Ubuntu 14.04上安装caffe

lcbwlx

于 2015-04-22 09:41:47 发布

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本文链接：https://blog.youkuaiyun.com/lcbwlx/article/details/45190639

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== 前言 ==
本教程指导大家如何在Ubuntu 14.04上安装caffe。

== 前期准备 ==
为了安装caffe，你需要准备如下东西， 1台安装了ubuntu 14.04的电脑， 1块nvidia显卡（当然，你可以只用cpu跑caffe，但时间会让你抓狂）， cuda安装包。以上为必备选项。还有一些可选选项，大家可以不安装，但安装他们会提高caffe的性能，包括intel mkl库（数学运算库，需注册intel）， cudnn（需注册nvidia开发者并通过审核）。

== 安装gcc， g++， gfortran ==
有了这些必须软件之后，我们就可以一步一步安装caffe了。不过在那之前，请先安装c++编译器， ubuntu下一般采用gcc。 
<syntaxhighlight lang="bash">
sudo apt-get install build-essential # 安装gcc编译器
</syntaxhighlight>

== 安装cuda ==
请参靠网上的文章

== 安装intel mkl（可选） ==
caffe需要用到blas库，有三个选择都可以满足caffe，分别是intel mkl， atlas以及openblas。 
三者之中，推荐安装intel mkl，因为我认为blas的速度中应该是intel mkl最快。 
 
下载intel mkl去[https://software.intel.com/en-us/qualify-for-free-software/ 这里]，大家选择学生，然后用邮箱申请一下即可。 intel会给你发一封带有邮箱和验证码的邮件。 
 
下载好之后，解压，然后安装
<syntaxhighlight lang="bash">
sudo sh install_GUI.sh # sudo 会采用root安装，安装在/opt/intel中
</syntaxhighlight>
增加intel的lib路径
<syntaxhighlight lang="bash">
sudo gedit /etc/ld.so.conf.d/intel_mkl.conf
</syntaxhighlight>
在文件中添加内容
<syntaxhighlight lang="bash">
/opt/intel/lib
/opt/intel/mkl/lib/intel64
</syntaxhighlight>
最后使命令生效
<syntaxhighlight lang="bash">
sudo ldconfig
</syntaxhighlight>

== 安装atlas ==
这一步是为了没有安装intel mkl的同学准备的，如果安装intel mkl，请跳过。 
如果你觉得intel mkl太麻烦了，你也可以采用简单的做法，安装atlas。 
你只需要敲入一行命令即可。
<syntaxhighlight lang="bash">
sudo apt-get install libatlas-base-dev
</syntaxhighlight>

== 安装opencv ==
请参靠网上的文章

== 安装其他依赖项 ==
caffe需要一些数据库的操作，因此需要安装leveldb， lmdb， hdf5等库，此外， caffe采用了google的一些库，比如protobuf以及glog，这些也需要安装。
<syntaxhighlight lang="bash">
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
</syntaxhighlight>

== 安装python ==
caffe的可视化接口是用python写的，另外做测试的时候也需要用到python代码。因此推荐安装。 
安装之前，请参靠网上的文章，用virtualenv新建一个python环境，否则可能会搞乱！ 
 
首先，采用你新建的python环境。假设你的python环境名就是cp
<syntaxhighlight lang="bash">
source path_to_cp/bin/active
</syntaxhighlight>
其次，进入caffe安装目录中的python文件夹，你会看到一个requirements.txt，首先，修改protobuf那一行为<code>protobuf==2.5.0</code>, 然后采用如下命令：
<syntaxhighlight lang="bash">
for req in $(cat requirements.txt); do pip install $req; done
</syntaxhighlight>
可能一次运行有些选项安装时会报错，不过可以多运行几次命令，直到所有选项都被安装。 
 
另注， anaconda包不推荐安装，因为安装之后进不去桌面。anaconda包只是一个自带了requirements.txt中所有选项的python集合库，将上面的库安装好之后，不装anaconda没有任何影响。

== 安装matlab ==
大家下载ubuntu版本的matlab安装即可。

== 更改caffe config==
终于到这一步了。首先，复制一份config以防写错。
<syntaxhighlight lang="bash">
cp Makefile.config.example Makefile.config
</syntaxhighlight>
其次，大家可以参考我的配置。
<syntaxhighlight lang="bash">
## 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

# 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 := mkl
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /opt/intel/mkl/include
BLAS_LIB := /opt/intel/mkl/lib/intel64

# 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/R2013a
# 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 := /home/wenzheng/caffepython/include/python2.7 \
 /home/wenzheng/caffepython/lib/python2.7/site-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := /home/wenzheng/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 # $(ANACONDA_HOME)/include/python2.7 \
 # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib/x86_64-linux-gnu
# PYTHON_LIB := $(ANACONDA_HOME)/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

# 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
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 ?= @
</syntaxhighlight>
最后，大家使用
<syntaxhighlight lang="bash">
make all -j20 # 20线程编译
make alltest -j20 # 编译测试
make runtest # 运行测试
make pycaffe # 编译caffe python接口
make matcaffe # 编译 matlab接口
</syntaxhighlight>

== 结束 ==

我写的大部分参考自

http://www.cnblogs.com/platero/p/3993877.html

https://wiki.qut.edu.au/display/cyphy/Caffe+Installation+on+Ubuntu+12.04

谢谢大家