- 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.
Removes auto-deploy-on-PR-merge trigger and the old PR-merge gate. Deploy now
triggers only via workflow_dispatch, with chart_ref required (no default) as
the literal sientia-helm-chart branch bumped and deployed — hard error if it
doesn't exist, no silent fallback to development/main.
- .github/workflows/deploy.yml: drop pull_request trigger, add chart_ref
input, branch_name now github.ref_name, drop pull-requests:read permission
- Delete .github/workflows/require-chart-ref-label.yml
- README.md: update Deploy section to describe manual trigger
[skip ci]
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).
Migrated to a shared sientia-module image and umbrella Helm chart, removing the standalone Dockerfile, .dockerignore, and values.yaml. Introduced CLAUDE.md for AI-assisted development. Updated quality-gate.yml with push triggers, concurrency, and refined permissions. Bumped project version to 2.0.0 to reflect these architectural changes.
Removed the local Dockerfile, .dockerignore, and values.yaml, as image building and Kubernetes deployment are now handled by external sientia-module components. Updated README accordingly.
- Made `date_column` a required field in `TrainModelParams`, ensuring it must be present in the input data.
- Updated related documentation in `input-sample.md`, `README.md`, and various test scenarios to reflect the change in requirement.
- Adjusted the handling of `date_format` to default to `yyyy-MM-dd HH:mm:ss` if omitted, enhancing usability.
- Refined test scenarios to include new examples and ensure compliance with the updated parameter structure.
These changes improve the robustness of the model training workflow and clarify the expectations for input data.
- Modified `.env.example` to set local defaults for PostgreSQL, MLflow, and MinIO configurations.
- Added MongoDB configuration parameters to the environment setup.
- Updated `README.md` to reflect changes in workflow input parameters and task queue naming conventions.
- Removed the `ModelServing` class to streamline the codebase, as it was deemed unnecessary.
- Adjusted `connectors_config.py` to align with new environment variable names and improve clarity.
- Updated tests to reflect changes in configuration handling and removed tests related to the deleted `ModelServing` class.
This parameter allows customizing the threshold (1-1000) used when rem_static_win is enabled, defaulting to 1 if null.
Updates include parameter definition, business rule validation, repository logic for passing the threshold, documentation in README.md and PIPELINE_PARAMS_CHANGELOG.md, and new unit and integration tests.
* Documents support for Polynomial Regression, per-variable lag configuration, date range filtering, and NaN treatment options.
* Includes details on RCE drift metrics and comprehensive business validation rules for training parameters.
* Describes new JSON-based integration test scenarios with batch execution and updated 100% code coverage.
* Refines architecture overview and example workflow parameters.
This commit adds a new section to the README.md file detailing how to use the 'act' tool for local testing of GitHub Actions workflows. It includes installation instructions, configuration details, usage examples, and troubleshooting tips.
This commit introduces a new model_repository.py to handle MLFlow artifact generation and model persistence. It also updates the README to reflect this change and modifies training_repository.py to separate training and MLFlow operations.
This commit refactors the MLFlow activities to focus on model training rather than prediction operations. It removes prediction-related activities and metrics, and updates the MLFlow activity descriptions to reflect the change in focus. The README is also updated to reflect these changes.
This commit introduces the 'Training' activity and 'TrainingRepository' for handling ML model training operations within the Model Manager system.
- Added model_manager/activities/training.py for the Training activity, which extends BaseActivity and integrates with Temporal workflows.
- Added model_manager/utils/repository/training_repository.py for the TrainingRepository, which encapsulates the core training logic.
- Updated model_manager/activities/activities.py to include the Training activity in the main activities orchestrator.
- Updated README.md to document the new 'Training' component.
- Added unit tests for the new activity and repository.
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.
This commit introduces the initial project structure, including:
- .env.example: Example environment configuration.
- .github/workflows/quality-gate.yml: CI workflow for quality checks.
- .gitignore: Specifies intentionally untracked files that Git should ignore.
- Makefile: Automation of tasks like docker builds.
- README.md: Project documentation.
- Source code for model management, activities, utils, worker and workflows.
- Test suite.
- Dockerfile for the simulator.
- sonar-project.properties: SonarQube configuration file.
- values.yaml: Helm chart values for deployment.
