- Caffe搭建手记_基于Ubuntu14.04LTS
我的硬件配置：
内存：4GB
处理器：i3-2120CPU@3.30GHz * 4
图形：GeForce GTX 960/PCIe/SSE2
操作系统：Ubuntu14.04LTS 64位
磁盘：100GB
Ubuntu14.04LTS安装
安装依赖库
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
CUDA Toolkit 7.5安装
CUDA Toolkit 7.5安装1.查看自己的显卡是否能安装CUDA Toolkit 7.5
$ lspci | grep -i nvidia
只要型号存在于https://developer.nvidia.com/cuda-gpus，就没问题了
2.下载 NVIDIA CUDA Toolkit7.5
下载地址：https://developer.nvidia.com/cuda-toolkit
验证地址：https://developer.nvidia.com/rdp/cuda-rc-checksums
我下载的是 cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb，关闭lightdm，以免和原来的驱动发生冲突：
$ sudo service lightdm stop
按下ctrl+alt+F2进入命令行模式，在命令行中输入：
$ cd /path/to/Downloads $ sudo dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb $ sudo apt-get update $ sudo apt-get install -y cuda
安装完成，重启，添加环境变量：
$ export PATH=/usr/local/cuda-7.5/bin:$PATH $ export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
3.安装NVIDIA cuDNN（可选，是用于深度神经网络的GPU加速库）
下载cuDNN，从https://developer.nvidia.com/rdp/cudnn-download，注册，然后下载，我下载的是cudnn-7.0-linux-x64-v4.0-prod.tgz:
$ sudo tar xvf cudnn-7.0-linux-x64-v4.0-prod.tgz $ cd cuda/include $ sudo cp *.h /usr/local/include/ $ cd ../lib64 $ sudo cp lib* /usr/local/lib/ $ cd /usr/local/lib $ sudo chmod +r libcudnn.so.4.0.7 $ sudo ln -sf libcudnn.so.4.0.7 libcudnn.so.4 $ sudo ln -sf libcudnn.so.4 libcudnn.so $ sudo ldconfig #这里有报错：/sbin/ldconfig.real: /usr/local/lib/libcudnn.so.4 is not a symbolic link但是看来不影响
设置环境变量，添加CUDA环境变量：
$ echo 'export PATH=/usr/local/cuda/bin:$PATH'>> ~/.bashrc $ echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
保存后，使环境变量立即生效，
$ sudo -s $ source ~/.bashrc
进入/usr/local/cuda/samples，执行下面的命令来build samples:
$ sudo make all -j4
全部编译完成后，进入 samples/bin/x86_64/linux/release，运行deviceQuery:
$ ./deviceQuery
![安装cuDNN效果图](/uploadfile/Collfiles/20160905/20160905095852433.png "\")
MATLAB R2014a安装
1.挂载iso（需新建matlab_iso文件夹）：
$ sudo mount -o loop MATHWORKS_R2014A.iso ～/matlab_iso
2.开始安装：
$ cd ~/matlab_iso
$ sudo ./install
3.选择”install manually without using the internet”项进行安装
4.输入”file installation key”:12345-67890-12345-67890（随便都行）
5.激活：选择”license_405329_R2014a.lic”文件进行激活（在Crack文件夹下面）
6.将libmwservices.so复制到/usr/local/MATLAB/R2014a/bin/glnxa64中：
$ sudo cp libmwservices.so /usr/local/MATLAB/R2014a/bin/glnxa64/libmwservices.so
7.验证：
$ matlab
若不报错就是安装成功
解决编译器gcc/g++版本问题
解决编译器gcc/g++版本问题因为Ubuntu 14.04的gcc/g++版本是4.8.4，而Matlab R2014a（2015a）的版本是4.7.x所以在使用matla调用mex文件的时候，基本上都会报错，根据报错信息，考虑如下两步解决方案。
1.下载gcc/g++ 4.7.x
$ sudo apt-get install -y gcc-4.7
$ sudo apt-get install -y g++-4.7
2.链接gcc/g++实现降级
$ cd /usr/bin
$ sudo rm gcc
$ sudo ln -s gcc-4.7 gcc
$ sudo rm g++
$ sudo ln -s g++-4.7 g++
降级成功！
