liveBook · Manning

Code compilation

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

Model compilation is the transformation of a trained model into lower-level executable code for faster inference or deployment.

Code compilation transforms a trained model into lower-level executable code, targeting faster prediction and easier deployment. For tree-based models, compilation can rebuild prediction trees in languages or toolchains such as C, Java, or LLVM rather than interpreting the original model directly. section 6.4.3

Compilation complements GPU acceleration rather than replacing it: GPUs mainly speed training and prediction, while compiled trees focus on efficient prediction-time execution. The benefit depends on the model, data, and target environment. section 6.4.3

Compiling an XGBoost predictor

An XGBoost pipeline saves its model as JSON, loads it into Treelite, generates C code, and exports a shared library with parallel compilation enabled.

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

How deep it goes
Demonstrates model export, conversion, code generation, library creation, preprocessing, and prediction, without detailing generated machine code internals.
Examples
Uses Treelite with XGBoost and lleaves with LightGBM to compile saved tree models for prediction.
What it assumes
Assumes a trained XGBoost or LightGBM tree model, its saved native format, and an available compilation toolchain.
What you can do after
Apply it via Treelite, tl2cgen, or lleaves to generate compiled predictors from trained tree models.
How it connects
Connects gradient-boosted decision trees with XGBoost, LightGBM, GPU computing, C, and LLVM.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
liveBookmanning.com
Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

Compare how 7 books explain code compilation →

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.