Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. Thanks to Hummingbird, users can benefit from: (1) all the current and future optimizations implemented in neural network frameworks; (2) native hardware acceleration; (3) having a unique platform to support for both traditional and neural network models; and have all of this (4) without having to re-engineer their models.
Currently, you can use Hummingbird to convert your trained traditional ML models into PyTorch. Hummingbird supports a variety of tree-based classifiers and regressors. These models include scikit-learn Decision Trees and Random Forest, and also LightGBM and XGBoost Classifiers/Regressors. Support for other neural network frameworks (e.g., ONNX, TVM, and models is on our roadmap.