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?
Asked
Active
Viewed 4,873 times
4
LITDataScience
- 380
- 5
- 14
-
2PyCaret is not for deep learning. Can it leverage GPU? – Frank May 27 '21 at 20:29
-
For some methods, it can. But for most, it doesn't. – LITDataScience May 30 '21 at 11:11
2 Answers
6
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).
wordsforthewise
- 13,746
- 5
- 87
- 117
2
To use gpu in PyCaret you have to simply pas use_gpu=True as parameter in setup function. Example: model = setup(data,target_variable,use_gpu=True)
Yatin Jain
- 21
- 1
-
Doesn't work with Kaggle. Tried this already and it is only using the RAM and the CPU, and not the GPU. – LITDataScience Jun 30 '21 at 12:20
-
1@LITDataScience possibly because the models that can use GPU are not compiled for it (e.g. xgboost should be installed with pip and CUDA 10+ needs to be installed). Here is the list and requirements: https://pycaret.readthedocs.io/en/latest/installation.html?highlight=gpu#pycaret-on-gpu – wordsforthewise Aug 29 '21 at 17:04