ubuntu14.04+cuda7.5+caffe+cudnn7.5+anaconda+opencv 2.4.9系统整合（2016.12.3）

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.5.0.5sudo ln -sf libcudnn.so.5.0.5 libcudnn.so.5sudo ln -sf libcudnn.so.5 libcudnn.sosudo 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 OpenBlasBLAS := 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/includeLIBRARY_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 := buildDISTRIBUTE_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 runtest  

10、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：

pythonimport numpyimport 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-mastermake 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：

pythonimport numpyimport caffe

就可以看见caffe加载成功了