QTZPOC-20: Improve worker shutdown handling by allowing SystemExit and KeyboardInterrupt to propagate directly, preserving their original exit codes and ensuring cleanup.
QTZPOC-20: Enable code_paths for sklearn models; improve worker shutdown handling
The MLFlowRepository now resolves and forwards code_paths to mlflow.sklearn.log_model, ensuring custom code dependencies for sklearn models are preserved and correctly reloaded. A new integration test validates this end-to-end functionality. Additionally, worker exception handling is refined to distinguish between deliberate shutdowns (SystemExit, KeyboardInterrupt) and unhandled exceptions, preventing unnecessary error logging for expected exits.
QTZPOC-20: Refactor ModelImport status engine for lazy initialization, add byte-counted size validation to bundle extraction, optimize MLflow run ID lookups, and ensure workflow cancellation propagates correctly.
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.
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.
