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: Enhance system robustness and test coverage across bundle processing, activity error handling, and worker operations.
* Strengthen bundle reader validation with new tests for archive integrity, security vulnerabilities (e.g., path traversal, duplicate entries, special file types), and I/O error resilience.
* Improve error handling and status propagation in PI Web API, MLflow, and OPC activities, including partial write scenarios and OPC session management.
* Add comprehensive tests for worker startup, configuration, graceful shutdown, and Prometheus metrics server initialization.
* Introduce a new utility for intelligent DataFrame debugging, summarizing large dataframes for concise logging, and refine exception chain summarization.
* Update code coverage configuration to exclude @overload decorator lines.
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: Add skip_predict_timestamp flag to model_config to prevent timestamp reinsertion in prediction data, and document other ad-hoc behavioral flags.
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.
Disables the unraisableexception plugin in pytest due to a known bug in
pytest>=9.1 where it crashes with tracemalloc errors when multiple
unraisable exceptions occur close together.
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.
SIENTIAPDE-1646 Refactor MLFlow tests and update artifact handling
- Enhanced test cases for MLFlow to improve clarity and accuracy in data handling.
- Updated references in tests to use 'evaluation_data.csv' and 'test_data.csv' instead of 'retrain_input.csv' and 'train_data.csv'.
- Introduced a new helper function for creating prediction frames to streamline test setup.
SIENTIAPDE-1646 Update prediction data handling in MLFlow
- Renamed the prediction column in the MLFlow retraining process to improve clarity and consistency in the output data.
SIENTIAPDE-1646 Enhance MLFlow retraining process and evaluation data handling
- Updated the MLFlow class to merge prediction data with retrain data for improved evaluation.
- Renamed target column to "target" and added a timestamp column to the evaluation data.
- Logged both retrain input and evaluation data as artifacts in MLFlow.
Enhance MLFlow reference data handling and testing
- Updated the artifact handling in MLFlow to prioritize 'retrain_input.csv' over 'train_data.csv' when resolving reference data.
- Introduced new methods for resolving artifact names and locating downloaded CSV files.
- Modified the `get_reference_data` method to improve artifact resolution and error handling.
- Expanded unit tests to cover scenarios for missing artifacts and preference logic between retrain and train data CSVs.
Update values.yaml to add MLFLOW environment variables
- Added MLFLOW_HOST and MLFLOW_PORT environment variables to values.yaml for tracking configuration.
Update values.yaml and connectors_config.py for runtime and environment variable changes
- Renamed the runtime entry from "single" to "legacy" in values.yaml for clarity.
- Updated the helm upgrade command comment in values.yaml to reflect the correct path for deployment.
- Changed the environment variable name from "MLFLOW_HOST" and "MLFLOW_PORT" to "MLFLOW_URL" in connectors_config.py for consistency.
Update values.yaml and worker.py for runtime installation logic
- Updated helm upgrade command version in values.yaml from 0.6.1 to 0.6.5 for deployment reference.
- Modified worker.py to adjust runtime installation logic, ensuring 'legacy' runtime is set to 'single' for compatibility.
Update values.yaml to change runtime configurations and GitHub branch
- Renamed runtime entries from list format to key-value pairs for "basic" and "single".
- Updated the GITHUB_BRANCH environment variable from "feature/SIENTIAPDE-1646-legacy-laborious-worker" to "main".
- Added a helm upgrade command comment for deployment reference.
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.
