注：系統安裝好后，先確認kernel kernel-headers kernel-devel kernel-firmware四個包的版本要相同
#rpm -qa |grep kernel
注： 先修改yum配置文件 /etc/yum.conf 修改 keepcache=1
1. 安裝庫
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
2、JDK安裝
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
3、編譯安裝python2.7（centOS6上用的是python2.6）
python官網：https://www.python.org/downloads/release/python-279/
替換python要在安裝boost依賴之前
# wget https://www.python.org/ftp/python/2.7.9/Python-2.7.9.tgz
編譯安裝python2.7（必須先安裝zlib與openssl的包再執行編譯）
先安裝gcc zlib openssl 等包
tar -xvf Python-2.7.9.tgz -C /usr/src
cd /usr/src/Python-2.7.9
./configure --enable-shared
make -j12
make altinstall
（altinstall在安裝時會區分已存在的版本）(解決libpython2.7.so.1.0辦法：vi /etc/ld.so.conf 添加/usr/local/lib，然后ldconfig)
替換系統中的python
ls -l `which python python2 python2.6`
rm /usr/bin/python
ln -s -f /usr/local/bin/python2.7 /usr/bin/python
保持yum可用性
vim /usr/bin/yum
#!/usr/bin/python 改為 #!/usr/bin/python2.6
4、安裝pip
curl -O https://bootstrap.pypa.io/get-pip.py # 得到一個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
（必須先安裝openssl-devel與zlib的包，再執行python編譯，若執行該命令的時候出現紅色cann't remove easy-install.pth的提醒，但目錄下又無此文件，可新建后再次執行一遍命令，安裝系統的時候最好把開發工具的選項給勾上，出現“Successfully installed pip-6.0.8 setuptools-14.3.1為安裝成功”）
5、安裝cuda-6.5及驅動
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
GTX 660顯卡裝cuda后會導致Xorg狂奔，直至系統死機，需要將/etc/inittab中的啟動級別改為3
注，驅動包文件結構不對，導致nvidia_uvm.ko模塊無法編譯，需手動解決
cd /var/lib/dkms/nvidia/346.46
cp -rv /usr/src/nvidia-346.46 build
如果使用yum 方式安裝的使用下載下的驅動包升級下
chmod +x NVIDIA-Linux-x86_64-346.72.run
./NVIDIA-Linux-x86_64-346.72.run
重啟后，dkms會在開機時完成nvidia_uvm.ko的編譯
/lib/modules/版本號/extra/下有兩個包：nvidia.ko nvidia_uvm.ko
lsmod|grep nvidia
vi /etc/rc.local #編輯該文件
modprobe nvidia_uvm #添加該條
5.1 run包安裝方式
chmod +x cuda_6.5.19_linux_64.run
./ cuda_6.5.19_linux_64.run
6、安裝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、安裝opencv
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
安裝ffmpeg： #此包不需要通過yum安裝，yum安裝版本不對
tar -xf ffmpeg-2.6.1.tar.bz2 -C /usr/src
cd /usr/src/ffmpeg-2.6.1/
./configure --enable-shared #要以共享庫方式配置，否則opencv編譯時鏈接靜態庫會出錯
make -j12 && make install
unzip opencv-2.4.9
cd opencv-2.4.9
mkdir release && cd release
修改源文件NCVPixelOperations.hpp，
文件替換到opencv路徑下的modules/gpu/src/nvidia/core/NCVPixelOperations.hpp
配置環境變量：
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=$PYTHONPATH:/opt/caffe-master/python
export HISTTIMEFORMAT="%F %T "
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
make –j12
make install
8、安裝boost-1.55（1.56不兼容）
yum -y install libicu.x86_64 libicu-devel.x86_64 bzip2-libs.x86_64 bzip2-devel.x86_64
tar –xf boost_1_55_0.tar.gz && cd boost_1_55_0
./bootstrap.sh
./b2
./b2 install
運行./b2 install命令，默認安裝在/usr/local/lib目錄下，頭文件在/usr/local/include/boost目錄下
9、安裝caffe其他依賴：
yum -y install snappy.x86_64 snappy-devel.x86_64 hdf5.x86_64 hdf5-devel.x86_64 epel-release leveldb.x86_64 leveldb-devel.x86_64 libgfortran.x86_64
------ 編譯安裝protobuf-2.5.0 protobuf-2.5.0
------ tar -xvf protobuf-2.5.0.tar.gz
------ cd /usr/src/protobuf-2.5.0
./configure
make
make check
make install
------ export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
10、編譯安裝caffe其他依賴包
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錯誤，手動建立一個
mkdir -p /usr/local/man/man1
11、將matlab上傳至服務器，通過圖形方式安裝
安裝秘鑰12345-67890-12345-67890
安裝完成后導入lic文件，然后替換libmwservices.so到
/usr/local/MATLAB/R2014a/bin/glnxa64/進行覆蓋，結束安裝。
12、解決python依賴
pip install 'six>=1.3'
easy_install -U distribute
pip2.7 install PIL --allow-external PIL --allow-unverified PIL
解包caffe-master.zip，并將該包移至opt目錄
cd /opt/caffe-master/python
for i in $(cat requirements.txt); do pip install $i; done #需要多執行幾遍
注：會出現一個報錯，關于PIL.Image >= 1.1.7，則可使用命令pip install 'PIL' 進行安裝
后再次執行以上的for循環語句，需要將python升級至2.7以上版本（安裝及注意事項下：）
13、安裝caffe
修改caffe-master/Makefile.config文件，增加如下幾句
cp /opt/caffe-master/Makefile.config.example Makefile.config
vim Makefile.config
MATLAB_DIR := /usr/local/MATLAB/R2014a/
BLAS := atlas
BLAS_LIB := /usr/lib64/atlas
PYTHON_INCLUDE := /usr/include/python2.7 /
/usr/local/lib/python2.7/site-packages/numpy/core/include /
/usr/local/include/python2.7
執行 ldconfig
make all -j12
make –j12 pycaffe
make –j12 matcaffe
make test –j12
make runtest –j12
如果matlab要使用靜態編譯libprotobuf.a的話，修改Makefile
在MATLAB_CXXFLAGS項上添加-static參數即可
但使用動態庫的matlab模型可能不可用
若一切沒有問題，至此caffe環境安裝結束，待測試。
以下為可選部分
編譯安裝protobuf-2.5.0 protobuf-2.5.0
tar -xvf protobuf-2.5.0.tar.gz
cd /usr/src/protobuf-2.5.0
./configure
make
make check
make install
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
繼續安裝protobuf的python模塊(如果不用python，可跳過這一步)
#cd ./python
#python setup.py build
#python setup.py test
#python setup.py install
安裝cudnn
LINUX
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.
a. 編輯確保Makefile.config,啟用GPU “# CPU_ONLY := 1”,并設置 “USE_CUDNN := 1”。
b. 安裝cuDNN
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
注1：將相關的頭文件，庫文件放到profile中定義的系統路徑里即可，matlab的mex運行時需要加載對應庫
caffe編譯時也可在Makefile.config中修改，添加cuDNN的路徑/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顯卡時，需要在Makefile.config里改如下幾個地方：
PYTHON_LIB := /usr/lib64 #原為PYTHON_LIB := /usr/lib
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib64 /usr/lib64 #原為如下：
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
注3：protobuf手動安裝，不需要通過yum，yum安裝版本不對，make runtest會報錯，使用protobuf2.5的版本，安裝方式見上，在編譯caffe前安裝好后再進行編譯。
包：咖啡環境需要上傳的包：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文件夾
