lightgbm GPU版本安装

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官网

https://lightgbm.readthedocs.io/en/latest/GPU-Windows.html

lightgbm GPU版本 Windows状况下安装：

https://www.jianshu.com/p/30555fd2bd50

如下基于ubuntu 16.04 python 3.6.5安装测试成功

一、安装软件依赖

sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev
二、安装python库

pip install setuptools wheel numpy scipy scikit-learn -U
三、安装lightGBM-GPU

sudo pip3.6 install lightgbm --install-option=--gpu --install-option="--opencl-include-dir=/usr/local/cuda/include/" --install-option="--opencl-library=/usr/local/cuda/lib64/libOpenCL.so"
四、测试

先下载测试文件而且进行文件转化

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
编写测试脚本

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': 0}

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))
测试结果以下，可见gpu版确实比cpu快

 

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