Vitor trimmed release/SIENTIAPDE-1646's requirements.txt to drop
explicit pins for packages already pulled transitively by sientia_do/
sientia_model (psycopg2-binary, sqlalchemy, redis, botocore, s3fs,
pyarrow, kaleido, hyperopt, shap, pycurl) — confirmed all of these
install anyway via sientia_do/sientia_model's own deps in the cluster
pod logs. Fewer redundant top-level pins means fewer chances for a
version mismatch like the scikit-learn one from earlier today.
Also bumps sientia_model floor to >=0.14.0 to match, and mirrors the
same trim in requirements-local.txt for local-dev parity — but keeps
this branch's sientia-model-library@0.13.1 git pin there (release's
own requirements-local.txt is stale at @0.10.0, predating the
RegressionMetrics class this card depends on).
The sientia-dev test cluster's "basic" plugin runtime declares
scikit-learn==1.8.0, installed on top of this repo's own
requirements.txt at container startup. The old scikit-learn==1.5.2
pin here got silently overwritten by that later install, but the
son-approx model's pickled LinearRegression wrapper was recreated
against a version needing sklearn 1.8's API — mismatched envs caused
`ImportError: cannot import name 'validate_data' from
sklearn.utils.validation` on model load (found while testing
SIENTIAPDE-1986 against the real cluster). Matching the pin here to
1.8.0 removes the redundant conflicting install.
RegressionMetrics class (needed for SIENTIAPDE-1986) was introduced in
sientia-model-library 0.12.0. The previous local pin (@0.10.0) predates
the class.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.
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.
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.
