- The sientia-module entrypoint now merges requirements.txt with the
Plugin Store runtime and installs both before boot (SIENTIAPDE-2223),
so the worker no longer calls install_runtime or runs the loaded
package version guard.
- Leave runtime libs unpinned in requirements files so the runtime
version wins; cap sqlalchemy<2.1 and evidently<0.7 for compatibility.
- Update README, CLAUDE.md and worker tests to match.
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.
- 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.
- Updated `Activities` class to improve garbage collection handling.
- Enhanced error messaging in `ExperimentTracking` for better clarity on update failures.
- Refactored `Training` class to streamline exception handling and improve type hints.
- Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata.
- Added functionality to extract model equations in `DataManagerRepository` for linear regression models.
- 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 refactors the .dockerignore file to use a whitelist approach, significantly reducing the Docker image size by excluding unnecessary files. It also fixes the queue names in values.yaml and adds TEMPORAL_USE_TLS to the test environment.
