[FIXED] PyCaret methods on GPU/TPU

Issue

When I ran best_model = compare_models() there is a huge load on CPU memory, while my GPU is unutilized. How do I run the setup() or compare_models() on GPU? Is there an in-built method in PyCaret?

Solution

Only some models can run on GPU, and they must be properly installed to use GPU. For example, for xgboost, you must install it with pip and have CUDA 10+ installed (or install a GPU xgboost version from anaconda, etc). Here is the list of estimators that can use GPU and their requirements: https://pycaret.readthedocs.io/en/latest/installation.html?highlight=gpu#pycaret-on-gpu

As Yatin said, you need to use use_gpu=True in setup(). Or you can specify it when creating an individual model, like xgboost_gpu = create_model('xgboost', fold=3, tree_method='gpu_hist', gpu_id=0).

For installing CUDA, I like using Anaconda since it makes it easy, like conda install -c anaconda cudatoolkit. It looks like for the non-boosted methods, you need to install cuML for GPU use.

Oh, and looks like pycaret can't use tune-sklearn with GPU (in the warnings here at the bottom of the tune_model doc section).

Answered By - wordsforthewise