注：系统安装好后，先确认kernel kernel-headers kernel-devel kernel-firmware四个包的版本要相同
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#rpm -qa |grep kernel
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注： 先修改yum配置文件 /etc/yum.conf 修改 keepcache=1
1. 安装库
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yum –y install epel–release.noarch
(wget http://pkgs.repoforge.org/rpmforge-release/rpmforge-release-0.5.3-1.el6.rf.x86_64.rpm)
rpm —import http://apt.sw.be/RPM-GPG-KEY.dag.txt
rpm –K rpmforge–release–0.5.3–1.el6.rf.*.rpm
rpm –ivh rpmforge–release–0.5.3–1.el6.rf.*.rpm rpmforge–releaser的安装方法
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2、JDK安装
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tar –xf jdk–7u25–linux–x64.tar.gz && mv jdk1.7.0_25/ jdk1.7 mv jdk1.7/ /opt
vim /etc/profile
export JAVA_HOME=/opt/jdk1.7
export JAVA_BIN=/opt/jdk1.7/bin
export PATH=$PATH:$JAVA_HOME/bin
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export PATH=$JAVA_HOME/bin:$JRE_HOME/bin:$PATH
source /etc/profile
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3、编译安装python2.7（centOS6上用的是python2.6）
python官网：https://www.python.org/downloads/release/python-279/
替换python要在安装boost依赖之前
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# wget https://www.python.org/ftp/python/2.7.9/Python-2.7.9.tgz
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编译安装python2.7（必须先安装zlib与openssl的包再执行编译）
先安装gcc zlib openssl 等包
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tar –xvf Python–2.7.9.tgz –C /usr/src
cd /usr/src/Python–2.7.9
./configure —enable–shared
make –j12
make altinstall
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（altinstall在安装时会区分已存在的版本）(解决libpython2.7.so.1.0办法：vi /etc/ld.so.conf 添加/usr/local/lib，然后ldconfig)
替换系统中的python
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ls –l `which python python2 python2.6`
rm /usr/bin/python
ln –s –f /usr/local/bin/python2.7 /usr/bin/python
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保持yum可用性
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vim /usr/bin/yum
#!/usr/bin/python 改为 #!/usr/bin/python2.6
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4、安装pip
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curl –O https://bootstrap.pypa.io/get-pip.py # 得到一个get-pip.py
python get–pip.py
Installing collected packages: pip, setuptools, wheel
Successfully installed pip–7.1.0 setuptools–18.0.1 wheel–0.24.0
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（必须先安装openssl-devel与zlib的包，再执行python编译，若执行该命令的时候出现红色cann’t remove easy-install.pth的提醒，但目录下又无此文件，可新建后再次执行一遍命令，安装系统的时候最好把开发工具的选项给勾上，出现“Successfully installed pip-6.0.8 setuptools-14.3.1为安装成功”）
5、安装cuda-6.5及驱动
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wget http://developer.download.nvidia.com/compute/cuda/repos/rhel6/x86_64/cuda-repo-rhel6-6.5-14.x86_64.rpm
rpm –ivh cuda–repo–rhel6–6.5–14.x86_64.rpm
yum install cuda–6–5
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GTX 660显卡装cuda后会导致Xorg狂奔，直至系统死机，需要将/etc/inittab中的启动级别改为3
注，驱动包文件结构不对，导致nvidia_uvm.ko模块无法编译，需手动解决
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cd /var/lib/dkms/nvidia/346.46
cp –rv /usr/src/nvidia–346.46 build
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如果使用yum 方式安装的使用下载下的驱动包升级下
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chmod +x NVIDIA–Linux–x86_64–346.72.run
./NVIDIA–Linux–x86_64–346.72.run
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重启后，dkms会在开机时完成nvidia_uvm.ko的编译
/lib/modules/版本号/extra/下有两个包：nvidia.ko nvidia_uvm.ko
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lsmod|grep nvidia
vi /etc/rc.local #编辑该文件
modprobe nvidia_uvm #添加该条
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5.1 run包安装方式
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chmod +x cuda_6.5.19_linux_64.run
