Enhance tests.ipynb and gates.py for improved logging and output clarity
- Set execution count to null in tests.ipynb for reproducibility.
- Added stdout output to tests.ipynb for better visibility of results.
- Updated DataFrame display logic in tests.ipynb to include additional print statements for length and size.
- Improved debug logging in gates.py to log prediction data as a string for better traceability.
- Changed info log in gates.py to use len() instead of size for consistency.
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.
Implement prediction store policy handling in Gates activity
- Added a new method `get_prediction_store_policy` to validate and parse the prediction store policy.
- Updated `format_prediction` method to utilize the new policy handling, allowing for sorting of predictions based on the specified policy.
- Enhanced test coverage for the new policy handling, including various scenarios for valid and invalid policies.
- Removed the obsolete `coverage.sh` script.
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.
Update values.yaml and enhance logging in OPC activity
- Change replicaCount from 5 to 1 for reduced resource allocation.
- Update image tag in values.yaml to 0.4.5 for the latest version.
- Modify GITHUB_BRANCH environment variable for improved async handling.
- Add logging statements in OPC class to track server initialization and connection status.
- 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.
Update requirements and modify MLFlow activity
- Bump sientia-mlops-library version in requirements.txt from 0.38.5 to 0.38.8.
- Comment out the reset_index call in the MLFlow activity to prevent unintended data manipulation.
Update dependencies and enhance datetime handling
- Bump sientia-dataops-library version in requirements.txt to 1.4.4.
- Update image tag in values.yaml to 0.4.4.
- Add 'datetime_columns' to metadata in MinimalRetrain and PredictionsBatch workflows for improved data handling.
Update Gates activity and format_and_export_prediction workflow to use DATETIME_FORMAT_WITH_TZ for consistent timestamp handling
- Modified Gates activity to correctly handle the maximum timestamp without formatting it prematurely.
- Updated format_and_export_prediction workflow to utilize DATETIME_FORMAT_WITH_TZ for timestamp conversion.
- Enhanced tests to ensure timestamp conversion is applied consistently across workflows.
Update requirements and refactor Gates activity for improved datetime handling
- Update sientia-dataops-library reference in requirements.txt to version 1.4.3.
- Refactor datetime handling in Gates activity to use a consistent format with timezone support, replacing direct datetime calls with a centralized now() function and DATETIME_FORMAT_WITH_TZ constant.
Update requirements and values for SIENTIAPDE-1193
- Bump sientia-dataops-library version from 1.4.1 to 1.4.2 in requirements.txt.
- Change GITHUB_BRANCH in values.yaml to reflect the new task SIENTIAPDE-1193 regarding datetime writing in temporal.
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 worker module to include metadata in custom info and error messages
- Updated logging statements in the worker module to utilize custom_info and custom_error methods, incorporating a metadata dictionary for enhanced context.
- Improved visibility of key operations such as starting the worker, notification handler, and activities, as well as error handling for unhandled exceptions.
Refactor logging in Gates and MLFlow activities to use info level for key operations
- Updated logging statements in the Gates class to replace debug logs with info logs for input and output gate operations, enhancing visibility.
- Modified MLFlow class to use info logs for data transformation and prediction processes, improving clarity in the logging output.
- Adjusted OPC class to return the count of successfully written tags, providing better insight into data writing operations.
Enhance logging in MLFlow activity to include formatted JSON response data
- Added JSON import for better formatting of response data in debug logs.
- Updated logging statements to output transformed and prediction response data as pretty-printed JSON, improving readability and debugging context.
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.
Update dependencies and refactor logging imports for observability
- Updated the sientia-dataops-library dependency version to 1.4.0 in requirements.txt.
- Changed image tag in values.yaml from 0.3.2 to 0.4.1.
- Refactored logging imports across multiple files to use the new observability module instead of the temporal.utils.logger.
- Updated retry policy imports in workflow files to reflect the new module structure.
Implement server validation and output tag management in OPC class
- Added a new method to validate the existence of OPC servers before writing data, improving error handling.
- Introduced a method to manage writing of prediction and confidence tags, streamlining the data writing process.
