在centos6.X上安装caffe

0.编译安装gcc4.8.5

由于centos6.x中的gcc版本老旧，不支持c++11所以要安装gcc4.8.5，以下是安装教程。参考CentOS 6.4 编译安装 gcc-4.8.0

解压安装包进入目录执行download_prerequisites脚本./contrib/download_prerequisites

新建buildmkdir build

进入build目录执行

  1. ../configure -enable-checking=release -enable-languages=c,c++ -disable-multilib(生成Makefile文件)

修改Makefile文件中prefix=安装路径，这里的安装路径是/home/guanjun/caffe_lib/third/gcc-4.8.5

注意本文以下的安装路径都是/home/guanjun/caffe_lib/third下的对应目录

  1. make -j32
  2. make install

安装完成后要将gcc4.8.5中bin目录添加到环境变量（临时创建env_caffe.sh）

在env_caffe.sh中添加

  1. export PATH=/home/guanjun/caffe_lib/third/gcc-4.8.5/bin:$PATH

1.安装Anaconda python 环境

执行安装文件

./Anaconda2-4.2.0-Linux-x86_64.sh

注意在提示的最后的选项选no即不添加到.bashrc

之后同样在env_caffe.sh中添加export PATH=/home/guanjun/anaconda2/bin:$PATH

之后执行下面的命令

source ~/env_caffe.sh

因为编译boost时会用到python环境

2.编译安装boost

解压安装包然后执行

  1. ./bootstrap.sh
  2. ./b2 install --prefix=安装路径

参考boost Installation

3.编译安装opencv

解压安装包然后进入安装包执行

  1. mkdir build
  2. cd build
  3. ccmake ../

按照提示加载配置文件(按c)、修改cmake_install_prefix路径为安装路径、

将WITH CUDA WITH CUFFT WITH JASPER分别设置为off，按照提示保存退出(按c 按g)，然后执行

  1. make -j32
  2. make install

4.编译安装glog

解压然后依次执行

  1. ./configure --prefix=安装路径
  2. make -j32
  3. make intstall

5.编译安装gflags

解压然后进入解压文件依次执行

  1. mkdir build
  2. cd build
  3. export CXXFLAGS="-fPIC"
  4. ccmake ../

按c加载配置文件、设置安装路径按c g退出，之后执行

  1. make -j32
  2. make install

6.编译安装lmdb

首先下载lmdb安装包执行git clone https://github.com/LMDB/lmdb

打开lmdb中MakeFile文件、修改安装路径

  1. make -j
  2. make install

7.安装openblas

下载新版OpenBLASgit clone https://github.com/xianyi/OpenBLAS

进入OpenBLAS打开目录中cpuid.h文件在倒数第二行添加#define NO_AVX2 1024然后执行

  1. make -j32
  2. make install PREFIX=安装路径

8.编译安装hdf5

解压、进入文件执行

  1. ./configure --prefix=安装路径
  2. make -j32
  3. make install

9.编译安装protobuf

解压、进入文件执行

  1. ./configure --prefix=安装路径
  2. make -j32
  3. make install

之后将protobuf添加到环境变量中(env_caffe.sh)export PATH=/home/guanjun/caffe_lib/third/protobuf/bin:$PATH

在编译caffe前确保env_caffe.sh文件如下

  1. export PATH=/home/guanjun/caffe_lib/third/gcc-4.8.5/bin:/home/guanjun/anaconda2/bin:/home/guanjun/caffe_lib/third/protobuf/bin:$PATH
  2. export PYTHONPATH=/home/guanjun/caffe/py-R-FCN/caffe/python:$PYTHONPATH
  3. export LD_LIBRARY_PATH=/home/guanjun/caffe_lib/third_source/leveldb/out-shared:/home/guanjun/anaconda2/lib:/usr/local/cuda/lib64:/home/guanjun/caffe_lib/third/boost/lib:/home/guanjun/caffe_lib/third/hdf5/lib:/home/guanjun/caffe_lib/third/lmdb/lib:/home/guanjun/caffe_lib/third/openblas_v1/lib:/home/guanjun/caffe_lib/third/opencv/lib:/home/guanjun/caffe_lib/third/protobuf/lib:/home/guanjun/caffe_lib/third/glog/lib:/home/guanjun/caffe_lib/third/gflags/lib:/home/guanjun/caffe_lib/third/glibc-2.14/lib:/home/guanjun/caffe_lib/third/gcc-4.8.5/lib64:$LD_LIBRARY_PATH

