Harden retrain merge and OPC write against silent failures
- pandas merge now uses validate='one_to_one' so duplicated timestamps
from wrapper.retrain fail loudly instead of silently multiplying rows
- OPC write_data raises OpcClientNotInitializedError instead of a bare
assert when client is torn down between validation and write, so
callers get a reported error instead of an AssertionError
- add regression test covering the torn-down client path
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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: 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: 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 .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.
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.
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.
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.
SIENTIAPDE-1646 Update dependencies and refactor OPC integration
- Replaced `opcua` with `asyncua` in `requirements-local.txt` and `requirements.txt` to utilize the async capabilities.
- Updated `values.yaml` to change the GitHub branch from `feature/SIENTIAPDE-1646` to `release/SIENTIAPDE-1646`.
- Refactored `opc.py` and `opc_repository.py` to accommodate the new `asyncua` library, ensuring compatibility with the synchronous API.
- Adjusted tests in `test_opc_repository.py` to reflect changes in the client implementation and maintain functionality.
Refactor ModelMetrics to utilize DriftAnalysis for drift detection
- Replaced ModelAnalysis with DriftAnalysis in the ModelMetrics class to enhance drift detection capabilities.
- Updated method signatures and documentation to reflect the changes in target_name and return values.
- Adjusted data handling to ensure compatibility with the new analysis methods and improved clarity in the drift metrics dataframe preparation.
Update README, requirements, and E2E tests for improved configuration and functionality
- Enhanced the README with updated model configuration examples, including the addition of an alias for production.
- Removed the `requirements-light.txt` file and updated `requirements-local.txt` and `requirements.txt` to replace `asyncua` with `opcua`.
- Refactored E2E test scenarios to utilize scenario input files for better maintainability and clarity.
- Improved test coverage for MinIO offload functionality and added new helper functions for loading scenario inputs.
- Updated `values.yaml` to reflect new global configurations and environment variables for the laborious worker.
Update dependencies and refactor MLFlow activities
- Replaced direct GitHub dependencies in `requirements.txt` with specific versioned packages for `sientia_do` and `sientia_model`.
- Refactored imports in `activities.py` to streamline the code structure.
- Enhanced the `MLFlow` class in `mlflow.py` by introducing a method to resolve model aliases, improving flexibility in model lookups.
- Simplified shutdown logic in `worker.py` for better readability.
- Added new tests for MLFlow activities and improved existing test coverage for data handling and model retraining processes.
Enhance environment configuration and update dependencies
- Added new environment variables for PluginStore and MLflow configuration in `.env.example`, including `RUNTIME`, `STORE_BASE_URL`, `STORE_OWNER`, `STORE_REPO`, `STORE_BRANCH`, `STORE_USERNAME`, `STORE_PASSWORD`, `STORE_CACHE_TTL_SECONDS`, `PYPI_SERVER`, `PYPI_USERNAME`, and `PYPI_PASSWORD`.
- Updated `git-requirements-mapping.txt` to reflect changes in repository names.
- Modified `requirements-light.txt` and `requirements.txt` to upgrade `sientia-dataops-library` to version 1.12.0 and `sientia-mlops-library` to version 0.8.1.
- Updated `values.yaml` to include new environment variables for worker runtime and PluginStore configuration.
- Refactored E2E tests to utilize new MLflow repository stubs and PluginStore mocks for improved testing accuracy.
Update dependencies and refactor input filter handling for consistency
- Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt.
- Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner.
- Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
Update environment variables in values.yaml and enhance metric labels in metrics.py
- Changed POSTGRES_USER and POSTGRES_PASSWORD values in values.yaml for improved security.
- Added 'runtime' label to metrics in metrics.py for better environment identification.
- Updated CORE_LABELS to include 'runtime' for consistency across metrics.
- Modified type hint for data parameter in PredictionProcess to use a dictionary for better clarity.
- Adjusted tests to reflect changes in core labels and MinIO configuration.
Refactor MinioDataFramePayload usage across activities
- Updated instances of MinioDataFramePayload initialization in Gates, MLFlow, and Storage classes to use the new from_dict method for better data reconstruction from dictionaries.
- Enhanced the PredictionProcess workflow to utilize the updated payload handling.
- Added passthrough fixtures in tests to accommodate the new from_dict method for consistent testing behavior.
Remove code validation script and refactor imports in activities and workflows
- Deleted the `validate.sh` script, which was responsible for running code quality checks.
- Cleaned up import statements in `activities.py`, `gates.py`, `mlflow.py`, and `storage.py` by removing unused imports and organizing them.
- Refactored initialization methods in `MinioManager` and `MLFlow` classes for improved readability.
- Updated various workflows to ensure compatibility with the new structure and removed unnecessary comments.
- Enhanced test cases to accommodate changes in the activities and workflows, ensuring proper mocking of dependencies.
Implement MinIO Offload and Retention Features
- Added configuration options for MinIO retention hours and offload threshold in README.
- Introduced MinIO payload offloading for large DataFrame-derived payloads, storing them as parquet files.
- Updated activities to utilize MinIO for data loading and cleanup, including new methods for offloading and retention management.
- Refactored existing activities to integrate MinIO functionality, ensuring compatibility with previous workflows.
- Removed the legacy MinioRepository class, consolidating MinIO operations under a new manager structure.
- Updated requirements to use the latest version of the sientia-dataops-library.
Update sonar-project.properties to exclude all worker files from coverage and modify execution counts and timestamps in tests.ipynb. Add a new test for empty DataFrame handling in test_model_repository.py.
Refactor validation script and improve logging in API and model repository
- Updated validation script to include 'e2e/' directory in code formatting and linting checks.
- Enhanced error logging in API class to improve readability of error messages.
- Refactored debug logging in model repository for better structured output.
- Cleaned up import statements in various files for improved organization.
Enhance end-to-end tests for PredictionsBatch workflow scenarios
- Introduced mock repositories for PI Web API and OPC operations to improve test coverage.
- Updated test scenarios to handle partial write errors for PI Web API and OPC.
- Refactored existing tests to assert correct behavior under various error conditions.
- Enhanced logging and error handling in API and OPC activities to provide clearer feedback on failures.
- Removed outdated integration test file to streamline test suite.
Enhance Activities and Prediction Workflows with PI Web API Integration
- Updated the Activities class to include API integration, allowing for configuration of PI Web API parameters.
- Modified prediction workflows to support output configuration for PI Web API, enabling data writing to the API.
- Refactored connectors_config.py by removing unused PostgreSQL and MongoDB configuration functions.
- Added tests to validate the new PI Web API functionality in activities and workflows, ensuring robust integration and functionality.
Update sientia-mlops-library dependency to version 0.40.6 and refactor get_prediction_data method calls for improved readability in model_repository.py and test_model_repository.py.
Enhance prediction data retrieval in MLFlowRepository by adding predict_flavor parameter
- Updated get_prediction_data method to accept a predict_flavor argument, allowing for different prediction model handling.
- Adjusted calls to get_prediction_data throughout the codebase to include the new parameter.
- Added new test cases to validate behavior for different predict_flavor values, ensuring robust functionality.