./ cuda_6.5.19_linux_64.run
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6、安装blas
yum -y install blas.x86_64 blas-devel.x86_64 \
atlas.x86_64 atlas-devel.x86_64 atlas-sse3.x86_64 atlas-sse3-devel.x86_64
7、安装opencv
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yum –y install ant.x86_64 gcc.x86_64 gcc–c++.x86_64 cmake.x86_64 git.x86_64 pkgconfig.x86_64 gtk2.x86_64 gtk2–devel.x86_64 libdc1394.x86_64 libdc1394–devel.x86_64 libjpeg–turbo.x86_64 libjpeg–turbo–devel.x86_64 libpng.x86_64 libpng–devel.x86_64 libtiff.x86_64 libtiff–devel.x86_64 jasper.x86_64 jasper–libs.x86_64 jasper–devel.x86_64 yasm.x86_64 yasm–devel.x86_64
pip install numpy
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安装ffmpeg： #此包不需要通过yum安装，yum安装版本不对
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tar –xf ffmpeg–2.6.1.tar.bz2 –C /usr/src
cd /usr/src/ffmpeg–2.6.1/
./configure —enable–shared #要以共享库方式配置，否则opencv编译时链接静态库会出错
make –j12 && make install
unzip opencv–2.4.9
cd opencv–2.4.9
mkdir release && cd release
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修改源文件NCVPixelOperations.hpp，
文件替换到opencv路径下的modules/gpu/src/nvidia/core/NCVPixelOperations.hpp
配置环境变量：
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vim /etc/profile.d/custom.sh 配置完成source /etc/profile.d/custom.sh
#!/bin/bash
export PATH=/usr/local/MATLAB/R2014a/bin:/usr/local/cuda–6.5/bin:$PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/boost–1.55.0/lib:/usr/local/cuda–6.5/lib64:/opt/caffe–master/build/lib:/usr/lib64/atlas
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/boost–1.55.0/lib:/usr/local/cuda–6.5/lib64:/opt/caffe–master/build/lib:/usr/lib64/atlas
export C_INCLUDE_PATH=$C_INCLUDE_PATH:/usr/local/boost–1.55.0/include:/usr/local/cuda–6.5/include:/opt/caffe–master/build/src:/opt/caffe–master/include
export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/boost–1.55.0/include:/usr/local/cuda–6.5/include:/opt/caffe–master/build/src:/opt/caffe–master/include
export PYTHONPATH=$PYTHONPATH:/opt/caffe–master/python
export HISTTIMEFORMAT=“%F %T “
cmake –D CMAKE_BUILD_TYPE=RELEASE –D CMAKE_INSTALL_PREFIX=/usr/local ..
make –j12
make install
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8、安装boost-1.55（1.56不兼容）
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yum –y install libicu.x86_64 libicu–devel.x86_64 bzip2–libs.x86_64 bzip2–devel.x86_64
tar –xf boost_1_55_0.tar.gz && cd boost_1_55_0
./bootstrap.sh
./b2
./b2 install
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运行./b2 install命令，默认安装在/usr/local/lib目录下，头文件在/usr/local/include/boost目录下
9、安装caffe其他依赖：
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yum –y install snappy.x86_64 snappy–devel.x86_64 hdf5.x86_64 hdf5–devel.x86_64 epel–release leveldb.x86_64 leveldb–devel.x86_64 libgfortran.x86_64
——— 编译安装protobuf–2.5.0 protobuf–2.5.0
——— tar –xvf protobuf–2.5.0.tar.gz
——— cd /usr/src/protobuf–2.5.0
./configure
make
make check
make install
——— export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
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10、编译安装caffe其他依赖包
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glog
wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
tar zxvf glog–0.3.3.tar.gz
cd glog–0.3.3
./configure
make && make install
gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip
unzip gflags–master.zip
cd gflags–master
mkdir build && cd build
export CXXFLAGS=“-fPIC” && cmake .. && make VERBOSE=1
make && make install
lmdb
git clone git://gitorious.org/mdb/mdb.git
tar –xf lmdb.tar
cd mdb/libraries/liblmdb
make && make install #若提示man1错误，手动建立一个
mkdir –p /usr/local/man/man1
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11、将matlab上传至服务器，通过图形方式安装
安装秘钥12345-67890-12345-67890
