openboost 1.0.0rc1

GPU-accelerated gradient boosting, written in Python

OpenBoost

A GPU-native, all-Python platform for tree-based machine learning.

Why OpenBoost?

For standard GBDT, use XGBoost/LightGBM—they're highly optimized C++.

For GBDT variants (probabilistic predictions, interpretable GAMs, custom algorithms), OpenBoost brings GPU acceleration to methods that were previously CPU-only and slow:

• NaturalBoost: 1.3-2x faster than NGBoost
• OpenBoostGAM: 10-40x faster than InterpretML EBM

Plus: ~20K lines of readable Python. Modify, extend, and build on—no C++ required.

	XGBoost / LightGBM	OpenBoost
Code	200K+ lines of C++	~20K lines of Python
GPU	Added later	Native from day one
Customize	Modify C++, recompile	Modify Python, reload

What You Can Build

OpenBoost provides primitives (histograms, binning, tree fitting) that you combine into algorithms:

• Standard GBDT — drop-in gradient boosting with multiple growth strategies
• Distributional GBDT — predict full probability distributions with NGBoost-style natural gradient boosting
• Interpretable GAMs — explainable feature effects inspired by EBM
• DART — dropout regularization for reduced overfitting
• Linear-leaf models — linear models in tree leaves for better extrapolation
• Your own algorithms — custom losses, distributions, or entirely new methods

All run on GPU with the same Python code.

Quick Start

High-level API:

import openboost as ob

model = ob.GradientBoosting(n_trees=100, max_depth=6)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Low-level API (full control over the training loop):

import openboost as ob

X_binned = ob.array(X_train)
pred = np.zeros(len(y_train), dtype=np.float32)

for round in range(100):
    grad = 2 * (pred - y_train)  # your gradients
    hess = np.ones_like(grad) * 2
    tree = ob.fit_tree(X_binned, grad, hess, max_depth=6)
    pred += 0.1 * tree(X_binned)

Installation

pip install openboost

# With GPU support
pip install openboost[cuda]

Documentation

Full docs, tutorials, and API reference: jxucoder.github.io/openboost

• Getting Started
• User Guide
• API Reference
• Examples

Roadmap

Train-many optimization: Industry workloads often train many models (hyperparameter tuning, CV, per-segment models). XGBoost optimizes for one model fast. OpenBoost plans to enable native optimization for training many models efficiently.

References

OpenBoost implements and builds on ideas from these papers:

• Gradient Boosting: Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics.
• XGBoost: Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD.
• LightGBM: Ke, G., et al. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. NeurIPS.
• CatBoost: Prokhorenkova, L., et al. (2018). CatBoost: Unbiased Boosting with Categorical Features. NeurIPS.
• NGBoost: Duan, T., et al. (2020). NGBoost: Natural Gradient Boosting for Probabilistic Prediction. ICML.
• EBM: Nori, H., et al. (2019). InterpretML: A Unified Framework for Machine Learning Interpretability.
• DART: Rashmi, K. V., & Gilad-Bachrach, R. (2015). DART: Dropouts meet Multiple Additive Regression Trees. AISTATS.

License

Apache 2.0