提前说明：在开始之前我已经安装了opencv2.4.9以及cuda7.5.opencv 有很多安装的博客可以参考没什么好说的，cuda7.5建议看我之前博客 ：ubuntu14.04+cuda7.5安装 官方步骤版，因为csdn中有不少关于cuda的安装，我之前也用了很简单的方法安装成功，但调用过程bug百出，中间重装了20+遍系统，所以还是按照官方步骤安装。
再研究deep learning中要用到很多python的库，需要我们搭建cuda与caffe等的链接，此文将详细介绍这一整套系统的安装过程方法
（接着我上一篇ubuntu14.04+cuda7.5安装 官方步骤版）
1、安装开发所需要的依赖包
sudo apt-get install build-essential # basic requirement sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe 2、下载安装cudnn注：不建议到官方下载安装，因为他需要审批时间，得1到两天，可以直接再csdn上下载，地址：http://download.csdn.net/detail/eagelangel/9617094
将压缩包解压
出来一个cuda文件夹，然后cd到该cuda/lib64文件夹
sudo cp lib* /usr/local/cuda/lib64/ 然后cd到另一个文件夹cuda/include sudo cp cudnn.h /usr/local/cuda/include/ 再依次执行，更新软连接： cd /usr/local/cuda/lib64/ sudo chmod +r libcudnn.so.5.0.5 sudo ln -sf libcudnn.so.5.0.5 libcudnn.so.5 sudo ln -sf libcudnn.so.5 libcudnn.so sudo ldconfig 3、设置环境变量（在profile中添加）在终端
sudo gedit /etc/profile 在文件的最下方加入（可有可无，最好还是加上吧，当时没加也成功了） PATH=/usr/local/cuda/bin:$PATH export PATH 再执行source，是刚才操作立即生效 source /etc/profile同时需要添加lib库路径： 在 /etc/ld.so.conf.d/建立文件 cuda.conf（方法为 sudo touch /etc/ld.so.conf.d/cuda.conf ）, 内容如下 /usr/local/cuda/lib64 保存后在终端执行以下语句，使其立即生效 sudo ldconfig 4、安装cuda sample进入/usr/local/cuda/samples, 执行下列命令来build samples
sudo make all -j4 整个过程大概10分钟左右, 全部编译完成后， 进入 samples/bin/x86_64/linux/release, 运行 ./deviceQuery如果看到以下信息即cuda已经cuda sample安装成功（注：我的显卡是GT 650M，不同显卡信息不同只要能看到很多信息即可）
5、安装Atlas
sudo apt-get install libatlas-base-dev 6、安装caffe所需要的python包建议安装Anaconda包，也可以用pip等安装方法，以下为Anaconda安装方式
Anaconda linux系统下载官网：https://www.continuum.io/downloads#all
下载download for linux 中的python2.7版本（建议）
下载好后cd到下载目录，运行：
bash Anaconda2-4.2.0-Linux-x86_64.sh （注：Anaconda包一直再更新，所以上面的命令版本号可能会变，具体参考官网上的下载命令）整个安装步骤一直选择默认（有一个选择为是否将Anaconda路径写如.bashrc文件，安装默认就是no，不要犹豫就是选no。。。如果选yes在之后的过程找不到caffe，血的教训。。）
7、添加Anaconda库路径
在/etc/ld.so.conf最后加入以下路径（具体方法 sudo gedit /etc/ld.so.conf） 注：username为你自己的计算机名字
/home/username/anaconda/lib 后在你自己的.bashrc文件中添加以下内容（注：username为你自己的计算机名字） export LD_LIBRARY_PATH="/home/username/anaconda/lib:$LD_LIBRARY_PATH" 8、下载caffe包（github）github网址 ： https://github.com/BVLC/caffe
建议解压到你的home文件夹下面
之后cd到caffe-master的python目录下面
执行命令
for req in $(cat requirements.txt); do pip install $req; done 就是按照 requirements.txt检查你目前的系统配置符不符合caffe要求，不符合自动为你升级到合适的版本9、编译caffe
进入caffe-master目录，复制一份Makefile.config.examples
cp Makefile.config.example Makefile.config 操作含义：本来caffe给的Makefile.config.example例子，复制到make操作需要的 Makefile.config中。下面需要修改该 Makefile.config中的以下参数具体为：
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome! # cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1 # CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1 # uncomment to disable IO dependencies and corresponding data layers # USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 0 # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) # You should not set this flag if you will be reading LMDBs with any # possibility of simultaneous read and write # ALLOW_LMDB_NOLOCK := 1 # Uncomment if you're using OpenCV 3 # OPENCV_VERSION := 3 # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ # CUDA directory contains bin/ and lib/ directories that we need. CUDA_DIR := /usr/local/cuda # On Ubuntu 14.04, if cuda tools are installed via # "sudo apt-get install nvidia-cuda-toolkit" then use this instead: # CUDA_DIR := /usr # CUDA architecture setting: going with all of them. # For CUDA < 6.0, comment the *_50 lines for compatibility. CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \ -gencode arch=compute_20,code=sm_21 \ -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=sm_50 \ -gencode arch=compute_50,code=compute_50 # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := atlas # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # Leave commented to accept the defaults for your choice of BLAS # (which should work)! # BLAS_INCLUDE := /path/to/your/blas # BLAS_LIB := /path/to/your/blas # Homebrew puts openblas in a directory that is not on the standard search path # BLAS_INCLUDE := $(shell brew --prefix openblas)/include # BLAS_LIB := $(shell brew --prefix openblas)/lib # This is required only if you will compile the matlab interface. # MATLAB directory should contain the mex binary in /bin. # MATLAB_DIR := /usr/local # MATLAB_DIR := /Applications/MATLAB_R2012b.app # NOTE: this is required only if you will compile the python interface. # We need to be able to find Python.h and numpy/arrayobject.h. #PYTHON_INCLUDE := /usr/include/python2.7 \ /usr/lib/python2.7/dist-packages/numpy/core/include # Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it's in root. ANACONDA_HOME := $(HOME)/anaconda2 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python2.7 \ $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \ # Uncomment to use Python 3 (default is Python 2) # PYTHON_LIBRARIES := boost_python3 python3.5m # PYTHON_INCLUDE := /usr/include/python3.5m \ # /usr/lib/python3.5/dist-packages/numpy/core/include # We need to be able to find libpythonX.X.so or .dylib. #PYTHON_LIB := /usr/lib PYTHON_LIB := $(ANACONDA_HOME)/lib # Homebrew installs numpy in a non standard path (keg only) # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include # PYTHON_LIB += $(shell brew --prefix numpy)/lib # Uncomment to support layers written in Python (will link against Python libs) # WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies # INCLUDE_DIRS += $(shell brew --prefix)/include # LIBRARY_DIRS += $(shell brew --prefix)/lib # Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @ 注意：①打开cudnn②ANACONDA_HOME为自己的anaconda文件路径，我的为/home/anaconda2再进行编译：
make all -j4 make test make runtest10、最后一步
make pycaffe 编译完成后测试： 用终端进入到caffe-master/python目录下，在终端输入： python import numpy import caffe 有可能会出现的问题： 1、import caffe error：can‘t find module skimage.io 这需要你重新编译以下caffe cd caffe-master make clean $ sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython $ sudo apt-get update make all -j4 make test make runtest make pycaffe 2.RuntimeError: module compiled against API version 0xa but this version of numpy is 0x9或者ImportError: numpy.core.multiarray failed to import 这个原因一般是caffe中的io.py作为关键字影响了python的运行环境,为什么这样说呢,因为安装这整套系统可能有的读者已经尝试的别人的方法,有的博客里可能要求读者添加caffe路径再.bashrc文件中,这有一个致命的错误,当你已经选定用anaconda或默认系统python,此时与caffe中的io.py有重定义.所以在之前的步骤也提了,一定不要把caffe路径放在.bashrc文件中.解决方法:查看你的.bashrc文件(操作在终端输入 sudo gedit .bashrc)里是否有你的caffe路径,如果有请注释掉注释完之后运行 source .bashrc 使得其立即生效 但是这里注意一点,由于该命令为立即生效,但我的ubuntu14.04该命令并不能立即生效.我的经验是source语句只有增添路径时候立即生效,撤销地址只能通过重启系统来生效 所以下一步就重启电脑 用终端进入到caffe-master/python目录下，在终端输入： python import numpy import caffe 就可以看见caffe加载成功了