Remove deprecated files and configurations from transformer_pyfunc module
- Deleted conda.yaml, MLmodel, python_env.yaml, requirements.txt, and various utility scripts related to data processing and model handling.
- Removed binary files including python_model.pkl and training_transformer.pkl to clean up the artifacts directory.
- This cleanup is part of the effort to streamline the transformer_pyfunc module and eliminate unused components.
Add placeholder class 'Any' in test_model_repository.py and update invalid_cases to use it
- Introduced a new placeholder class 'Any' to be used in test cases.
- Updated the 'invalid_cases' list to replace integer keys with instances of the 'Any' class, enhancing test coverage for key types.
Refactor model configuration handling in MLFlow and workflows
- Replaced 'model_retention' with 'model_config' to encapsulate retention settings and improve consistency across various components.
- Updated test cases to reflect changes in argument structure, ensuring compatibility with the new model configuration format.
- Added 'prediction_store_policy' to input data handling in workflows for enhanced configuration management.
Enhance MLFlow data handling by adding timestamp column and improving debug logging
- Added a 'timestamp' column to the input data, converting the index to a datetime format for better tracking of predictions.
- Improved debug logging to provide clearer context by including the input data preview in the log output.
Update MLFlow class to pass logger instance directly to MLFlowRepository
- Modified the initialization of MLFlowRepository in the MLFlow class to pass the logger instance directly, improving logging capabilities and consistency across the application.
SIENTIAPDE-1222 Update MLFlow methods to include metadata parameter
- Modified the transform and predict method calls in the MLFlow class to include a new 'metadata' parameter, enhancing the functionality and data handling capabilities of the model monitoring repository.
Update test cases in model repository and prediction process
- Replaced string data with MagicMock in test_transform_success and test_transform_error to improve test isolation.
- Updated the predict method calls in test_predict_success and test_predict_error to reflect changes in argument structure.
- Added 'prediction_store_policy' to the test_run configuration in test_prediction_process for consistency with recent updates.
Refactor datetime index handling in MLFlow and MLFlowRepository
- Moved the detect_and_parse_datetime_index method from MLFlow to MLFlowRepository for better organization and reusability.
- Updated the method to include enhanced logging and error handling for invalid datetime formats.
- Adjusted the transform method in MLFlowRepository to utilize the new datetime index parsing logic.
- Added unit tests for both valid and invalid datetime index cases to ensure robustness.
Update README.md to reflect new features and configuration changes
- Added details about two dedicated task queues: `predictions_batch-queue` and `minimal_retrain-queue`.
- Enhanced descriptions of activities and workflows, including multiple inheritance patterns and configurable MLFlow model serving.
- Updated monitoring metrics section to include new labels and metrics for prediction and OPC export operations.
- Revised configuration section with updated default values and added new environment variables for Kubernetes pod identification.
- Improved clarity in the Predictions Batch Workflow configuration example, including structured input filters and updated retention policies.
Update model configuration key for compression in MLFlowRepository
- Changed the key for compression from 'compressed' to 'is_compressed' in model configuration handling to ensure consistency with updated standards.
Update model configuration keys in MLFlowRepository for consistency
- Changed 'model_retention' to 'retention_minutes' and 'is_compressed' to 'compressed' in model configuration handling, ensuring alignment with updated configuration standards.
Enhance debug logging in MLFlowRepository to improve data traceability
- Added a debug logging statement to capture received data for model prediction, improving visibility into input data.
- Changed an existing info logging statement to debug level for consistency, ensuring all relevant data is logged at the appropriate level.
Update prediction_store_policy handling in workflows
- Added 'prediction_store_policy' to the input data handling in PredictionsBatch, ensuring a default value of 'lts:1' is used when not provided.
- Modified FormatAndExportPrediction to directly use 'prediction_store_policy' from input_data, removing the default fallback.
- Updated PredictionProcess to include 'prediction_store_policy' in the output data structure, ensuring consistency across workflows.
Enhance MLFlow debug logging to limit output size
- Updated debug logging statements in the MLFlow activity to include a maximum of 5 items and a depth of 5 for the sample dictionary, improving readability and reducing log clutter for raw and transformed response data.
Refactor debug logging in Gates activity for improved readability
- Reformatted the debug logging statement for input data in the Gates activity to enhance clarity and maintain consistency with previous logging improvements.
Refactor MLFlow debug logging for improved readability
- Reformatted debug logging statements in the MLFlow activity to enhance clarity and consistency.
