Treelite is a universal model exchange and serialization format for decision tree forests. Treelite aims to be a small library that enables other C++ applications to exchange and store decision trees on the disk as well as the network.

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Tree compiler was migrated to TL2cgen

If you are looking for a compiler to translate tree models into C code, use TL2cgen. To migrate existing code using Treelite 3.x, consult the page Migration Guide: How to migrate from Treelite 3.x.

Why Treelite?

Universal, lightweight specification for all tree models

Are you designing a C++ application that needs to read and write tree models, e.g. a prediction server? Do not be overwhelmed by the variety of tree models in the wild. Treelite lets you convert many kinds of tree models into a common specification. By using Treelite as a library, your application now only needs to deal with one model specification instead of many. Treelite currently supports:

In addition, tree libraries can directly output trained trees using the Treelite specification. For example, the random forest algoritm in RAPIDS cuML stores the random forest object using Treelite.

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A small library that’s easy to embed in another C++ application

Treelite has an up-to-date CMake build script. If your C++ application uses CMake, it is easy to embed Treelite. Treelite is currently used by the following applications:

Quick start

Install Treelite:

# From PyPI
pip install treelite
# From Conda
conda install -c conda-forge treelite

Import your tree ensemble model into Treelite:

import treelite
model = treelite.frontend.load_xgboost_model("my_model.json")

Compute predictions using General Tree Inference Library (GTIL):

X = ... # numpy array
treelite.gtil.predict(model, data=X)