安装OpenCV3.0
安装OpenCV3.0安装OpenBLAS
安装OpenBLAS1.下载OpenBLAS:
https://github.com/xianyi/OpenBLAS/archive/v0.2.19.tar.gz
2.安装：
$ mkdir ~/OpenBLAS
$ cd ~/OpenBLAS
$ tar zxvf OpenBLAS-0.2.19.tar.gz
$ make
$ make all #默认安装在opt/OpenBLAS
安装Anaconda python
安装Anaconda python1.下载Anaconda
https://repo.continuum.io/archive/Anaconda2-4.1.1-Linux-x86_64.sh
2.安装：
$ bash ~/Downloads/Anaconda3-4.0.0-Linux-x86_64.sh
3.添加环境变量，不用每次都要手动输入
3.1.修改profile文件：
$ sudo -s
#vi /etc/profile
在里面最后一行加入:
export PATH=/home/username/anaconda2/bin:$PATH
让环境变量立即生效需要执行如下命令：
#source /etc/profile
3.2修改.bashrc文件：
# vi /root/.bashrc
在里面最后一行加入：
export PATH=/home/username/anaconda2/bin:$PATH
重新注销系统才能生效
编译caffe
编译caffe1.下载caffe-master包，解压重命名为caffe，我放在/home目录下
git@github.com:BVLC/caffe.git
2.进入caffe目录，编译主程序
$ cp Makefile.config.example Makefile.config
$ sudo gedit Makefile.config
参数说明
CPU_ONLY 是否只使用CPU模式，没有GPU没安装CUDA的同学可以打开这个选项 BLAS (使用intel mkl、atlas还是OpenBLAS)我用的是优化后的BLAS，即OpenBLAS，所以 BLAS=：open 并加入库文件路径 MATLAB_DIR 如果需要使用MATLAB wrapper的同学需要指定matlab的安装路径, 如我的路径为 /usr/local/MATLAB/R2014a (注意该目录下需要包含bin文件夹，bin文件夹里应该包含mex二进制程序) 我的Makefile.config配置如下：
## Refer to https://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 := open
# 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/OpenBLAS/include
BLAS_LIB := /opt/OpenBLAS/lib
# 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/R2014a
#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 ?= @
$ make all -j4 #使用多核加速，我的是四核
$ make test
$ make runtest
出现报错信息:
error while loading shared libraries: libhdf5_hl.so.10: cannot open shared object file: No such file or directory
凡是这种情况,如果报错文件的确是存在,就把文件所在的路径加入环境变量,在~./bashrc添加文件所在路径,这是我成功编译后的bashrc文件添加的路径:
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/home/dawin/anaconda2/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/home/dawin/OpenBLAS:$LD_LIBRARY_PATH
再make runtest,效果如图:
![caffe安装成功效果图](/uploadfile/Collfiles/20160905/20160905095853435.png "\")
3.编译Matlab wrapper
$ make matcaffe
然后就可以跑官方的matlab demo!
4.编译Python wrapper
$ make pycaffe
以后也可以跑官方的demo
5.验证python接口
$ python
在python命令环境下输入import caffe,出现报错:
ImportError: No module named google.protobuf.internal
这是因为有两个python路径在变量环境中,要给anaconda中python和系统中的python安装protobuf,退出python,输入以下命令:
$ sudo pip install protobuf
$ sudo /home/dawin/anaconda2/bin/pip install protobuf
再次在python命令环境下输入import caffe,不报错就成功了!
后记
后记安装caffe真是一个大工程,我从windows里面安装caffe,再到ubuntu中安装花了半个月,接着试图测试fast-rcnn和faster-rcnn都没成功,最后测试rcnn成功,可是前几天手贱输入nvcc,提示没有安装nvcc,就重新安装nvcc,导致显卡冲突并且不能重新安装nvidia的显卡,只能被逼着重装ubuntu,道路相当曲折,在此过程中感谢我的两位导师–贺博和周博,没有他们的指导我很难继续下去,希望这是我做神经网络相关课题的良好开端
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