前记
由于是在一台用了很久的机器上安装caffe,过程比较复杂,网上说再干净的机器上装比较简单。如果能有干净的机器,就不用再过这么多坑了,希望大家好运!介绍这里就不说了,直接进入正题:
Caffe 主页 http://caffe.berkeleyvision.org/
github主页 https://github.com/BVLC/caffe
机器配置:
[root@cdh-nn-182 build]# lsb_release -a
LSB Version: :base-4.0-amd64:base-4.0-noarch:core-4.0-amd64:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-noarch
Distributor ID: RedHatEnterpriseServer
Description: Red Hat Enterprise Linux Server release 6.3 (Santiago)
Release: 6.3 gcc 版本 4.4.7 20120313 (Red Hat 4.4.7-16) (GCC) Python 2.7.10
python已安装numpy,没有GPU
-
CUDA is required for GPU mode.
- library version 7.0 and the latest driver version are recommended, but 6.* is fine too
- 5.5, and 5.0 are compatible but considered legacy
- BLAS via ATLAS, MKL, or OpenBLAS.
- Boost >= 1.55
- OpenCV >= 2.4 including 3.0
-
protobuf
,glog
,gflags
- IO libraries
hdf5
,leveldb
,snappy
,lmdb
Pycaffe and Matcaffe interfaces have their own natural needs.
- For Python Caffe:
Python 2.7
orPython 3.3+
,numpy (>= 1.7)
, boost-providedboost.python
- For MATLAB Caffe: MATLAB with the
mex
compiler.
1. 安装各种依赖包
yum install -y gcc gcc-c++ gtk+-devel libjpeg-devel libtiff-devel jasper-devel libpng-devel zlib-devel cmake
yum install git gtk2-devel pkgconfig numpy python python-pip python-devel gstreamer-plugins-base-devel libv4l ffmpeg-devel mplayer mencoder flvtool2
yum install libdc1394 libdc1394-devel.x86_64
yum install gtk*
2. python包安装
下载Caffe源码,按照./caffe/caffe-master/python/requirements.txt 安装所需要的包,用pip安装比较方便,不行就自己下载手动安装,没什么问题。
3. 安装protobuf
, glog
, gflags
先从比较简单的来:
4. 安装CUDA
从nvidia网站上下载最新的CUDA7.5,按自己的操作系统进行选择,这里选择下载cuda_7.5.18_linux.run,直接运行:
./cuda_6.5.14_linux_64.run
运行后会出现选择安装的项目,选择不安装驱动,否则会出错(driver installation is unable to locate the kernel source),也就是第一个选项No
5. 安装OpenBLAS
ATLAS, MKL, or OpenBLAS都可以安装,以前用过OpenBLAS,这次就还装他吧
下载OpenBLAS源码,安装也很简单,make && make install即可,更多请参考OpenBLAS编译和安装简介
6. 安装OpenCV
OpenCV装起来比较麻烦,中间遇到了很多问题,参考安装文档,也可以参考网上很多人给的 自动安装配置脚本,由于我安装时出了很多问题,所以基本是自己手动装的。
首先将自己的CMake升级到最新版本,yum默认装的默认不行,只能手动升级了,否则在CMake阶段就会出现各种警告什么的。
unzip opencv-3.0.0.zip
cd opencv-3.0.0
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. ##如果不出问题
make -j 32
sudo make install
sudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig
下面说说我在make的时候碰到的问题:
Q1:已经安装了ffmpeg,出现错误AVCodecID未声明
cap_ffmpeg_impl.hpp:1556:83:错误:使用枚举‘AVCodecID’前没有给出声明
A1: 解决的方法是,添加make参数 -D WITH_FFMPEG=OFF,参考
Q2:出现parallel_for_pthreads undefined reference 错误,貌似是只有在CentOs中才会出现的
A2: 需要更改modules/core/src/parallel.cpp文件,参考1,参考2,我这里只按照参考2给了parallel.cpp文件
Q3: 出现undefined reference to `jpeg_default_qtables'
../../../lib/libopencv_imgcodecs.so.3.0.0: undefined reference to `jpeg_default_qtables'
A3:安装,jpegsrc.v9a.tar.gz, 参考1, 参考2, 参考3
tar -xzvf jpegsrc.v9.tar.gz
cd jpeg-9
./configure
make libdir=/usr/lib64
make libdir=/usr/lib64 install
Q4:编译已完成,但是还是有问题:
[100%] Linking CXX shared library ../../lib/cv2.so
/usr/bin/ld: /usr/local/lib/libpython2.7.a(abstract.o): relocation R_X86_64_32 against `.rodata.str1.8' can not be used when making a shared object; recompile with -fPIC
/usr/local/lib/libpython2.7.a: could not read symbols: Bad value
collect2: ld 返回 1
make[2]: *** [lib/cv2.so] 错误 1
make[1]: *** [modules/python2/CMakeFiles/opencv_python2.dir/all] 错误 2
make: *** [all] 错误 2
A4:重新编译安装python,configure时添加--enable-shared,参考
./configure --enable-shared
make
make install
重新安装完以后可能会出现,执行python时error while loading shared libraries: libpython2.7.so.1.0: cannot open shared object file: No such file or directory,解决方法是:
vi /etc/ld.so.conf
#如果是非root权限帐号登录,使用 sudo vi /etc/ld.so.conf
#添加上python2.7的lib库地址,如我的/usr/local/Python2.7/lib,保存文件 /sbin/ldconfig
7. 安装Caffe
如果以上安装没有什么问题,这一不应该不会出错
unzip caffe-master.zip
cd caffe-master
cp Makefile.config.example Makefile.config vim Makefile.config
# 按照实际情况修改配置
CPU_ONLY := 1
BLAS := open make all
8. 运行MINIST例子
cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh vim ./examples/mnist/lenet_solver.prototxtsolver_mode: CPU
./examples/mnist/train_lenet.sh
就可以运行了
I0916 17:43:44.016604 63362 solver.cpp:571] Iteration 9900, lr = 0.00596843
I0916 17:44:05.355252 63362 solver.cpp:449] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I0916 17:44:05.371235 63362 solver.cpp:734] Snapshotting solver state to binary proto fileexamples/mnist/lenet_iter_10000.solverstate
I0916 17:44:05.464294 63362 solver.cpp:326] Iteration 10000, loss = 0.00184362
I0916 17:44:05.464337 63362 solver.cpp:346] Iteration 10000, Testing net (#0)
I0916 17:44:11.869861 63362 solver.cpp:414] Test net output #0: accuracy = 0.9907
I0916 17:44:11.869920 63362 solver.cpp:414] Test net output #1: loss = 0.0280591 (* 1 = 0.0280591 loss)
I0916 17:44:11.869931 63362 solver.cpp:331] Optimization Done.
I0916 17:44:11.869940 63362 caffe.cpp:214] Optimization Done.