mpsboost 0.2.0b0

Lightweight gradient boosting for Apple Silicon with an MPS/Metal backend

MPSBoost

MPSBoost is an early-stage gradient boosting project for Apple Silicon. Its current accelerated backend uses custom Metal compute kernels for squared-error gradients and two-stage histogram construction while keeping one deterministic tree-building implementation shared with the CPU oracle.

Development status: 0.2.0b0 is the first GPU hot-path beta milestone. It supports dense numeric regression with a real MPS training path, split-scan and partition kernels, histogram subtraction, buffer reuse, and documented performance boundaries.

Installation

python -m pip install mpsboost

Accelerated releases provide prebuilt Apple Silicon wheels; normal users will not need a heavyweight framework, package manager, CMake, or a local Metal shader compiler.

Estimator-style API

import mpsboost as mps

model = mps.MPSBoostRegressor(
    n_estimators=200,
    learning_rate=0.05,
    max_depth=6,
    device="mps",
)

model.fit(X_train, y_train)
prediction = model.predict(X_test)
model.save_model("model.mpsb")

restored = mps.MPSBoostRegressor(device="mps")
restored.load_model("model.mpsb")

Backend diagnostics

The native backend exposes non-sensitive device diagnostics:

import mpsboost as mps

print(mps.__version__)
print(mps.is_available())
print(mps.system_info())

Project principles

• Familiar XGBoost/scikit-learn-style Python entry points.
• device="mps" as the stable user-facing Apple GPU backend name.
• Custom Metal kernels for tree-specific irregular computation.
• Prebuilt wheels and no heavyweight Python runtime dependency.
• Explicit errors instead of silent CPU fallback.
• End-to-end benchmarks, including preprocessing and synchronization.

Status

The public API currently includes MPSBoostRegressor, is_available, system_info, and __version__. Training supports dense finite float32/float64-compatible data, squared error, deterministic quantization, depth-limited histogram trees, model save/load, and explicit device="mps" or diagnostic device="cpu" selection.

The checked-in S6 benchmark records both regressions and wins. On the M2 Ultra validation machine, small end-to-end training remains slower on MPS, while the gbdt-large-wide scenario reached a 1.629x median speedup with maximum prediction difference around 5.4e-6 versus the CPU oracle.

Classification, missing values, sparse matrices, categorical features, sampling, early stopping, public GPU prediction, and full third-party API compatibility are not implemented in this milestone. Small datasets are expected to be slower on the GPU because fixed device setup and synchronization costs dominate; the checked-in benchmark report preserves this regression region alongside larger wins.

Independence notice

MPSBoost is an independent open-source project. It is not affiliated with, endorsed by, or sponsored by Apple Inc., the XGBoost project, or the scikit-learn project. Apple, Metal, and Metal Performance Shaders may be trademarks of Apple Inc.

License

Apache-2.0