SIENTIAPDE-1646 Update dependencies and refactor OPC integration
- Replaced `opcua` with `asyncua` in `requirements-local.txt` and `requirements.txt` to utilize the async capabilities.
- Updated `values.yaml` to change the GitHub branch from `feature/SIENTIAPDE-1646` to `release/SIENTIAPDE-1646`.
- Refactored `opc.py` and `opc_repository.py` to accommodate the new `asyncua` library, ensuring compatibility with the synchronous API.
- Adjusted tests in `test_opc_repository.py` to reflect changes in the client implementation and maintain functionality.
Update E2E test report and enhance drift analysis handling
- Updated the E2E test report metrics to reflect the latest test results, showing 47 collected tests with all passing.
- Removed outdated sections related to failed tests and their causes, streamlining the report.
- Implemented a regression fix in the drift analysis to handle empty merged frames, ensuring workflows skip export when no drift metrics are available.
- Enhanced the `insert_sample_data` and `insert_sample_prediction` functions to allow customizable timestamps for better test accuracy.
- Refactored E2E tests to improve clarity and maintainability, particularly in handling repeat scenarios with distinct timestamps.
Refactor ModelMetrics to utilize DriftAnalysis for drift detection
- Replaced ModelAnalysis with DriftAnalysis in the ModelMetrics class to enhance drift detection capabilities.
- Updated method signatures and documentation to reflect the changes in target_name and return values.
- Adjusted data handling to ensure compatibility with the new analysis methods and improved clarity in the drift metrics dataframe preparation.
Update README, requirements, and E2E tests for improved configuration and functionality
- Enhanced the README with updated model configuration examples, including the addition of an alias for production.
- Removed the `requirements-light.txt` file and updated `requirements-local.txt` and `requirements.txt` to replace `asyncua` with `opcua`.
- Refactored E2E test scenarios to utilize scenario input files for better maintainability and clarity.
- Improved test coverage for MinIO offload functionality and added new helper functions for loading scenario inputs.
- Updated `values.yaml` to reflect new global configurations and environment variables for the laborious worker.
Refactor MLFlow retraining logic to always use retrain method
- Removed the conditional logic for full retraining, ensuring the `retrain` method is always called.
- Updated the documentation in the `retrain_model` method to reflect the changes in retraining flow.
- Adjusted tests to verify that the `retrain` method is invoked correctly, while ensuring `train` is not called when the full retrain flag is set.
Update dependencies and refactor MLFlow activities
- Replaced direct GitHub dependencies in `requirements.txt` with specific versioned packages for `sientia_do` and `sientia_model`.
- Refactored imports in `activities.py` to streamline the code structure.
- Enhanced the `MLFlow` class in `mlflow.py` by introducing a method to resolve model aliases, improving flexibility in model lookups.
- Simplified shutdown logic in `worker.py` for better readability.
- Added new tests for MLFlow activities and improved existing test coverage for data handling and model retraining processes.
Enhance environment configuration and update dependencies
- Added new environment variables for PluginStore and MLflow configuration in `.env.example`, including `RUNTIME`, `STORE_BASE_URL`, `STORE_OWNER`, `STORE_REPO`, `STORE_BRANCH`, `STORE_USERNAME`, `STORE_PASSWORD`, `STORE_CACHE_TTL_SECONDS`, `PYPI_SERVER`, `PYPI_USERNAME`, and `PYPI_PASSWORD`.
- Updated `git-requirements-mapping.txt` to reflect changes in repository names.
- Modified `requirements-light.txt` and `requirements.txt` to upgrade `sientia-dataops-library` to version 1.12.0 and `sientia-mlops-library` to version 0.8.1.
- Updated `values.yaml` to include new environment variables for worker runtime and PluginStore configuration.
- Refactored E2E tests to utilize new MLflow repository stubs and PluginStore mocks for improved testing accuracy.
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 values.yaml to adjust workflow and activity poller configurations for improved performance, increasing minimum, initial, and maximum values. Additionally, add new minimal retrain parameters to enhance retraining capabilities.
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.
Update values.yaml to rename SIE_MINIO_OFFLOAD_THRESHOLD_MEGABYTES to SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES and adjust its value to 0.5 for improved configuration clarity.
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.
Refactor prediction process to use execute_activity_method for MLFlow model transformation and prediction requests, enhancing consistency in workflow execution.
Update values.yaml to enhance PredictionsBatch worker tuning parameters
- Renamed SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES to SIE_MINIO_OFFLOAD_THRESHOLD_MEGABYTES for consistency.
- Added multiple configuration options for PredictionsBatch to optimize workflow and activity concurrency, including max concurrent tasks, activities, local activities, cached workflows, and poller behaviors.
Update Gates and FormatAndExportPrediction classes to standardize timestamp usage
- Changed 'last_timestamp' to 'timestamp' in the Gates class for consistency in input data handling.
- Updated the FormatAndExportPrediction class to reflect the same change in the output data structure.
Update Gates and FormatAndExportPrediction classes to use 'last_timestamp' for improved data handling
- Modified the Gates class to utilize 'last_timestamp' when only one row is present, ensuring accurate timestamp assignment.
- Updated the FormatAndExportPrediction class to replace 'timestamp' with 'last_timestamp' in the output data structure.
Enhance prediction export functionality by adding 'on_conflict' and 'unique_columns' parameters to the payload initialization in FormatAndExportPrediction class.
Update pyproject.toml to remove specific test file from exclusion list and bump sientia-dataops-library dependency version to 1.10.4 in requirements-light.txt
Enhance E2E testing with MinIO support and update documentation
- Updated `requirements-dev.txt` to include MinIO support in testcontainers.
- Added a new fixture for MinIO container setup in `conftest.py` to facilitate E2E tests involving S3-compatible storage.
- Introduced a new test fixture for activities using a real MinIO container in `conftest.py`.
- Updated E2E test scenarios and documentation to reflect the integration of MinIO for offload uploads and clarified error handling in workflows.
- Refactored existing tests to improve clarity and maintainability.
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.
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.
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 imports in gates.py and mlflow.py for improved organization
- Removed unnecessary import statement in mlflow.py and re-added it in a more appropriate location.
- Cleaned up the workflow metadata assignment in gates.py for better readability.
