Simplify bandit nosec comments and mock metrics_controller with real spe
- drift.py/simple_metrics.py: move nosec rationale to a code comment above
the query instead of an inline suppression comment, keeping bandit happy
with a plain `# nosec`
- tests: replace bare `MagicMock()` with `MagicMock(spec=MetricsController)`
for metrics_controller across activities/repository tests so mocks fail
fast on attribute typos instead of silently accepting any call
SIENTIAPDE-1646 Merge origin/main into release/SIENTIAPDE-1646
Bring main up to date while keeping this branch's sync execution, PluginStore
runtime and sientia_model (SientiaMLflowRepository/SientiaModel) structure.
Resolution decisions:
- OPC: keep the sync asyncua.sync implementation; port main's server_uri
validation, e2e race fix (lock held until released) and the new unit tests
converted to sync.
- Model import workflow (.sientia bundles), joblib/transform flavors and the
local MLFlowRepository (model_repository.py) are dropped, with their tests,
docs, metrics, env vars and connectors config.
- Worker: drift/simple_metrics stay pinned to the 'core' runtime; port main's
HOSTNAME pod id, logger-based prometheus startup and sys.exit after finally.
- values.yaml removed, following main (SIENTIAPDE-1945).
- Deps: keep sientia_model; take main's fsspec/s3fs/botocore/boto3 pins and
sientia_do>=1.12.2; drop import-only deps (pynacl, cryptography, pymongo,
python-dotenv, pycaret, joblib, gcsfs).
- e2e: keep sientia_data schema and scenario input files.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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: 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.
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 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.
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 requirements and enhance OPC communication handling
- Updated `requirements-local.txt` to use the latest versions of `sientia-dataops-library` (1.12.1) and `sientia-model-library` (0.10.0).
- Improved documentation in `opc-communication.md` to clarify the transition from async to sync implementation and added details on error classification and reconnection behavior.
- Refactored `init_opc` method in `opc.py` to enhance connection handling and logging, ensuring independent server initialization.
- Enhanced validation and writing methods in `opc.py` to provide better feedback and error handling for OPC server operations.
- Updated `opc_repository.py` to improve error payload construction and session management metrics.
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.
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.
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-1646 Enhance OPC integration and E2E testing framework
- Updated .gitignore to exclude '.cursor/*' for better file management.
- Added new marker in pyproject.toml for E2E tests using OPC.
- Introduced async OPC server fixtures and improved connection handling in conftest.py.
- Enhanced error handling in OPC activities and added metrics for session management in metrics.py.
- Updated E2E tests to cover new OPC scenarios, including session errors and reconnect handling.
- Refactored helper functions to improve prediction assertion logic in helpers.py.
- Documented new OPC scenarios in scenarios.md for clarity on expected outcomes.
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.
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.
Refactor OPC and OpcRepository classes to remove logger dependency and streamline logging calls
- Removed logger attribute from OPC and OpcRepository classes, replacing direct logger calls with class methods for logging.
- Updated write_data method signatures to eliminate logger parameter, simplifying the interface.
- Adjusted related tests to reflect changes in method signatures and logging behavior.
Implement new scheduling configurations for model retraining and drift analysis in input_sample.json
- Added three new schedule configurations: `minimal-retrain-test-runtime`, `drift-test-runtime`, and `simple-metrics-test-runtime`.
- Each configuration includes parameters such as model ID, workflow type, frequency, and specific queries for data retrieval.
- Enhanced the structure to support active status and updated timestamps for better tracking of schedule states.
SIENTIAPDE-1646 Add new scheduling configurations and remove outdated documentation
- Introduced new scheduling configurations for minimal retrain, drift analysis, and simple metrics in `input_sample.json`.
- Removed obsolete documentation files related to drift analysis and E2E test reports to streamline project resources.
- Updated E2E tests for minimal retrain to enhance reporting and error handling during model retraining processes.
