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在 ubuntu 16.04 上安装 LightGBM gpu版

  1. 安装相关的依赖（见官网，这里省略）

  2. 安装LGBM，在某个目录（It's up to u.）下:

    git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM
mkdir build
cd build
# cmake -DUSE_GPU=1 ..
# 由于服务器上cuda位置不是默认(我是cuda-10.0)的，因此使用了下面这个命令
# if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:
cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda-10.0/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda-10.0/include/ ..
make -j$(nproc)
cd ..

    可以看到新生成了2个二进制文件—— lightgbm 和 lib_lightgbm.so。

    cd python-package/
# 因为我是在conda env中
python setup.py install --precompile

    不出意外就可以了。

  3. 测试

git clone https://github.com/guolinke/boosting_tree_benchmarks.git
cd boosting_tree_benchmarks/data
wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz"
gunzip HIGGS.csv.gz
python higgs2libsvm.py

新建一个test.py文件，使用以下代码：

import lightgbm as lgb
import time


params = {'max_bin': 63,
'num_leaves': 255,
'learning_rate': 0.1,
'tree_learner': 'serial',
'task': 'train',
'is_training_metric': 'false',
'min_data_in_leaf': 1,
'min_sum_hessian_in_leaf': 100,
'ndcg_eval_at': [1,3,5,10],
'sparse_threshold': 1.0,
'device': 'gpu',
'gpu_platform_id': 0,
'gpu_device_id': 2,  # 因为有4块卡
'num_threads': 35}  # 这个比较关键


dtrain = lgb.Dataset('data/higgs.train')
t0 = time.time()
gbm = lgb.train(params, train_set=dtrain, num_boost_round=10,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True,
          keep_training_booster=False, callbacks=None)
t1 = time.time()

print('gpu version elapse time: {}'.format(t1-t0))


params = {'max_bin': 63,
'num_leaves': 255,
'learning_rate': 0.1,
'tree_learner': 'serial',
'task': 'train',
'is_training_metric': 'false',
'min_data_in_leaf': 1,
'min_sum_hessian_in_leaf': 100,
'ndcg_eval_at': [1,3,5,10],
'sparse_threshold': 1.0,
'device': 'cpu'
}

t0 = time.time()
gbm = lgb.train(params, train_set=dtrain, num_boost_round=10,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True,
          keep_training_booster=False, callbacks=None)
t1 = time.time()

print('cpu version elapse time: {}'.format(t1-t0))
python test.py

出现如下字段表示成功：

[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 1524
[LightGBM] [Info] Number of data: 10500000, number of used features: 28
[LightGBM] [Info] Using requested OpenCL platform 0 device 2
[LightGBM] [Info] Using GPU Device: GeForce RTX 2080 Ti, Vendor: NVIDIA Corporation
[LightGBM] [Info] Compiling OpenCL Kernel with 64 bins...
[LightGBM] [Info] GPU programs have been built
[LightGBM] [Info] Size of histogram bin entry: 12
....
....