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.
Update .gitignore, requirements, and enhance OPC UA error handling
- Added new entries to .gitignore for openspec and cursor directories.
- Updated sientia-dataops-library dependency version in requirements-light.txt from 1.10.4 to 1.12.0.
- Enhanced OPC UA communication by refining reconnect logic and error handling in opc_repository.py, including the introduction of a reconnect flag and improved session management.
- Updated tests to cover new reconnect scenarios and ensure robust error handling for protocol states.
SIENTIAPDE-1811 Implement OPC write error handling and refactor tag writing logic
- Introduced a new function `_apply_opc_write_error` to manage session and reconnect flags based on OPC write error responses.
- Refactored the `_write_tags_from_config` method to streamline the writing of OPC tags for both prediction and confidence data.
- Enhanced unit tests to cover various scenarios for OPC write errors, including session bad and reconnect in progress cases.
- Updated existing tests to validate the new logic and ensure robust error handling.
Update values.yaml to rename worker references and adjust GitHub branch for SIENTIAPDE-1811. Changed nameOverride, fullnameOverride, and service account name to "sientia-laborious-legacy-worker" and updated the GITHUB_BRANCH value to "fix/SIENTIAPDE-1811".
Enhance OPC UA testing framework and documentation
- Added a new marker in pyproject.toml for tests using the in-process OPC UA server.
- Updated opc-communication.md to clarify E2E test scenarios involving the real OPC server and mock server.
- Introduced an in-process asyncua OPC UA server fixture in conftest.py for E2E tests.
- Created a new fixture for activities using the real OpcRepository connected to the in-process server.
- Updated scenarios.md to include instructions for running OPC real-server tests.
Enhance OPC UA communication and metrics tracking
- Updated README.md to include new OPC UA Communication section and detailed metrics for session and write diagnostics.
- Added new metrics in laborious/metrics.py for tracking OPC UA session states and write attempts.
- Refactored OPC activity in laborious/activities/opc.py to handle session errors and improve error reporting.
- Updated e2e tests to cover new scenarios for OPC session/channel errors and reconnect handling.
- Modified .gitignore to include relatorio files and mlruns directory.
- Added ipykernel to requirements-dev.txt for Jupyter notebook support.
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.