- Refactored the write_opc_data method to utilize the new validation and management methods for better code organization and clarity.
Refactor OPC data writing and repository initialization
- Simplified success tracking logic in the OPC class for writing prediction and confidence data.
- Removed unused pod_id attribute from OpcRepository initialization.
- Updated test cases to include pod_id for improved metrics tracking during data writing operations.
Refactor OPC data writing to improve success tracking
- Updated the OPC class to store the success status of data writing operations for both prediction and confidence tags.
- Added logging for successful and failed writes to the OPC server, enhancing traceability of data operations.
Refactor OPC metrics to use unpacking for CORE_LABELS in metrics.py
- Updated the definition of prediction OPC writing metrics to utilize unpacking for CORE_LABELS, enhancing code clarity and maintainability.
Refactor opc_repository.py to import metrics module
- Added import statement for the metrics module to enhance functionality in the OPC repository.
- This change supports the integration of metrics tracking for OPC operations.
Implement OPC writing metrics and enhance OPC class initialization
- Added metrics for counting predictions written to the OPC server and monitoring their response times.
- Enhanced the OPC class initialization to include the pod ID for better tracking.
- Updated the write method to increment the prediction count and observe response times.
Update helm chart version in values.yaml and modify logging in worker.py
- Updated helm upgrade command in values.yaml to version 0.5.0-uat.
- Changed log message in worker.py to indicate the start of the SDK Metrics Server, while retaining the original log for the Temporal Client.
SIENTIAPDE-1174 Add SDK metrics configuration and update worker for telemetry
- Introduced sdk-metrics service in values.yaml with ClusterIP configuration.
- Updated worker.py to integrate SDK metrics telemetry using the new HTTP_SDK_METRICS_PORT environment variable.
- Enhanced Prometheus configuration to bind SDK metrics to the specified port.
Enhance BaseActivity initialization across multiple activities to include error counter
- Updated the initialization of the BaseActivity in Gates, MLFlow, and OPC classes to set the error counter to True, improving error tracking and handling capabilities.
Update dependencies and configuration for improved functionality
- Updated sientia-dataops-library version from 1.3.7 to 1.3.8 in requirements.txt.
- Changed GITHUB_BRANCH in values.yaml to reflect new testing focus: SIENTIAPDE-1169.
- Modified notification level in gates.py from WARNING to ERROR for better error handling.
pdate metrics tracking to include response time histogram
- Changed PREDICTION_RESPONSE_TIME_MONITOR from Gauge to Histogram for better response time analysis.
- Updated response time observation method in gates.py to utilize the new Histogram functionality.
SIENTIAPDE-1174 Update replica count and enhance logging in MLFlow
- Changed replicaCount in values.yaml from 3 to 1 for reduced resource usage.
- Added debug logging for prediction response data in MLFlow to improve traceability.
SIENTIAPDE-1174 Add write_metrics activity to main workflow for enhanced metrics tracking
- Included the write_metrics activity in the main workflow to support Prometheus metrics tracking.
Update dependencies, modify replica count, and implement metrics tracking
- Updated sientia-dataops-library version from 1.3.5 to 1.3.7 in requirements.txt.
- Changed replicaCount in values.yaml from 5 to 3 and incremented image tag from 0.2.7 to 0.3.1.
- Added Prometheus metrics tracking in gates.py and worker.py, including a new write_metrics method.
- Configured Prometheus service and ServiceMonitor in values.yaml for metrics collection.
Update GITHUB_BRANCH in values.yaml and enhance error notification handling in MLFlow
- Changed GITHUB_BRANCH in values.yaml to reflect the new pipeline for alerts orchestration.
- Added NotificationLevel.ERROR to error notifications in MLFlow for retraining and production model updates.
- Updated tests to verify error notification levels for model management workflows.
Refactor data merging in MLFlowRepository to simplify merge operation in fit method. Removed unnecessary parameters for improved readability and maintainability.
Enhance data merging in MLFlowRepository by specifying the 'on' parameter for improved join accuracy. This change ensures that the merge operation correctly aligns data based on the 'id' field.
