详细介绍在Ubuntu 16.04下搭建CUDA7.5+Caffe深度学习环境的过程步骤。
1.安装Ubuntu 16.04
省略。不懂可以自行百度，系统安装后安装必要的更新和工具。
sudo apt update
sudo apt-get upgrade
sudo apt-get install vim
sudo apt-get install cmake2.安装显卡驱动
进入all setting->Software Update，更换英伟达361.42驱动，重启电脑，使用nvidia-smi测试是否成功。
3.安装cuda
（1）安装必要的依赖库
ca-certificates-java
default-jre
default-jre-headless
fonts-dejavu-extra
freeglut3
freeglut3-dev
java-common
libatk-wrapper-java
libatk-wrapper-java-jni
libdrm-dev
libgl1-mesa-dev
libglu1-mesa-dev
libgnomevfs2-0
libgnomevfs2-common
libice-dev
libpthread-stubs0-dev
libsctp1
libsm-dev
libx11-dev
libx11-doc
libx11-xcb-dev
libxau-dev
libxcb-dri2-0-dev
libxcb-dri3-dev
libxcb-glx0-dev
libxcb-present-dev
libxcb-randr0-dev
libxcb-render0-dev
libxcb-shape0-dev
libxcb-sync-dev
libxcb-xfixes0-dev
libxcb1-dev
libxdamage-dev
libxdmcp-dev
libxext-dev
libxfixes-dev
libxi-dev
libxmu-dev
libxmu-headers
libxshmfence-dev
libxt-dev
libxxf86vm-dev
lksctp-tools
mesa-common-dev
openjdk-7-jre
openjdk-7-jre-headless
tzdata-java
x11proto-core-dev
x11proto-damage-dev
x11proto-dri2-dev
x11proto-fixes-dev
x11proto-gl-dev
x11proto-input-dev
x11proto-kb-dev
x11proto-xext-dev
x11proto-xf86vidmode-dev
xorg-sgml-doctools
xtrans-dev
libgles2-mesa-dev
nvidia-modprobe
build-essential（2）安装cuda-toolkit
① 安装cuda_7.5.18_linux.run
sudo ./cuda_7.5.18_linux.run--override安装过程如下:
Do you accept the previously read EULA? (accept/decline/quit): accept You are attempting to install on an unsupported configuration. Do you wish tocontinue? ((y)es/(n)o) [ defaultisno ]: y Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 352.39? ((y)es/(n)o/(q)uit): n Install the CUDA 7.5 Toolkit? ((y)es/(n)o/(q)uit): y Enter Toolkit Location [ defaultis /usr/local/cuda-7.5 ]: Do you want to install a symbolic link at /usr/local/cuda? ((y)es/(n)o/(q)uit): y Install the CUDA 7.5 Samples? ((y)es/(n)o/(q)uit): y Enter CUDA Samples Location [ defaultis /home/kinghorn ]: /usr/local/cuda-7.5 Installing the CUDA Toolkit in /usr/local/cuda-7.5 ... Finished copying samples. =========== = Summary = =========== Driver: Not Selected Toolkit: Installed in /usr/local/cuda-7.5 Samples: Installed in /usr/local/cuda-7.5② 设置环境变量
vi /home/xxx/.bashrc添加如下内容：
export PATH=/usr/local/cuda/bin:$PATH执行如下命令使环境变量生效
source /home/xxx/.bashrc将cuda动态库添加到动态库管理器
sudo vi /etc/ld.so.conf.d/cuda.conf添加：
/usr/local/cuda/lib64执行ldconfig使新加的库生效
sudo ldconfig③ 强制使用gcc5
编辑/usr/local/cuda/include/host_config.h文件，注释掉115行
#error -- unsupported GNU version! gcc versions later than 4.9 are not supported! 改为：
//#error-- unsupported GNU version! gcc versions later than 4.9 are not supported! （3）编译cuda例子与测试
进入到/usr/local/cuda/NVIDIA_CUDA-7.5_Samples/1_Utilities/deviceQuery目录执行：
sudo make
./deviceQuery4.安装cudnn库
（1）解压
tar xzvf cudnn-xxx-ga.tgz得到cuda文件夹里面含有的lib64和include两个文件夹
（2）拷贝到cuda安装目录
