最简便的lightGBM GPU支持的安装、验证方法 - 好文

以下基于ubuntu 16.04 python 3.6.5安装测试成功

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.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))
测试结果如下，可见gpu版确实比cpu快

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