Introduction ============ Why SMLE? --------- * **Auto-Configuration:** ``yaml`` files are automatically parsed and injected into your entrypoint, so you avoid hardcoded hyperparameters. * **Instant Logging:** All print statements and configurations are captured to local logs and compatible remote trackers. * **Remote Monitoring:** Native integration with `Weights & Biases (WandB) `_ lets you monitor experiments from anywhere. .. tip:: If you are using SMLE for the first time, start with the ``Quickstart`` section to scaffold a project, configure your first ``smle.yaml``, and run an experiment end-to-end in a few minutes. Contributing ------------ Contributions are welcome! If you have ideas for improvements, feel free to fork the repository and submit a pull request. #. Fork the Project #. Create your Feature Branch (``git checkout -b feature/AmazingFeature``) #. Commit your Changes (``git commit -m 'Add some AmazingFeature'``) #. Push to the Branch (``git push origin feature/AmazingFeature``) #. Open a Pull Request Roadmap ------- 🚀 High Priority ^^^^^^^^^^^^^^^^ High-priority goals include richer documentation, safer key management (for example, through ``.env`` support), and multiple or layered YAML configurations. * **Documentation:** Write comprehensive documentation and examples. * **Security:** Improve user key management (e.g., WandB key) using ``.env`` file support. * **Configuration:** Add support for multiple/layered YAML files. 🔮 Planned Features ^^^^^^^^^^^^^^^^^^^ Planned features include ML project templates, model utilities, notification tools, data exploration helpers, analysis utilities (such as confusion matrices), additional integrations like TensorBoard, and comprehensive testing support. * **ML Templates:** Automated creation of standard project structures. * **Model Tools:** Utilities for Neural Network creation, training, and testing. * **Notifications:** Email notification system for completed training runs. * **Data Tools:** Data exploration and visualization helpers. * **Analysis:** Result analysis tools (diagrams, confusion matrices, etc.). * **Integrations:** Support for TensorBoard and similar tracking tools. * **Testing:** Comprehensive unit and integration tests for the framework.