/home/guanjun/替换成/home/你的用户名/

同时，保证caffe中的Makefile.config和下面的配置文件一样

  1. # Refer to http://caffe.berkeleyvision.org/installation.html
  2. # Contributions simplifying and improving our build system are welcome!
  4. # cuDNN acceleration switch (uncomment to build with cuDNN).
  5. USE_CUDNN := 1
  6. D_PATH := /home/guanjun/caffe_lib/third
  7. # CPU-only switch (uncomment to build without GPU support).
  8. #CPU_ONLY := 1
  10. # uncomment to disable IO dependencies and corresponding data layers
  11. # USE_OPENCV := 0
  12. USE_LEVELDB := 0
  13. # USE_LMDB := 0
  15. # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
  16. #	You should not set this flag if you will be reading LMDBs with any
  17. #	possibility of simultaneous read and write
  18. # ALLOW_LMDB_NOLOCK := 1
  20. # Uncomment if you're using OpenCV 3
  21. # OPENCV_VERSION := 3
  23. # To customize your choice of compiler, uncomment and set the following.
  24. # N.B. the default for Linux is g++ and the default for OSX is clang++
  25. # CUSTOM_CXX := g++
  27. # CUDA directory contains bin/ and lib/ directories that we need.
  28. CUDA_DIR := /usr/local/cuda
  29. # On Ubuntu 14.04, if cuda tools are installed via
  30. # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
  31. # CUDA_DIR := /usr
  33. # CUDA architecture setting: going with all of them.
  34. # For CUDA < 6.0, comment the *_50 lines for compatibility.
  35. #CUDA_ARCH := -gencode arch=compute_20,code=sm_20
  36. #		-gencode arch=compute_20,code=sm_21
  37. CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
  38. 	     -gencode arch=compute_35,code=sm_35 \
  39.              -gencode arch=compute_50,code=sm_50 \
  40. 	     -gencode arch=compute_50,code=compute_50
  42. # BLAS choice:
  43. # atlas for ATLAS (default)
  44. # mkl for MKL
  45. # open for OpenBlas
  46. # BLAS := atlas
  47. BLAS := open
  48. # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
  49. # Leave commented to accept the defaults for your choice of BLAS
  50. # (which should work)!
  51. BLAS_INCLUDE := /home/guanjun/caffe_lib/third/openblas_v1/include
  52. BLAS_LIB := /home/guanjun/caffe_lib/third/openblas_v1/lib
  54. # Homebrew puts openblas in a directory that is not on the standard search path
  55. # BLAS_INCLUDE := $(shell brew --prefix openblas)/include
  56. # BLAS_LIB := $(shell brew --prefix openblas)/lib
  58. # This is required only if you will compile the matlab interface.
  59. # MATLAB directory should contain the mex binary in /bin.
  60. # MATLAB_DIR := /usr/local
  61. # MATLAB_DIR := /Applications/MATLAB_R2012b.app
  63. # NOTE: this is required only if you will compile the python interface.
  64. # We need to be able to find Python.h and numpy/arrayobject.h.
  65. #PYTHON_INCLUDE := /usr/include/python2.7 \
  66. 		/usr/lib/python2.7/dist-packages/numpy/core/include
  67. # Anaconda Python distribution is quite popular. Include path:
  68. # Verify anaconda location, sometimes it's in root.
  69. ANACONDA_HOME := /home/guanjun/anaconda2
  70. PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
  71. 		$(ANACONDA_HOME)/include/python2.7 \
  72. 		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
  74. # Uncomment to use Python 3 (default is Python 2)
  75. # PYTHON_LIBRARIES := boost_python3 python3.5m
  76. # PYTHON_INCLUDE := /usr/include/python3.5m \
  77. #                 /usr/lib/python3.5/dist-packages/numpy/core/include
  79. # We need to be able to find libpythonX.X.so or .dylib.
  80. #PYTHON_LIB := /usr/lib
  81. PYTHON_LIB := $(ANACONDA_HOME)/lib
  83. # Homebrew installs numpy in a non standard path (keg only)
  84. # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
  85. # PYTHON_LIB += $(shell brew --prefix numpy)/lib
  87. # Uncomment to support layers written in Python (will link against Python libs)
  88. WITH_PYTHON_LAYER := 1
  90. # Whatever else you find you need goes here.
  92. INCLUDE_DIRS := $(D_PATH)/protobuf/include
  93. #INCLUDE_DIRS := /data/shiyang/anaconda2/include
  94. INCLUDE_DIRS += $(D_PATH)/hdf5/include
  95. INCLUDE_DIRS += $(D_PATH)/gflags/include
  96. INCLUDE_DIRS += $(D_PATH)/glog/include
  97. INCLUDE_DIRS += $(D_PATH)/opencv/include
  98. INCLUDE_DIRS += $(D_PATH)/boost/include
  99. INCLUDE_DIRS += $(D_PATH)/lmdb/include
  100. INCLUDE_DIRS += $(D_PATH)/glibc-2.14/include
  101. INCLUDE_DIRS += $(D_PATH)/gcc-4.8.5/include
  102. INCLUDE_DIRS += /home/guanjun/caffe_lib/third_source/leveldb/include
  104. LIBRARY_DIRS := $(D_PATH)/protobuf/lib
  105. #LIBRARY_DIRS := /data/shiyang/anaconda2/lib
  106. LIBRARY_DIRS += $(D_PATH)/hdf5/lib
  107. LIBRARY_DIRS += $(D_PATH)/gflags/lib
  108. LIBRARY_DIRS += $(D_PATH)/glog/lib
  109. LIBRARY_DIRS += $(D_PATH)/opencv/lib
  110. LIBRARY_DIRS += $(D_PATH)/boost/lib
  111. LIBRARY_DIRS += $(D_PATH)/lmdb/lib
  112. LIBRARY_DIRS += $(D_PATH)/glibc-2.14/lib
  113. LIBRARY_DIRS += $(D_PATH)/gcc-4.8.5/lib64
  114. LIBRARY_DIRS += /home/guanjun/caffe_lib/third_source/leveldb/out-shared
  116. INCLUDE_DIRS += $(PYTHON_INCLUDE) /usr/local/include
  117. LIBRARY_DIRS += $(PYTHON_LIB) /usr/local/lib /usr/lib
  118. # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
  119. # INCLUDE_DIRS += $(shell brew --prefix)/include
  120. # LIBRARY_DIRS += $(shell brew --prefix)/lib
  122. # Uncomment to use `pkg-config` to specify OpenCV library paths.
  123. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
  124. # USE_PKG_CONFIG := 1
  126. # N.B. both build and distribute dirs are cleared on `make clean`
  127. BUILD_DIR := build
  128. DISTRIBUTE_DIR := distribute
  130. # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
  131. #DEBUG := 1
  133. # The ID of the GPU that 'make runtest' will use to run unit tests.
  134. TEST_GPUID := 0
  136. # enable pretty build (comment to see full commands)
  137. #Q ?= @

