带 GPU 的 Lightgbm 分类器

model = lgbm.LGBMClassifier(
    n_estimators=1250,
    num_leaves=128,
    learning_rate=0.009,
    verbose=1
)

使用 LGBM 分类器， 现在有没有办法通过 GPU 来使用它？

首先，您需要为 GPU 构建 LightGBM，例如：

git clone --recursive https://github.com/Microsoft/LightGBM 
cd LightGBM && mkdir build && cd build
cmake -DUSE_GPU=1 ..
make -j4
pip uninstall lightgbm
cd ../python-package/ && python setup.py install

之后您可以使用device="gpu"在 GPU 上训练模型的参数，例如：

lgbm.train(params={'device'='gpu'}, ...)

or

lgbm.LGBMClassifier(device='gpu')

并加速大型数据集：

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import lightgbm as lgbm
X,y = make_classification(n_samples=10000000, n_features=100, n_classes=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

%%timeit
model = lgbm.LGBMClassifier(device="gpu")
model.fit(X_train, y_train)
19.9 s ± 163 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
model = lgbm.LGBMClassifier(device="cpu")
model.fit(X_train, y_train)
1min 23s ± 46.4 s per loop (mean ± std. dev. of 7 runs, 1 loop each)