1、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

2、安装python库

pip install setuptools wheel numpy scipy scikit-learn -U

3、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"

4、测试

先下载测试文件并且进行文件转化

git clone https://github.com/guolinke/boosting_tree_benchmarks.gitcd boosting_tree_benchmarks/datawget "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz"gunzip HIGGS.csv.gzpython higgs2libsvm.py

编写测试脚本

import lightgbm as lgbimport timeparams = {'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))

测试结果如下，可见gpu版确实比cpu快