sudo cp cuda/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64注意：拷贝后将链接删除重新建立链接，否则，拷贝是多个多个不同名字的相同文件，链接关系参见cudnn解压后的文件夹。也可以分别拷贝每一个文��，链接文件拷贝使用cp -d命令。
5.安装opencv3.1.0
（1）安装基本必要库
sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev（2）配置opencv，生成Makefile
cd opencv-3.1.0
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..在configure过程中过程中，可能会出现下面的错误:
– ICV: Downloading ippicv_linux_20151201.tgz…在直接下载该文件的过程中，会因为超时而失败，需要收到下载，将其拷贝至opencv-3.1.0/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b目录内，重新执行配置命令。
（3）编译opencv
make -j8此时可能会出现另一个错误：
/usr/include/string.h: In function ‘void* __mempcpy_inline(void*, constvoid*, size_t)’: /usr/include/string.h:652:42: error: ‘memcpy’ was not declared inthisscopereturn (char *) memcpy (__dest, __src, __n) + __n;这是因为ubuntu的g++版本过高造成的，只需要在opencv-3.1.0目录下的CMakeList.txt 文件的开头加入：
set(CMAKE_CXX_FLAGS “${CMAKE_CXX_FLAGS} -D_FORCE_INLINES”)添加之后再次进行编译链接即可。
（4）查看版本号
pkg-config--modversionopencv （5）安装
sudo make install6.安装caffe与配置
（1）安装必要的依赖库
sudo apt-get install build-essential
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install Python-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev如果这些库都能顺利安装，会大大减少后面遇到的问题。
（2）下载caffe-master并解压得到源码包
解压：
unzip caffe-master.zip （3）修改配置文件Make.config
cd caffe-master
cp Makefile.config.example Makefile.config
vi Makefile.config将# USE_CUDNN := 1前得#注释去掉，表示使用cuDNN，如果不是使用GPU，可以将# CPU_ONLY := 1前得注释去掉。这里我使用cuDNN来加速。
（4）编译caffe
方法1：使用cmake编译
mkdir build
cd build
cmake ..
make all-j8这种方法一般不会出现问题。
方法2：直接使用gcc编译
make -j8错误1：
src/caffe/net.cpp:8:18: fatal error: hdf5.h: No such fileordirectorycd /usr/lib/x86_64-linux-gnu
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5_serial.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_serial_hl.so修改Makefile.config
INCLUDE_DIRS:= $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS:= $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial错误2：
error-- unsupported GNU version! gcc versions later than 5.3 are not supported!目前caffe不支持高于5.3的gcc，理论上可通过对gcc，g++降级解决，但是降级后还会引起其他兼容性问题，因此并不能解决实际问题，下面附上降级方法。解决方法在后面。
① 安装低版本gcc、g++
sudo apt-get install gcc-4.7 gcc-4.7-multilib
sudo apt-get install g++-4.7 g++-4.7-multilib② 设置优先级
sudoupdate-alternatives--install/usr/bin/gccgcc/usr/bin/gcc-4.740sudoupdate-alternatives--install/usr/bin/gccgcc/usr/bin/gcc-550sudoupdate-alternatives--install/usr/bin/g++g++/usr/bin/g++-4.740sudoupdate-alternatives--install/usr/bin/g++g++/usr/bin/g++-550③ 选择版本
sudo update-alternatives --config gccThere are 2 choices for the alternative gcc (providing /usr/bin/gcc)
Selection Path Priority Status ------------------------------------------------------------ 0 /usr/bin/gcc-5 50 auto mode* 1 /usr/bin/gcc-4.7 40 manual mode
2 /usr/bin/gcc-5 50 manual mode
sudoupdate-alternatives--configg++There are 2 choices for the alternative g++ (providing /usr/bin/g++).
