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Treelite

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Treelite is a tool that compiles decision-tree ensemble models into portable low-dependency C or Java code.

Treelite compiles decision-tree ensemble models into portable C or Java with minimal dependencies, helping production systems make predictions faster and deploy models more easily. section 6.4.3

A practical Treelite workflow has four important considerations:

  • Train the model normally, then save and load the model through Treelite. section 6.4.3
  • Generate C code and export a compiled library with a toolchain such as GCC. section 6.4.3
  • Transform predictors before inference: the preprocessing pipeline is not compiled with the model. section 6.4.3
  • Convert input data to DMatrix format when using the compiled XGBoost model. section 6.4.3

XGBoost prediction speedup

Fit an XGBoost pipeline, extract its model, save it as JSON, load it with Treelite, generate C code, export a shared library, and predict with the compiled runtime.

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Explains compilation, model-only deployment, preprocessing limits, and DMatrix conversion without detailing generated-code internals.
Examples
Uses an XGBoost pipeline that exports a compiled shared library through Treelite, tl2cgen, and GCC.
What it assumes
Assumes a trained tree model and separate knowledge of predictor preprocessing and XGBoost DMatrix inputs.
What you can do after
Operate Treelite and tl2cgen to compile an XGBoost model for faster Python-side prediction.
How it connects
Connects gradient-boosted decision trees with XGBoost, LightGBM, scikit-learn, C, Java, and model deployment.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.