v1.5.0

On PyPI as bonsai-gbt, and the XGBoost drop-in surface.

Added

• PyPI via trusted publishing: pip install bonsai-gbt with no --find-links. Releases upload through OIDC (no stored tokens); the CUDA-enabled linux x86_64 wheel reaches the index only after the rented-GPU release gate passes, and a TestPyPI rehearsal path (workflow dispatch) proved the flow end to end before the first real upload.
• The XGBoost drop-in surface: the canonical XGBRegressor/XGBClassifier script runs with only the class name swapped. New aliases min_child_weight (tree.min_child_hess, the same minimum hessian mass per child), gamma, subsample (switches on the bernoulli sampler), and device="cuda" (picks the matching CUDA grower); XGBoost objective strings on both estimators (reg:squarederror, reg:quantileerror with quantile_alpha, binary:logistic, multi:softprob, ...); eval_set in the list-of-tuples form; evals_result(), best_iteration, and best_score backed by a per-round eval history (in-memory only, the model format is untouched; squared error is presented as rmse, the exact root); save_model/load_model/apply/iteration_range; n_features_in_. The full swap table and the deliberate differences: Switching from XGBoost.

Measured

• XGBoost 3.3 recheck (decision 87): every published standing survives against the 2026-07-21 release on a same-pod three-arm ladder; the one real competitor gain (wide-CPU histogram tiling, 2x at 1M x 4096) flips no published cell. The 16M host-memory ratio (22.1 vs 6.9GB) reproduced on a second host.