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: Prioritize code/ directory download and sys.path injection before model loading, enabling MLflow flavors to stream models directly from URIs and simplifying joblib artifact handling.
QTZPOC-20: Unify artifact code directory handling for all model flavors and standardize joblib pickle path
Implemented a robust mechanism to ensure an artifact's code/ directory is prepended to sys.path before loading for sklearn, pyfunc, and joblib flavors. sklearn and pyfunc now resolve MLflow URIs to local directories first to facilitate this sys.path modification. Standardized the joblib flavor to explicitly read and write model.pkl within the artifact directory. Updated documentation and added comprehensive tests to reflect these changes.
QTZPOC-20: Improve structured logging and reporting for all model import pipeline failures by introducing a dedicated activity and centralizing error classification. This ensures consistent observability, with all workflow failures now reported before cleanup. Also, adjusted import step timeouts and refined manual run script cleanup logic.
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.
QTZPOC-13: Centralize MLflow joblib fallback logic, ensuring consistent model version resolution by reusing the initial run ID, improving logging, and updating dependencies.
QTZPOC-13: Introduce joblib fallback for MLflow model loading
Enhances model loading resilience by attempting to deserialize models directly with joblib if MLflow's native flavor loaders fail. This mechanism handles cases where .pkl files are present in the artifact.
Adds joblib as a core dependency and includes new end-to-end and unit tests to validate fallback behavior, covering success, no-fallback, and double-failure scenarios. Also updates .gitignore for .claude/ files and incorporates minor refactorings and linting adjustments across E2E tests and utility functions.
Enhance MLFlow run ID resolution with error handling for missing and invalid source URIs
- Added checks in `get_model_run_id` method to raise exceptions for models with missing or invalid source URIs.
- Introduced new test cases to validate error handling for these scenarios.
- Updated `requirements-light.txt` to include `mlflow` as a dependency.
Update .gitignore, requirements, and enhance OPC UA error handling
- Added new entries to .gitignore for openspec and cursor directories.
- Updated sientia-dataops-library dependency version in requirements-light.txt from 1.10.4 to 1.12.0.
- Enhanced OPC UA communication by refining reconnect logic and error handling in opc_repository.py, including the introduction of a reconnect flag and improved session management.
- Updated tests to cover new reconnect scenarios and ensure robust error handling for protocol states.
Enhance OPC UA communication and metrics tracking
- Updated README.md to include new OPC UA Communication section and detailed metrics for session and write diagnostics.
- Added new metrics in laborious/metrics.py for tracking OPC UA session states and write attempts.
- Refactored OPC activity in laborious/activities/opc.py to handle session errors and improve error reporting.
- Updated e2e tests to cover new scenarios for OPC session/channel errors and reconnect handling.
- Modified .gitignore to include relatorio files and mlruns directory.
- Added ipykernel to requirements-dev.txt for Jupyter notebook support.
SIENTIAPDE-1712 Enhance logging across various classes by adding logger parameters and improving debug statements. This update includes adjustments in Gates, MLFlow, ModelMetrics, and MLFlowRepository classes for better traceability and observability during operations.
SIENTIAPDE-1712 Enhance logging in MLFlowRepository class by introducing a dedicated _debug_dataframe method for conditional logging of DataFrame content. This update improves observability during model transformation, prediction, and retraining processes while managing log output effectively.
SIENTIAPDE-1712 Refactor logging in MLFlowRepository class by removing the debug_dataframe method and replacing it with direct debug statements for improved clarity. This change enhances the logging of DataFrame content during model transformation, prediction, and retraining processes, ensuring better observability without excessive log output.
SIENTIAPDE-1712 Refactor logging in MinioDataFramePayload class to utilize custom_debug method for improved clarity and consistency. Enhanced DataFrame size logging by integrating a dedicated debug method, streamlining the logging process.
SIENTIAPDE-1712 Enhance logging in Gates, MLFlow, and Storage classes by integrating logger parameter for improved traceability. This update allows for better monitoring of operations and data handling across these components.
SIENTIAPDE-1712 Implement debug logging in MinioDataFramePayload class for enhanced traceability. Added a static method for conditional logging and integrated debug statements throughout methods to capture DataFrame size estimates, upload actions, and retrieval processes, improving overall observability.
SIENTIAPDE-1712 Refactor debug logging for DataFrames across multiple classes. Introduced a new method to log DataFrame content conditionally based on row count in Gates, MLFlow, ModelMetrics, and MLFlowRepository classes, improving debugging capabilities while managing log output effectively.
SIENTIAPDE-1712 Implement debug logging for DataFrames in MLFlow and MLFlowRepository classes. Added a method to log DataFrame content conditionally based on row count, enhancing debugging capabilities while preventing excessive log output.
Update dependencies and refactor input filter handling for consistency
- Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt.
- Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner.
- Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
Update MinioDataFramePayload to reflect changes from training to prediction datasets
- Renamed TRAINING_DATASETS_PREFIX to PREDICTION_DATASETS_PREFIX for clarity.
- Updated object key naming convention to use prediction datasets directory.
SIENTIAPDE-1712 Add last_timestamp parameter to MLFlow and Gates activities for enhanced tracking
- Introduced last_timestamp parameter in the MLFlow and Gates classes to improve tracking of data processing times.
- Updated MinioDataFramePayload to handle last_timestamp, ensuring it defaults to the maximum timestamp from the dataframe if not provided.
SIENTIAPDE-1712 Add timestamp column to processed data in MLFlow and improve dataframe validation in MinioDataFramePayload
- Added a 'timestamp' column to the processed data in the MLFlow class for better tracking of data entries.
- Updated the validation check in MinioDataFramePayload to handle None values for the dataframe more explicitly.
Refactor MinioDataFramePayload usage across activities
- Updated instances of MinioDataFramePayload initialization in Gates, MLFlow, and Storage classes to use the new from_dict method for better data reconstruction from dictionaries.
- Enhanced the PredictionProcess workflow to utilize the updated payload handling.
- Added passthrough fixtures in tests to accommodate the new from_dict method for consistent testing behavior.
Enhance README and Implement Drift Detection and Metrics Workflows
- Added new sections in README for Drift Workflow and Simple Metrics Workflow, detailing their execution flows and functionalities.
- Introduced `drift.py` for data drift detection, comparing current data against reference datasets.
- Added `simple_metrics.py` for calculating regression metrics (RMSE, MSE, MAE, R²).
- Updated `values.yaml` to include configuration for MinIO retention hours and offload threshold.
- Refactored `minio_dataframe_payload.py` to use the new offload threshold environment variable.
- Adjusted tests to reflect changes in environment variable handling for MinIO offload threshold.
Remove code validation script and refactor imports in activities and workflows
- Deleted the `validate.sh` script, which was responsible for running code quality checks.
- Cleaned up import statements in `activities.py`, `gates.py`, `mlflow.py`, and `storage.py` by removing unused imports and organizing them.
- Refactored initialization methods in `MinioManager` and `MLFlow` classes for improved readability.
- Updated various workflows to ensure compatibility with the new structure and removed unnecessary comments.
- Enhanced test cases to accommodate changes in the activities and workflows, ensuring proper mocking of dependencies.
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 validation script and improve logging in API and model repository
- Updated validation script to include 'e2e/' directory in code formatting and linting checks.
- Enhanced error logging in API class to improve readability of error messages.
- Refactored debug logging in model repository for better structured output.
- Cleaned up import statements in various files for improved organization.
Enhance MLFlow logging and add skip_transform option in MLFlowRepository
- Updated logging in mlflow.py to output processed input data as CSV.
- Introduced skip_transform parameter in MLFlowRepository to conditionally bypass data transformation.
- Improved logging in model_repository.py to display data in a more structured format (to_dict) for predictions and transformations.
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.
Update sientia-mlops-library dependency to version 0.40.6 and refactor get_prediction_data method calls for improved readability in model_repository.py and test_model_repository.py.
Enhance prediction data retrieval in MLFlowRepository by adding predict_flavor parameter
- Updated get_prediction_data method to accept a predict_flavor argument, allowing for different prediction model handling.
- Adjusted calls to get_prediction_data throughout the codebase to include the new parameter.
- Added new test cases to validate behavior for different predict_flavor values, ensuring robust functionality.
SIENTIAPDE-1273
Enhance security analysis and SQL injection handling
- Added skip for potential SQL injection false positives in Bandit configuration.
- Updated validate.sh to use the pyproject.toml configuration for Bandit security analysis.
- Refactored code to replace ensure_dataframe utility with direct DataFrame usage in multiple activities, improving clarity and reducing dependencies.
- Removed the deprecated dataframe_utils module to streamline the codebase.
Update dependencies and refactor data handling in various modules
- Updated sientia-dataops-library dependency version from 1.5.3 to 1.5.4 in requirements files.
- Updated sientia-mlops-library dependency version from 0.39.0 to 0.40.2 in requirements files.
- Refactored return types in Gates, MLFlow, and ModelMetrics classes to return dictionaries instead of DataFrames for improved compatibility with downstream systems.
- Removed the temporal_codec module as it is no longer needed for DataFrame serialization.
- Adjusted data handling in the Drift workflow to ensure proper data structure is maintained.
Refactor data handling in various modules to ensure DataFrame consistency
- Replaced direct DataFrame instantiation with `ensure_dataframe` utility in Gates, MLFlow, OPC, and ModelMetrics classes to standardize data handling.
- Updated return types in several asynchronous methods to return DataFrames instead of dictionaries for improved usability.
- Adjusted data export processes in workflows to convert DataFrames to dictionaries with `to_dict(orient='records')` for compatibility with downstream systems.
