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GPU

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

A GPU is a massively parallel processor designed to execute many computational operations concurrently.

A GPU is a massively parallel processor that can shorten model-training waits when datasets contain many cases or features. For gradient-boosted decision trees, it is especially effective with XGBoost and can also accelerate LightGBM. section 6.4.3

Use GPU acceleration when reducing the training data would compromise model quality:

  • Configure XGBoost with device="cuda" or device="gpu"; select a particular device with a CUDA ordinal such as device="cuda:1". section 6.4.3
  • Treat gpu_hist as a deprecated XGBoost 2.0.0 setting and use device instead. section 6.4.3
  • Check CUDA 11.00 or later and a GPU with compute capability 5.0 before using XGBoost acceleration. section 6.4.3
model.set_param({"predictor": "gpu_predictor"})
shap_values = model.predict(X, pred_contribs=True)
shap_interaction_values = model.predict(X, pred_interactions=True)

Inspect the XGBoost prediction configuration and GPU-based SHAP prediction calls. section 6.4.3

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

How deep it goes
Explains acceleration, configuration, hardware requirements, and GPU tree growth, but stops short of detailed GPU architecture.
Examples
Uses XGBoost device settings, CUDA device ordinals, GPU prediction, and SHAP contribution calculations.
What it assumes
Assumes familiarity with gradient-boosted decision trees and focuses on practical settings rather than teaching GPU architecture.
What you can do after
Operate/configure GPU-accelerated XGBoost training and prediction while checking supported hardware and CUDA requirements.
How it connects
Connects GPU acceleration to XGBoost, LightGBM, CUDA, decision trees, and feature histograms.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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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.