Changelog

Full release notes live on GitHub. The highlights below summarise major updates.

v0.2.0

  • Multi-GPU orchestration now auto-tunes max_in_flight based on the number of detected devices and keeps Dask clusters alive until every future is drained. This prevents premature shutdowns on longer studies and improves throughput on 4+ GPU systems.

  • Added cohort-wide helpers in genboostgpu.tuning, including select_tuning_windows() for stratified sampling and global_tune_params() for Optuna-backed ridge refits derived from sparsity targets.

  • Documentation now covers the reproducibility checklist, deterministic Optuna configuration, and richer tutorials linked from examples/ so new users can mirror the exact benchmarking pipelines.

v0.1.0

  • Initial public release with elastic net boosting, cis-window preprocessing, and PLINK/CuPy data loaders.