v1.3.0 — GPU training from a 2.3MB pip install

GPU training from a 2.3MB pip install, and the benchmark harness ships in the package.

Added

• CUDA in the linux x86_64 wheel (decision 70): GPU training out of the box on any NVIDIA driver R525+, SASS for sm_70 through sm_120 plus a compute_90 PTX forward-JIT floor, cudart statically linked. The whole backend costs 2.33MB of wheel (vs ~300MB for xgboost's GPU wheel) and behaves exactly like a CPU wheel on GPU-less machines. This wheel was validated on rented GPU hardware before it attached; the byte-identity model-hash gate runs across all three wheel platforms on every build.
• Runtime docker image: ghcr.io/daniel-m-campos/bonsai:cuda with the CUDA wheel preinstalled, RunPod-ready (sshd entrypoint); the release gate boots this exact image, so the image and the wheel are validated together.
• bonsai.bench in the wheel (decision 69): pip install bonsai-gbt[bench] reproduces the published benchmark tables; python -m bonsai.bench.grinsztajn out.jsonl --report re-runs the external standings suite. Normative rules in the benchmark charter.

Fixed

• Model.n_classes reports 0 unless the model was trained with the softmax objective.
• Linux wheels vendored a dynamic libomp while claiming static linkage; the workflow now documents the vendoring honestly (#134).

Changed

• CLI fit no longer runs the binning pass on validation sets (#119): per-iteration eval reads features and labels only, so the pass was pure waste in every early-stopping run.

Full changelog: CHANGELOG.md