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Check if XGBoost Supports GPU

XGBoost is known for its excellent performance, and using GPU acceleration can further speed up the training process, especially for large datasets.

XGBoost provides GPU support, but it depends on the installation and system configuration. Here’s how you can check if your XGBoost installation supports GPU.

import xgboost


Output (your output may differ):

{'BUILTIN_PREFETCH_PRESENT': True, 'CLANG_VERSION': [14, 0, 0], 'DEBUG': False, 'MM_PREFETCH_PRESENT': True, 'USE_CUDA': False, 'USE_FEDERATED': False, 'USE_NCCL': False, 'USE_OPENMP': True, 'USE_RMM': False, 'libxgboost': '/usr/local/lib/python3.12/site-packages/xgboost/lib/libxgboost.dylib'}

We can make the output easier to read with a little formatting, for example:

import xgboost

build_info = xgboost.build_info()
for name in sorted(build_info.keys()):
    print(f'{name}: {build_info[name]}')

Output (your output may differ):

CLANG_VERSION: [14, 0, 0]
DEBUG: False
USE_RMM: False
libxgboost: /usr/local/lib/python3.12/site-packages/xgboost/lib/libxgboost.dylib

Here’s what to look for:

  1. Check if the output includes a USE_CUDA field set to True. If present, it indicates that XGBoost was built with CUDA support, enabling GPU acceleration.
  2. If the USE_CUDA field is missing or shows False, it means XGBoost was built without GPU support.

To use XGBoost with GPU acceleration, you need:

Installing XGBoost with GPU support depends on your system and package manager.

Here are a few common methods:

If you don’t see GPU support in your current XGBoost installation, you may need to reinstall it with the GPU-enabled version.

Note that even with GPU support, XGBoost will only use the GPU if the dataset and model are large enough to benefit from GPU acceleration. For smaller datasets, the overhead of copying data to and from the GPU may outweigh the performance gains.

To explicitly enable or disable GPU usage, you can set the device parameter to cuda or gpu when training your model:

# Enable GPU acceleration
model = xgb.XGBClassifier(device='cuda')

# Disable GPU acceleration (use CPU)
model = xgb.XGBClassifier(device='cpu')

By checking for GPU support and configuring XGBoost accordingly, you can leverage the power of your GPU to accelerate model training and improve overall performance.

See Also