Ubuntu14.04搭建Caffe(仅CPU）详解教程

这篇文章主要介绍了Ubuntu14.04在系统平台（未使用anaconda环境管理）搭建CPU版Caffe，无GPU，详解教程,操作系统是Ubuntu 14.04，本文分步骤给大家介绍的非常详细，具有参考借鉴价值，需要的朋友可以参考下.

操作系统:  Ubuntu  14.04

PYTHON版本：2.7，使用python3会有各种包依赖问题

是否使用PYTHON API: 是, 目标是安装后CAFFE能作为PYTHON MODULE来使用

硬件: 云服务器, 只使用CPU模式

本文主要参考大牛的博客https://www.cnblogs.com/xuanxufeng/p/6016945.html 然后填了一些自己安装过程中遇到的坑。

1.安装依赖

1 sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf- compiler

2 sudo apt-get install --no- install -recommends libboost-all- dev

3 sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb- dev

4 sudo apt-get install libatlas-base-dev

5 sudo apt-get install libopencv-dev

PYTHON需要2.7版本,这是操作系统本身已经安装好的. 输入python2.7 --version 会显示具体的版本号说明安装了.

但是还需要sudo apt-get install python-dev

2.下载Caffe 使用Git直接下载Caffe非常简单，或者去https://github.com/BVLC/caffe下载。由于我习惯去github上找代码，所以就直接去下载的源码。

使用git下载caffe到家目录，下载完成后，会在家目录下的下载里找到caffe文件夹，切换到caffe文件夹中。

$ cd ~

$ git clone git://github.com/BVLC/caffe.git

$ cd caffe

3.编译Caffe （1）创建Makefile.comfig文件

cp Makefile.config.example Makefile.config

（2）修改配置文件Makefile.config

 style="font-size:14px;"># CPU-only switch (uncomment to build without GPU support).
 CPU_ONLY := 1        #一定需要打开。
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3                    #用的是2.4版本不需要打开。

# 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++                                               #已经更新到至少4.8，选择默认的就好。

# CUDA directory contains bin/ and lib/ directories that we need.    #这是GPU用到的，注释掉就可以。
#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.        #这些部分全部注释，和我们cpu家族没有关系。
#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                      #如果安装mkl并且设置好路径的可以试一下mkl，不过一般会出错，找不到lmkl文件，建议采用atlas，简单易行。
# 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.     #python很流行，matlab暂时没安装，也就用不到了。
# 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 \  #因为下安装的时候采用的时2.7版本的python，所以这里打开2.7版本的，但是要确保python安装在系统默认路径下。
       /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  /usr/include/hdf5/serial    #增加hdf5路径
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib  /usr/lib/x86_64-linux-gnu/hdf5/serial
# 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

BUILD_DIR := build                            #编译分配用到的路径，打开即可。
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1                       #挑错模式，喜欢debug的朋友不妨试试。

（3）编译 Caffe

make pycaffe -jX                  #为了提高编译速度，这里的x设成自己的cpu核数
make all -jX
make test -jX

make  runtest -jX                 #注意，这里也可直接运行测试，make runtest

注意：这里如果使用MAC OS系统终端登陆服务器配置的话在runtest的时候会有一些Field，原因是字符集不匹配，在终端执行以下命令再运行runtest就好.

export LC_ALL="C"

如果上面4行某一行报错之后想要重试,建议先make clean再重新开始。

4.编译Python接口 Caffe拥有python\C++\shell接口，在Caffe使用python特别方便，在实例中都有接口的说明。

• 确保pip已经安装

sudo apt-get install python-pip

• 执行安装依赖

在caffe根目录的python文件夹下，有一个requirements.txt的清单文件，上面列出了需要的依赖库，按照这个清单安装就可以了。

在安装scipy库的时候，需要fortran编译器（gfortran)，如果没有这个编译器就会报错，因此，我们可以先安装一下。

首先回到caffe的根目录，然后执行安装代码：

cd ~/ caffe

sudo add -apt -repository ppa:kirillshkrogalev/ffmpeg -next

sudo apt -get update

sudo apt-get install gfortran

cd ./python

for req in $( cat requirements.txt); do pip install $req; done

安装完成以后，再次回到caffe根目录我们可以执行：

sudo pip install -r python/requirements.txt

就会看到，安装成功的，都会显示Requirement already satisfied, 没有安装成功的，会继续安装。

• 运行python结构

$ python2. 7

Python 2.7. 12 (default, Jul 1 2016, 15: 12: 24 )

[GCC 5.4. 0 20160609 ] on linux2

Type " help ", " copyright ", " credits " or " license " for more information.

>>> import caffe

>>>

如果没有报错，说明caffe安装全部完成

注意：如果import caffe提示找不到caffe，需要将 PYTHONPATH加入.bashrc中：

1. vim ~/.bashrc
2. 在文件末端添加下面两句
3. #set PYTHONPATH                        设置caffe下的路径。否则caffe也找不到。  
4. export PYTHONPATH="/home/tom/caffe/python:$PYTHONPATH"  标红的地方换成自己的caffe路径

加完后执行：

source ~/.bashrc

5.在Mnist运行Lenet

• 获取数据源

./data/mnist/get_mnist. sh

./examples/mnist/create_mnist. sh

• 因为是CPU运行，所以修改在examples文件下的Mnist下的lenet_solver.prototxt中的solver_mode:CPU

solver_mode: CPU

• 训练模型

./examples/mnist/train_lenet.sh

6.最后，我的文章是基于各位前辈大神们的文章，虽然按他们的过程走我都报错了，但是最终还是帮助我安装成功。为了表示对别人成果的尊重，这里留下大神们的参考链接：

http://www.linuxidc.com/Linux/2016-09/135034.htm

http://blog.csdn.net/u010402483/article/details/51506616

http://www.cnblogs.com/denny402/p/5679037.html

http://blog.csdn.net/u012029332/article/details/51098248

http://blog.csdn.net/mandagod/article/details/52337434