安装完成后导入lic文件，然后替换libmwservices.so到
/usr/local/MATLAB/R2014a/bin/glnxa64/进行覆盖，结束安装。
12、解决python依赖
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pip install ‘six>=1.3’
easy_install –U distribute
pip2.7 install PIL —allow–external PIL —allow–unverified PIL
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解包caffe-master.zip，并将该包移至opt目录
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cd /opt/caffe–master/python
for i in $(cat requirements.txt); do pip install $i; done #需要多执行几遍
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注：会出现一个报错，关于PIL.Image >= 1.1.7，则可使用命令pip install ‘PIL’ 进行安装
后再次执行以上的for循环语句，需要将python升级至2.7以上版本（安装及注意事项下：）
caffe
13、安装caffe
修改caffe-master/Makefile.config文件，增加如下几句
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cp /opt/caffe–master/Makefile.config.example Makefile.config
vim Makefile.config
MATLAB_DIR := /usr/local/MATLAB/R2014a/
BLAS := atlas
BLAS_LIB := /usr/lib64/atlas
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/local/lib/python2.7/site–packages/numpy/core/include \
/usr/local/include/python2.7
执行 ldconfig
make all –j12
make –j12 pycaffe
make –j12 matcaffe
make test –j12
make runtest –j12
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如果matlab要使用静态编译libprotobuf.a的话，修改Makefile
在MATLAB_CXXFLAGS项上添加-static参数即可
但使用动态库的matlab模型可能不可用
若一切没有问题，至此caffe环境安装结束，待测试。
以下为可选部分
编译安装protobuf-2.5.0 protobuf-2.5.0
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tar –xvf protobuf–2.5.0.tar.gz
cd /usr/src/protobuf–2.5.0
./configure
make
make check
make install
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
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继续安装protobuf的python模块(如果不用python，可跳过这一步)
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#cd ./python
#python setup.py build
#python setup.py test
#python setup.py install
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安装cudnn
LINUX
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cd <installpath>
export LD_LIBRARY_PATH=`pwd`:$LD_LIBRARY_PATH
Add <installpath> to your build and link process by adding –I<installpath> to your compile line and –L<installpath> –lcudnn to your link line.
==========================
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a. 编辑确保Makefile.config,启用GPU “# CPU_ONLY := 1”,并设置 “USE_CUDNN := 1”。
b. 安装cuDNN
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tar –xzvf cudnn–6.5–linux–R1.tgz
cd cudnn–6.5–linux–R1
cp lib* /usr/local/cuda–6.5/lib64/
cp cudnn.h /usr/local/cuda–6.5/include/
cd /usr/local/cuda–6.5/lib64/
rm –rf libcudnn.so libcudnn.so.6.5
chmod u=rwx,g=rx,o=rx libcudnn.so.6.5.18
ln –s libcudnn.so.6.5.18 libcudnn.so.6.5
ln –s libcudnn.so.6.5 libcudnn.so
ldconfig
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注1：将相关的头文件，库文件放到profile中定义的系统路径里即可，matlab的mex运行时需要加载对应库
caffe编译时也可在Makefile.config中修改，添加cuDNN的路径/cache/INSTALL_cuDNN/cudnn-6.5-linux-R1
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /cache/INSTALL_cuDNN/cudnn-6.5-linux-R1
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /cache/INSTALL_cuDNN/cudnn-6.5-linux-R1
注2：在使用tesla-c2050显卡时，需要在Makefile.config里改如下几个地方：
PYTHON_LIB := /usr/lib64 #原为PYTHON_LIB := /usr/lib
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib64 /usr/lib64 #原为如下：
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
注3：protobuf手动安装，不需要通过yum，yum安装版本不对，make runtest会报错，使用protobuf2.5的版本，安装方式见上，在编译caffe前安装好后再进行编译。
包：咖啡环境需要上传的包：gflags-master.zip、opencv-2.4.9.zip、boost_1_55_0.tar.gz、caffe-master.zip、glog-0.3.3.tar.gz、protobuf-2.5.0.tar.gz、cuda-repo-rhel6-6.5-14.x86_64.rpm、jdk-7u25-linux-x64.tar.gz、lmdb.tar、Python-2.7.9.tgz、 ffmpeg-2.6.1.tar.bz2、
rpmforge-release-0.5.3-1.el6.rf.x86_64.rpm、NVIDIA-Linux-x86_64-346.72.run、
NCVPixelOperations.hpp、matlab文件夹
文章转载来自：ttlsa.com
