QTZPOC-20: Refactor model import workflow, introduce atomic model ID generation, and clarify MinIO expiry.
Fuses bundle download, decryption, MLflow experiment creation, and artifact upload into a single activity to prevent cross-worker issues. Implements an atomic counter for MongoDB model IDs, replacing the max(id) + 1 scan to avoid collisions during concurrent imports. Adds comprehensive documentation for MinIO object expiry mechanisms and refines lifecycle rule application. Improves error handling with clearer responsibility classification and centralizes MLflow repository injection.
QTZPOC-20: Implement explicit joblib model flavor and enhance model import workflow with extensive E2E tests.
This commit introduces a first-class joblib model flavor for direct loading and saving of Python pickle artifacts, including custom code within the artifact's code/ directory, bypassing MLflow's flavor loaders. The model import workflow's status database configuration is now streamlined to default to the POSTGRES_* server, with explicit IMPORT_STATUS_DB_* overrides.
Comprehensive end-to-end tests have been added for the model import workflow, covering success paths, gate rejections, digest enforcement, provisioning failures, status record integrity, cleanup, and retry/recovery scenarios using a real-clock environment. New E2
QTZPOC-20: Implement .sientia model bundle import workflow
Introduces a new Temporal workflow and activities to import .sientia model bundles. This enables:
- Secure decryption and validation of bundles through a seven-gate process.
- Provisioning imported models in MLflow and updating the MongoDB model listing.
- Comprehensive status tracking in the public.experiment_run table with new IMPORT_SUCCESS/IMPORT_ERROR states.
- Dedicated metrics, robust error handling, and configurable settings.
- Includes updated e2e database fixtures and extensive unit/integration tests for bundle format, reader, and overall workflow.
Implement MinIO Offload and Retention Features
- Added configuration options for MinIO retention hours and offload threshold in README.
- Introduced MinIO payload offloading for large DataFrame-derived payloads, storing them as parquet files.
- Updated activities to utilize MinIO for data loading and cleanup, including new methods for offloading and retention management.
- Refactored existing activities to integrate MinIO functionality, ensuring compatibility with previous workflows.
- Removed the legacy MinioRepository class, consolidating MinIO operations under a new manager structure.
- Updated requirements to use the latest version of the sientia-dataops-library.
Refactor Activities and API Integration for PI Web API
- Reintroduced the API import in the Activities class for proper integration.
- Cleaned up whitespace and formatting in the API class and related tests for improved readability.
- Updated test cases to ensure consistent formatting in error messages and configuration structures for PI Web API.
- Enhanced connectors_config.py with additional whitespace for better organization.
Enhance Activities and Prediction Workflows with PI Web API Integration
- Updated the Activities class to include API integration, allowing for configuration of PI Web API parameters.
- Modified prediction workflows to support output configuration for PI Web API, enabling data writing to the API.
- Refactored connectors_config.py by removing unused PostgreSQL and MongoDB configuration functions.
- Added tests to validate the new PI Web API functionality in activities and workflows, ensuring robust integration and functionality.
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.
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.
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.
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.
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.
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.
Update dependencies and enhance ML model retraining functionality
- Updated sientia-dataops-library version in requirements.txt from 1.3.3 to 1.3.4.
- Incremented image tag in values.yaml from 0.2.4 to 0.2.5 and added a new environment variable MONGODB_TTL_INDEX_HOURS.
- Introduced new methods in MLFlowRepository for model retraining and production model updates, including error handling and logging.
- Added retrain_model and update_production_model activities in mlflow.py to support model management workflows.
- Modified MongoDB connection settings in connectors_config.py for improved security and configuration flexibility.
Update notification handler references to use CoreNotificationHandler and add MongoDB configuration to connectors. Update GITHUB_BRANCH in values.yaml for optimization tasks.
SIENTIAPDE-1110 Refactor OPC activity to use server IDs instead of names, update values.yaml for OPC_ID, and enhance OpcRepository initialization for improved clarity and consistency.
Refactor and enhance the laborious workflow and utilities
- Removed outdated test file `test_predictions_batch.py` from workflows.
- Added `input_sample.json` for standardized input configuration.
- Introduced `connectors_config.py` to manage database and service configurations.
- Implemented a logging utility in `logger.py` for consistent logging across the application.
- Created `policies.py` to define retry policies for workflows.
- Developed comprehensive tests for `MLFlowRepository` in `test_model_repository.py`.
- Added extensive tests for `OpcRepository` in `test_opc_repository.py`.
- Updated `test_predictions_batch.py` to reflect new workflow structure and testing methodology.
