Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as ) 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 . Hummingbird a variety of tree-based classifiers and regressors. These models include Decision Trees and Random Forest, and also and Classifiers/Regressors. Support for other neural network frameworks (e.g., , , and models is on our .
Hummingbird was tested on Python >= 3.6 on Linux, Windows and MacOS machines. It is recommended to use a virtual environment (See: or .)
pip install hummingbird-ml
See also for common problems.