Selection Path Priority Status ------------------------------------------------------------ 0 /usr/bin/g++-5 50 auto mode* 1 /usr/bin/g++-4.7 40 manual mode
2 /usr/bin/g++-5 50 manual mode错误3：
/usr/include/string.h: In function ‘void* __mempcpy_inline(void*, constvoid*, size_t)’: /usr/include/string.h:652:42: error: ‘memcpy’ was not declared inthisscopereturn (char *) memcpy (__dest, __src, __n) + __n;NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)改为：
NVCCFLAGS +=-D_FORCE_INLINES-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)错误3：
/usr/bin/ld: cannot find -lippicvcp opencv-3.1.0/3rdparty/ippicv/unpack/ippicv_lnx/lib/intel64/libippicv.a /usr/local/lib再次编译即可。
至此，gcc、g++降级完成。
下面是错误2 的真正解决方法（红色字体）：
sudo vi /usr/local/cuda/include/host_config.h
#if __GNUC__ > 5 || (__GNUC__ == 5 && __GNUC_MINOR__ > 3)#error -- unsupported GNU version! gcc versions later than 5.3 are not supported!修改为：
#if __GNUC__ > 5 || (__GNUC__ == 5 && __GNUC_MINOR__ > 4)#error -- unsupported GNU version! gcc versions later than 5.4 are not supported!我的gcc版本为5.4.0，可根据具体情况修改。
（5）编译caffe的python接口
make pycaffe出错：
python/caffe/_caffe.cpp:10:31: fatal error: numpy/arrayobject.h: No such fileordirectory原因是numpy路径配置错误将：
PYTHON_INCLUDE:= /usr/include/python2.7 \ /usr/lib/python2.7/dist-packages/numpy/core/include改为：
PYTHON_INCLUDE:= /usr/include/python2.7 \ /usr/local/lib/python2.7/dist-packages/numpy/core/include （6）测试caffe
make runtest这个时间有点长。
7.运行手写体例程
caffe自带手写体识别的测试例子。每一步caffe都已经写好脚本，执行几个简单命令就可以将第一个深度学习程序跑起来。
（1）获取数据（并完成数据标注）
shdata/mnist/get_mnist.sh（2）将标签数据转换成caffe使用的LMDB数据格式
sh examples/mnist/create_mnist.sh（3）网络求解文件修改
vi caffe-master/examples/mnist/lenet_solver.prototxt# The train/test net protocol buffer definitionnet:"examples/mnist/lenet_train_test.prototxt"# test_iter specifies how many forward passes the test should carry out.# In the case of MNIST, we have test batch size 100 and 100 test iterations,# covering the full 10,000 testing images.test_iter:100# Carry out testing every 500 training iterations.test_interval:500# The base learning rate, momentum and the weight decay of the network.base_lr:0.01momentum:0.9weight_decay:0.0005# The learning rate policylr_policy:"inv"gamma:0.0001power:0.75# Display every 100 iterationsdisplay:100# The maximum number of iterationsmax_iter:10000# snapshot intermediate resultssnapshot:5000snapshot_prefix:"examples/mnist/lenet"# solver mode: CPU or GPUsolver_mode: GPU 最后一行，训练过程采用CPU、GPU选择，如果不使用GPU，修改solver_mode: GPU为solver_mode: CPU即可，这里我使用GPU。
（4）执行训练脚本
sh examples/mnist/train_lenet.sh大约10分钟左右，模型训练完成
I0716 14:46:01.36070927985 solver.cpp:404] Test net output #0: accuracy = 0.9908
I0716 14:46:01.36075027985 solver.cpp:404] Test net output #1: loss = 0.0303895 (* 1 = 0.0303895 loss)
I0716 14:46:01.36075527985 solver.cpp:322] Optimization Done.
I0716 14:46:01.36075727985 caffe.cpp:222] Optimization Done.模型精度在0.99以上。
至此，Caffe+Linux深度学习环境搭建完成。
文章来源网络，作者：运维，如若转载，请注明出处：https://shuyeidc.com/wp/223287.html<
