The current version of XGBoost needs a graphics card with compute capability 3.5 or better and works with CUDA toolkits version 9.0 and above.īasically, it needs to be a decent, modern card and it has to be an NVIDIA one. You also need to install the CUDA toolkit software packages on your machine. Therefore, to use your GPU with XGBoost you need to have a CUDA-capable graphics card. XGBoost uses NVIDIA’s CUDA (Compute Unified Device Architecture) parallel computing platform, which lets software developers modify their code so it can run on a GPU instead of a slower CPU. However, your models should run several times quicker on the GPU - maybe more if you have a really powerful graphics card or several of them at your disposal. In XGBoost, it’s in the tree construction (or training) and prediction steps that the GPU is used to accelerate the process, so you won’t see much performance gain if the bottleneck is inefficient Pandas loops in your feature engineering steps. It’s for this reason that cryptocurrency miners favour GPUs (and ASICs) over CPUs. Some studies on deep learning neural nets reckon GPU performance can be as much as 250 times quicker than CPU. The GPU (Graphics Processing Unit) in your graphics card is much more efficient for performing highly parallel calculations, compared to the CPU in your computer. The XGBoost package, which uses the popular Extreme Gradient Boosting algorithm, is not only extremely powerful and very fast, it also has the advantage of being able to run on your GPU, which means that model training times can be significantly reduced. However, with the right kind of machine and a half decent graphics card, you can greatly speed things up - at least if your chosen model is XGBoost. Quite often I will end up leaving my machine running overnight or over the weekend and it’s still not finished training the model. That your models can take quite a while to train, leaving your machine busy for long periods with the fans going full blast. If you’re not fortunate enough to have a really powerful data science workstation for your work, one of the problems
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