March 2025 monthly summary for lanl/Yoke focusing on business value and technical achievements. Delivered a key feature to simplify study script execution from the harness by introducing a symbolic link to START_study.py within the ch_lightning_loderunner app directory, enabling seamless execution of the study script from the harness and improving workflow efficiency. No major bug fixes were reported this month. Overall impact includes reduced friction for running experiments, improved reproducibility, and clearer traceability of changes. Technologies demonstrated include filesystem operations (symbolic links), cross-directory references, and commit-based traceability.

February 2025 monthly summary for lanl/Yoke: Implemented scheduled sampling in LodeRunner time-series training, expanded dataset utilities and training workflows, and established robust test coverage to validate improvements in time-series prediction accuracy. This work reduces exposure bias, enhances sequential data learning, and provides a foundation for more flexible experimentation in future sprints. Overall impact: improved model robustness and faster iteration on sequential forecasting tasks, contributing to better product reliability and business value.

2024-12 monthly summary for lanl/Yoke. Key feature delivered: comprehensive MLP test coverage improvements for the Swin Transformer encoder (yoke.models.vit.swin.encoder). The effort focused on strengthening test reliability and maintainability without altering production behavior. Reorganized the test structure, added thorough unit tests for the MLP class, and enhanced documentation via docstrings and explicit type hints. This work facilitates safer future changes and faster onboarding for new contributors. Key commits include adding tests for the MLP class and moving pytest files while implementing ruff formatting.