在GPU上运行LGBM_lightgbmerror: gpu tree learner was not enabled in

在GPU上运行LGBM 并做速度对比

2019/11/4

参考文章：
最简便的lightGBM GPU支持的安装、验证方法 - lccever的博客 - CSDN博客
https://blog.csdn.net/lccever/article/details/80535058

目录: /home/hexi/boost

实践：

1. 安装依赖

sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev

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2. 安装PY库

pip install setuptools wheel numpy scipy scikit-learn -U
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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"

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4. 测试脚本

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
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等待下载数据，压缩包有2.6G大小，需要比较长的时间。
下载后解压的数据文件有8G！
运行higgs2libsvm.py，把数据转换成libsvm格式；

编写测试脚本，并运行：

cd /home/hexi/boost/boosting_tree_benchmarks/data
python lgbm-gpu.py
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报错：

lightgbm.basic.LightGBMError: b'GPU Tree Learner was not enabled in this build. Recompile with CMake option -DUSE_GPU=1'
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参考另一篇文章，重新编译：
版权声明：本文为CSDN博主「·清尘·」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u012969412/article/details/71433960

$ git clone --recursive https://github.com/Microsoft/LightGBM
$ cd LightGBM
$ mkdir build
$ cd build
$ cmake -DUSE_GPU=1 .. 
$ make -j

cd ../LightGBM/python-package
sudo python setup.py install
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编译完后再运行测试程序：

cd /home/hexi/boost/boosting_tree_benchmarks/data
python lgbm-gpu.py
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运行结果如下：

hexi@ubuntu:~/boost/boosting_tree_benchmarks/data$ python lgbm-gpu.py
[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 0
[LightGBM] [Info] Using GPU Device: GeForce GTX 1080 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
[LightGBM] [Info] 28 dense feature groups (280.38 MB) transferred to GPU in 0.183250 secs. 0 sparse feature groups
[LightGBM] [Info] Start training from score 0.529963
gpu version elapse time: 15.894050121307373
[LightGBM] [Info] Total Bins 1524
[LightGBM] [Info] Number of data: 10500000, number of used features: 28
[LightGBM] [Info] Start training from score 0.529963
cpu version elapse time: 10.747112035751343

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测试发现模型创建有个时间差，也就是后面CPU的模型不需要创建时间了，所以时间变短了。
为了公平起见，把两个模型的运行分开成两个PY程序，然后用一个程序调用,
运行结果：

hexi@ubuntu:~/boost/boosting_tree_benchmarks/data$ python lgbmtest.py
Dataset loading...
GPU train beginning...
[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 0
[LightGBM] [Info] Using GPU Device: GeForce GTX 1080 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
[LightGBM] [Info] 28 dense feature groups (280.38 MB) transferred to GPU in 0.197229 secs. 0 sparse feature groups
[LightGBM] [Info] Start training from score 0.529963
gpu version elapse time: 12.653455257415771
Dataset loading...
CPU train beginning...
[LightGBM] [Info] Total Bins 1524
[LightGBM] [Info] Number of data: 10500000, number of used features: 28
[LightGBM] [Info] Start training from score 0.529963
cpu version elapse time: 19.767507553100586
hexi@ubuntu:~/boost/boosting_tree_benchmarks/data$
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这时候就发现区别了。

运行截图如下：

** 补充 **
很多人测试的时候发现GPU的速度更慢，其实是因为把GPU和CPU的代码放到同一个文件里执行了，这样会造成对模型重新创建速度不一致。
正确的方式是把GPU测试和CPU测试的程序，分成两个文件；
然后使用一个脚本程序来启动它们：

lgbm-cpu.py 代码如下：

#!/usr/bin/env python3
#coding:utf-8

__author__ = 'xmxoxo<xmxoxo@qq.com>'

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': 'cpu'
}

print('Dataset loading...')
dtrain = lgb.Dataset('./higgs.train')
print('CPU train beginning...')
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))

if __name__ == '__main__':
    pass

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lgbm-gpu.py 代码如下：

#!/usr/bin/env python3
#coding:utf-8

__author__ = 'xmxoxo<xmxoxo@qq.com>'



import lightgbm as lgb
import time
 
 
print('Dataset loading...')
dtrain = lgb.Dataset('./higgs.train')

def lgbm_gpu ():
    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}

    print('GPU train beginning...')
    t0 = time.time()
    #print(t0)
    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(t1)
    print('gpu version elapse time: {}'.format(t1-t0))


lgbm_gpu()



def lgbm_cpu ():
    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',
    }

    print('开始测试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))
if __name__ == '__main__':
    pass
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然后用一个脚本来启动上述的测试程序，当然你也可以手工执行。

lgbmtest.py 代码如下：

#!/usr/bin/env python3
#coding:utf-8

__author__ = 'xmxoxo<xmxoxo@qq.com>'

import os

os.system("python lgbm-gpu.py") 
os.system("python lgbm-cpu.py") 

if __name__ == '__main__':
    pass
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