QTZPOC-20: Refactor model repository tests for improved assertion logic and exception handling, including correcting pytest.raises exception access and refining mock verifications.
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 model import workflow for .sientia bundles
Introduces the import_model workflow and its associated activities to enable importing .sientia bundles into MLflow and MongoDB. This includes:
* New core workflow and activity logic for handling bundle download, decryption, validation, MLflow provisioning (experiments, runs, registered versions), and MongoDB model document creation.
* Comprehensive E2E and unit test suites, including a dedicated test harness and a manual run script for development.
* Updated .env.example and README.md with new configurations and usage instructions.
* Ensures correct MLflow model source resolution for legacy loader compatibility and robust MongoDB document management.
* Refactors existing utility scripts into a scripts/ directory.
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.
Renamed the OPC.close asynchronous method to OPC.aclose to align with common Python conventions for asynchronous context managers and methods, improving clarity. All call sites and tests have been updated accordingly.
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.
SIENTIAPDE-1811 Implement OPC write error handling and refactor tag writing logic
- Introduced a new function `_apply_opc_write_error` to manage session and reconnect flags based on OPC write error responses.
- Refactored the `_write_tags_from_config` method to streamline the writing of OPC tags for both prediction and confidence data.
- Enhanced unit tests to cover various scenarios for OPC write errors, including session bad and reconnect in progress cases.
- Updated existing tests to validate the new logic and ensure robust error handling.
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.
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.
Refactor metrics and API handling for improved consistency and clarity
- Removed the SIENTIA_CORE_LABELS constant and replaced it with CORE_LABELS for uniformity across metrics.
- Updated the API class to ensure operation_type is always included in core labels for PI Web API metrics.
- Simplified metric tag handling in the Gates class by consolidating common tags into a single core_tags dictionary.
- Enhanced the PredictionProcess class to improve error handling and variable naming for clarity.
Update environment variables in values.yaml and enhance metric labels in metrics.py
- Changed POSTGRES_USER and POSTGRES_PASSWORD values in values.yaml for improved security.
- Added 'runtime' label to metrics in metrics.py for better environment identification.
- Updated CORE_LABELS to include 'runtime' for consistency across metrics.
- Modified type hint for data parameter in PredictionProcess to use a dictionary for better clarity.
- Adjusted tests to reflect changes in core labels and MinIO configuration.
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.
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 PI Web API response handling in tests
- Updated test cases in test_api.py to handle response data as lists instead of dictionaries for consistency with the API's expected output format.
- Adjusted mock responses to reflect the new structure, ensuring tests accurately simulate API behavior.
- Enhanced clarity in test descriptions and improved overall test coverage for response processing scenarios.
Update sonar-project.properties to exclude all worker files from coverage and modify execution counts and timestamps in tests.ipynb. Add a new test for empty DataFrame handling in test_model_repository.py.
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 end-to-end tests for PredictionsBatch workflow scenarios
- Introduced mock repositories for PI Web API and OPC operations to improve test coverage.
- Updated test scenarios to handle partial write errors for PI Web API and OPC.
- Refactored existing tests to assert correct behavior under various error conditions.
- Enhanced logging and error handling in API and OPC activities to provide clearer feedback on failures.
- Removed outdated integration test file to streamline test suite.
SIENTIAPDE-1478
Enhance end-to-end tests for PredictionsBatch workflow
- Added new test scenarios for input and transform gates handling CONTINUE, STOP, and REPEAT policies.
- Implemented sample data insertion functions for testing various prediction outcomes.
- Updated existing tests to verify behavior under different input conditions and response validations.
- Refactored test structure for clarity and maintainability.
Update coverage source in pyproject.toml, add testcontainers for PostgreSQL in requirements-dev.txt, increment image tag and adjust probe delays in values.yaml, and refine condition checks in format_and_export_prediction.py and mlflow.py. Additionally, enhance test coverage in test_gates.py.
SIENTIAPDE-1478
Implement PI Web API response processing and metrics tracking
- Added a new method in the API class to process responses from the PI Web API, validating tag writes and emitting metrics for success and errors.
- Enhanced error handling for missing WebIds and tag names in responses, with appropriate logging and notifications.
- Updated tests to cover various scenarios for processing PI Web API responses, ensuring robust functionality and metrics emission.
- Refactored existing methods to integrate the new response processing logic, improving overall code clarity and maintainability.
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.
Update sientia-mlops-library dependency to version 0.40.5 and fix SQL interval formatting in Drift and SimpleMetrics workflows for improved query accuracy.
Enhance data handling and export processes in Laborious workflows
- Updated `gates.py` to improve data quality validation, filtering, and formatting operations, including enhanced metrics recording.
- Refined `mlflow.py` to better manage model transformations and reference data retrieval from MLflow Model Registry.
- Enhanced `format_and_export_prediction.py` to support separate export of transformed data, improving flexibility in data handling.
- Added comprehensive test coverage for new functionalities, including transformed data formatting and retrain report generation.
- Improved documentation in `README.md` to reflect changes in activities and workflows, ensuring clarity on data processing and export paths.
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 version and enhance metrics calculation in Laborious system
- Updated image tag in values.yaml from 1.1.0 to 1.1.1.
- Modified GITHUB_BRANCH environment variable for consistency.
- Added a new method `calculate_simple_metrics` in model_metrics.py to compute various model performance metrics including RMSE, MSE, MAE, and R2.
- Integrated the new metrics calculation into the worker setup, allowing for concurrent processing of simple metrics.
- Updated tests to cover the new metrics calculation functionality, ensuring comprehensive validation of the implementation.
Refactor Drift class to improve target name handling
- Extracted target name from model configuration in the Drift class for better clarity and maintainability.
- Updated test cases to reflect changes in input data structure, ensuring consistency in target name retrieval.
Enhance MLFlowRepository and Activities classes with new methods and metrics
- Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry.
- Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models.
- Updated Activities class to integrate ModelMetrics for improved metrics handling.
- Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities.
- Updated various workflows to include `transform_table_name` in input data for better data handling.
Update logging in MLFlowRepository and OpcRepository to use unified logging methods
- Refactored logging calls in MLFlowRepository to replace `self.logger.info` and `self.logger.debug` with `self.info` and `self.debug` for consistency.
- Updated connection logging in OpcRepository to format the connection message properly.
- Adjusted test cases to reflect changes in logging behavior and ensure proper assertions.
Enhance metrics handling in Gates and MLFlow classes
- Added checks for `None` response times before emitting OPC writing metrics in the Gates class to prevent unnecessary metric emissions.
- Updated the MLFlow class to conditionally sort and drop duplicates based on the presence of the 'created_at' column, ensuring robustness in data processing.
- Adjusted corresponding tests to validate the new behavior in both classes.
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.
