow and MLFlowRepository with model configuration support
- Introduced `model_config` parameter in MLFlow methods to streamline model handling and configuration management.
- Updated `retrain_model`, `transform`, and `predict` methods to accept `model_config` and `metadata` for improved flexibility and logging.
- Added `detect_and_parse_datetime_index` method to handle datetime index parsing with enhanced error handling and logging.
- Refactored model experiment creation to include transformation and prediction flavors, along with compression options.
- Improved documentation and type hints across methods for better clarity and usability.
Update model configuration keys in MLFlowRepository for consistency
- Changed 'model_retention' to 'retention_minutes' and 'is_compressed' to 'compressed' in model configuration handling, ensuring alignment with updated configuration standards.
Update image tag in values.yaml and enhance debug logging in Gates and MLFlow activities
- Updated the image tag in values.yaml from '0.0.1' to '0.0.2'.
- Improved debug logging in the Gates activity to format input data and filters for better readability.
- Enhanced MLFlow activity logging to include formatted output for raw and transformed response data, ensuring consistent logging format.
Refactor OPC and model repository for improved functionality and clarity
- Updated OPC server logging to handle missing prediction and confidence tags gracefully.
- Corrected documentation for OPC reconnection interval from milliseconds to seconds.
- Enhanced MLFlowRepository with new methods for model retrieval, caching, and transformation, improving model management and retraining workflows.
Update tests.ipynb and values.yaml for improved functionality and versioning
- Updated execution count in tests.ipynb for reproducibility.
- Modified DataFrame creation in tests.ipynb to include a timestamp column and save to CSV.
- Changed image tag in values.yaml from "0.4.4" to "0.4.5" for versioning.
- Updated GITHUB_BRANCH in values.yaml to reflect the latest branch adjustments.
Refactor connectors_config.py and conditional_filters.py for improved configuration management and data filtering
- Updated PostgreSQL and MLFlow configuration functions to enhance default values and environment variable handling.
- Simplified OPC server configuration logic and improved MongoDB connection string construction.
- Refactored conditional filters to streamline null value checks and empty data validation, removing unnecessary comments and examples for clarity.
- Removed extensive module docstrings to enhance code readability.
Update README.md to enhance installation instructions and refactor MLFlow filters
- Added steps for installing GitHub CLI and authenticating with GitHub.
- Updated the `api_error_filter` and `nan_values_filter` functions to improve parameter handling and streamline logic.
Remove all module docstrings and the versioning information from the Laborious package, activities, utils, and workflows. This cleanup enhances code readability and reduces unnecessary comments in the codebase.
Remove Docker configuration files and refactor project structure
- Deleted docker-compose.yml and Dockerfile as part of the project restructuring.
- Updated README.md to reflect changes in project setup and configuration.
- Introduced a new __init__.py file in the laborious package to provide an overview of the system.
- Enhanced documentation across various modules, including metrics, activities, and workflows, to improve clarity and usability.
- Added comprehensive docstrings and comments to key classes and methods for better maintainability.
Refactor OpcRepository and update tests for async handling
- Removed the shutdown method from OpcRepository and adjusted the disconnect logic.
- Updated tests in test_activities.py and test_opc.py to support async shutdown functionality.
- Enhanced test cases in test_opc_repository.py to ensure proper async behavior and error handling in OpcRepository methods.
Revert image tag in values.yaml to 0.4.4 and rename shutdown method in OpcRepository class
- Changed image tag in values.yaml from 0.4.5 back to 0.4.4.
- Renamed __del__ method to shutdown in OpcRepository for clarity.
- Enhanced connection validation logic in OpcRepository to improve error handling and logging.
- Expanded the .env file with configurations for MongoDB, Postgres, MlFlow, and Temporal.
- Refactored OPC class methods to be asynchronous, including init_opc, write_data, manage_output_tags, and shutdown.
- Updated the worker to initialize OPC asynchronously and adjusted shutdown handling for activities.
Enhance logging in OpcRepository to include metadata for all log messages
- Introduced a metadata dictionary to the OpcRepository class for improved context in logging.
- Updated logging statements to utilize custom_info, custom_warning, and custom_error methods, ensuring all key operations and error messages include relevant metadata.
- Improved visibility of connection status, disconnection attempts, and error handling, facilitating better monitoring and debugging.
Refactor logging in OpcRepository to include full metadata in error and info messages
- Updated logging statements in the OpcRepository class to pass the complete metadata object instead of just the schedule name, enhancing the context available for debugging and monitoring.
