The probes only check app_up, which was set to 1 when the metrics server started,
before install_runtime. A pod that would fail the guard could turn Ready first, so a
rollout would remove the old pod. app_up now goes to 1 right after the guard.
Also: copy sys.modules keys before iterating, raise the sientia_model floor to the
runtime's >=0.14.0, pin the same runtime versions in requirements-local.txt, test
that the snapshot is taken before the install and the real importlib.metadata path,
and document the boot failure in README and CLAUDE.md.
Replaces the re-exec. requirements.txt now pins the union of the basic and xgboost
runtime libraries, so install_runtime has nothing to change in the packages the worker
already imported. If a runtime pin changes and requirements.txt is not updated, the
worker logs which packages changed and exits before starting the workers.
test_main_entrypoint assigned worker.main = AsyncMock() without restoring it, so every
later test of main() ran the mock; it now uses patch.object.
From the code-review-python pass on #34:
- README, CLAUDE.md and the module docstring: the worker re-executes
itself after install_runtime, and SIENTIA_RUNTIME_READY is internal;
setting it in .env/the chart would skip the install silently.
- "Installing runtime" is now logged only on the pass that installs; the
re-executed pass logs that the install was skipped.
- The ponytail comment notes that the install success metrics are lost
to the exec.
- New test: a failed install propagates without setting the flag or
calling os.execv (locks the install -> flag -> exec order).
The worker imports sklearn (metrics, evidently) before
PluginStore.install_runtime replaces it on disk with the runtime's pinned
version, the same order that breaks every sklearn prediction in laborious.
After installing the runtime, the worker now replaces its process once
(SIENTIA_RUNTIME_READY=1 skips the second install), so every module is
loaded from the runtime's packages.
- Changed project name in values.yaml from "sientia-dataops-model-manager" to "sientia-model-manager".
- Added new environment variables for GitHub repository and branch configuration.
- Refactored cleanup paths to use a centralized REPORTS_TEMP_DIR constant for consistency.
- Updated runtime configurations and adjusted volume mounts for better resource management.
- Enabled SSH access for the model manager and disabled Grafana dashboard creation.
- Updated tests to reflect changes in directory paths and environment variable usage.
- Replaced synchronous MinIO repository calls with asynchronous counterparts in the Training class for improved performance.
- Enhanced logging throughout the training process to provide better insights into model metadata loading, parameter validation, and training execution.
- Updated the train_test_split function to enforce DataFrame input type, ensuring consistency in data handling.
- Removed the deprecated model_repository.py file to streamline the codebase.
- Adjusted cleanup schedule logic to improve error handling and logging during schedule reconciliation.
- Updated tests to reflect changes in the training workflow and repository interactions.
- Updated `.env.example` to include new environment variables for MinIO and PyPI configuration.
- Refactored `create_cleanup_schedule` to utilize runtime-specific task queues and improve schedule reconciliation logic.
- Enhanced `Activities` class to require a default bucket in MinIO configuration.
- Adjusted `requirements.txt` to specify version for `evidently`.
- Updated tests to reflect changes in schedule creation and configuration handling.
- Added new ignore rule for Ruff to allow temporary paths in tests.
- Introduced MyPy overrides for specific modules to ignore errors.
- Refactored `Cleanup` and `ExperimentTracking` classes to remove async keywords from methods, improving consistency in method signatures.
- Updated `Training` class methods to handle synchronous operations, enhancing performance and clarity.
- Adjusted `requirements.txt` to remove unnecessary Git dependency, streamlining project setup.
- Introduced a new activity to load model metadata from the model store.
- Refactored training logic to utilize new model metadata and improved parameter handling.
- Updated the `TrainModelParams` class to include additional fields for model configuration.
- Replaced deprecated utility functions with a custom train-test split implementation.
- Removed unused utility functions and cleaned up the data manager repository.
- Adjusted experiment tracking to include model-specific metadata in notifications.
- Added PluginStore integration for model management.
- Replaced StorageRepository with MinIORepository in Activities, Cleanup, and Training classes.
- Updated training logic to handle validation files and improved data management.
- Enhanced configuration for MinIO and PluginStore in connectors.
- Removed deprecated model repository and storage repository files.
- Updated environment variable handling for new configurations.
This commit removes redundant logging statements from training and experiment tracking activities, preventing duplicate log entries. It also introduces a logger helper to disable log propagation, further addressing the duplicate logs issue. Additionally, the Makefile, run_coverage.sh, setup_port_forwards.sh, and simulator/Dockerfile files were removed as they are no longer needed.
This commit introduces MinIO integration for object storage within the Model Manager system. It includes:
- Added MinIO activity class for file operations (fetch, delete).
- Updated Activities orchestrator to include MinIO activities.
- Added MinIO configuration builder to utils/connectors_config.py.
- Added environment variables for MinIO configuration in .env.example.
- Added boto3 and botocore dependencies to requirements.txt.
- Added unit tests for MinIO activities.
This commit includes several changes:
- Reorganized imports and class inheritance in activities.py, gates.py and mlflow.py for better readability and maintainability.
- Improved error handling and logging in gates.py and mlflow.py.
- Added input validation and filtering in gates.py to ensure data quality.
- Enhanced prediction formatting and storage policy management in gates.py.
- Updated metrics.py to use consistent naming conventions and labels.
- Refactored connectors_config.py to use type hints and improve code clarity.
- Updated conditional and MLFlow filters for better data quality checks.
- Improved model repository logic for retraining and updating models.
- Enhanced worker.py to include SDK metrics and improved error handling.
- Refactored workflows for better modularity and error handling.
- Updated tests to reflect the changes and improve test coverage.
This commit removes the OPC server integration from the Model Manager, including related activities, repositories, metrics, and configuration. It also adds code quality tools such as Ruff (linting/formatting), mypy (type checking), and Bandit (security analysis) along with a validation script and CI/CD integration for automated code validation. The README has been updated to reflect these changes.
This commit renames the 'laborious' package to 'model_manager' across the entire project. This includes renaming directories, modules, references in code, configuration files, and documentation to reflect the new package name. This change improves clarity and consistency within the project.