- Ensured that the output of raw and transformed response data is presented in a more readable format, maintaining the use of create_sample_dict for better visualization.
Update image tag in values.yaml and enhance debug logging in Gates and MLFlow activities
- Updated the image tag in values.yaml from '0.0.1' to '0.0.2'.
- Improved debug logging in the Gates activity to format input data and filters for better readability.
- Enhanced MLFlow activity logging to include formatted output for raw and transformed response data, ensuring consistent logging format.
Update requirements and enhance logging in Gates and MLFlow activities
- Updated the sientia-dataops-library and sientia-mlops-library dependencies in requirements.txt to the latest versions.
- Improved debug logging in the Gates activity to display a sample of input data and filters, enhancing clarity and reducing output size.
- Refactored MLFlow activity logging to utilize the create_sample_dict function for better visualization of nested data structures in logs.
SIENTIAPDE-1222
Enhance MLFlow logging with sample dictionary for response data
- Introduced a new method to create a sample dictionary for debugging, allowing for better visualization of nested data structures in logs.
- Updated debug logging to utilize the new sampling method for raw and transformed response data, improving clarity and reducing output size.
- Adjusted logging for processed input data to display only the first few rows, enhancing readability.
Refactor MLFlow logging to enhance data output clarity
- Updated debug logging to use data.to_string() for processed input data, improving readability.
- Modified raw response data logging to format the output as a string, ensuring consistent logging format.
Update values.yaml and MLFlow logging for courier integration
- Changed the image repository to 'sientia-module-courier' and updated the image tag to '0.0.1'.
- Modified environment variables for GITHUB_BRANCH and MLFLOW_PASSWORD to reflect new configurations.
- Enhanced MLFlow logging to include additional debug statements for raw response data and added a check for empty DataFrames.
Refactor MLFlow logging to improve data output clarity
- Updated the debug logging to directly capture the output of data.to_csv, enhancing traceability of processed input data.
- Removed redundant debug statements for raw response data to streamline logging and focus on essential information.
SIENTIAPDE-1214: Enhance MLFlow and tests with datetime index handling and logging improvements
- Added a new method in MLFlow to detect and parse datetime indices in DataFrames, ensuring proper format and raising errors for invalid types.
- Updated prediction workflows to utilize the new datetime index handling, improving data integrity during transformations.
- Enhanced logging in model_repository to include detailed data outputs for better traceability.
- Adjusted timeout settings in prediction workflows for improved execution time management.
- Updated tests.ipynb to include additional checks for index types and outputs for better validation of functionality.
SIENTIAPDE-1214: Update requirements.txt to clarify dependencies and improve project setup
- Commented out the previous sientia-mlops-library dependency for better clarity.
- Ensured that the requirements.txt reflects the current state of dependencies for easier management.
SIENTIAPDE-1214: Refactor MLFlow and model repository methods to use model_config dictionary
- Updated MLFlow class methods to accept model_config instead of model_retention for improved flexibility.
- Modified model_repository methods to handle model_config, extracting necessary parameters for transformation and prediction.
- Adjusted predictions_batch and prediction_process workflows to utilize model_config for better configuration management.
- Commented out the previous sientia-mlops-library dependency in requirements.txt for clarity.
Update tests.ipynb and gates.py for enhanced functionality and clarity
- Adjusted execution count in tests.ipynb for reproducibility.
- Modified DataFrame creation in tests.ipynb to dynamically set size and update timestamp frequency.
- Added a new filter function for empty data in gates.py to improve data handling.
Refactor test data structure in gates.py and model_repository.py for improved clarity and consistency
- Updated test cases in test_gates.py to use dictionaries for prediction and response_time, associating values with timestamps.
- Modified test_predict_success in test_model_repository.py to create a DataFrame with named indices for better readability in assertions.
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.
Update quality-gate.yml to configure Git for OAuth2 authentication
- Added global Git configuration to replace SSH and HTTPS URLs with OAuth2 token-based authentication for GitHub access.
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.
Update README.md to enhance architecture documentation
- Removed detailed system overview diagram to streamline content.
- Added architecture diagrams for key workflows: PredictionsBatch, PredictionProcess, FormatAndExportPrediction, and MinimalRetrain.
- Improved clarity and structure of the architecture principles section.
Update README.md to reflect changes in prediction workflow configuration
- Revised input parameters for the prediction process, including updates to schedule name, model ID, and workflow name.
- Enhanced structure of input filters and MLflow filter policies for improved clarity and functionality.
- Introduced new fields for model retention and output configuration, while maintaining backward compatibility with existing parameters.