之后执行source ~/env_caffe.sh

进入caffe目录执行

  1. make -j32
  2. make runtest
  3. make pycaffe

将caffe的python添加到环境变量export PYTHONPATH=/home/guanjun/caffe/py-R-FCN/caffe/python:$PYTHONPATH就是env_caffe.sh中的第二行。

新建一个python文件测试import caffe是否可用。

在本地Ubuntu16.04上安装caffe

1.安装cuda

先把错配的显卡驱动清理干净

sudo apt-get --purge remove nvidia-*

到https://developer.nvidia.com/cuda-downloads下载对应的deb文件(cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb)

到deb的下载目录下

  1. sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
  2. sudo apt-get update
  3. sudo apt-get install cuda
  4. sudo reboot

参考ubuntu 14.04 现在安装cuda7.5超级简便，惊了

2.安装caffe

安装依赖

  1. sudo apt-get install -y opencl-headers build-essential protobuf-compiler \
  2.     libprotoc-dev libboost-all-dev libleveldb-dev hdf5-tools libhdf5-serial-dev \
  3.     libopencv-core-dev  libopencv-highgui-dev libsnappy-dev \
  4.     libatlas-base-dev cmake libstdc++6-4.8-dbg libgoogle-glog0v5 libgoogle-glog-dev \
  5.     libgflags-dev liblmdb-dev git python-pip gfortran libopencv-dev
  6. sudo apt-get clean

下载caffe并安装caffe python依赖

  1. git clone https://github.com/BVLC/caffe.git
  2. cd caffe
  3. cd python
  4. for req in $(cat requirements.txt); do sudo pip install $req; done

准备Makefile.config，以便它可以ubuntu上构建

  1. cd ../
  2. cp Makefile.config.example Makefile.config

修改Makefile.config如下

  1. ## Refer to http://caffe.berkeleyvision.org/installation.html
  2. # Contributions simplifying and improving our build system are welcome!
  4. # cuDNN acceleration switch (uncomment to build with cuDNN).
  5. USE_CUDNN := 1
  7. # CPU-only switch (uncomment to build without GPU support).
  8. # CPU_ONLY := 1
  10. # uncomment to disable IO dependencies and corresponding data layers
  11. # USE_OPENCV := 0
  12. USE_LEVELDB := 1
  13. # USE_LMDB := 0
  15. # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
  16. #	You should not set this flag if you will be reading LMDBs with any
  17. #	possibility of simultaneous read and write
  18. # ALLOW_LMDB_NOLOCK := 1
  20. # Uncomment if you're using OpenCV 3
  21. # OPENCV_VERSION := 3
  23. # To customize your choice of compiler, uncomment and set the following.
  24. # N.B. the default for Linux is g++ and the default for OSX is clang++
  25. # CUSTOM_CXX := g++
  27. # CUDA directory contains bin/ and lib/ directories that we need.
  28. CUDA_DIR := /usr/local/cuda
  29. # On Ubuntu 14.04, if cuda tools are installed via
  30. # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
  31. # CUDA_DIR := /usr
  33. # CUDA architecture setting: going with all of them.
  34. # For CUDA < 6.0, comment the *_50 lines for compatibility.
  36. CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
  37. 	     -gencode arch=compute_35,code=sm_35 \
  38. 	     -gencode arch=compute_50,code=sm_50 \
  39. 	     -gencode arch=compute_50,code=compute_50
  41. # BLAS choice:
  42. # atlas for ATLAS (default)
  43. # mkl for MKL
  44. # open for OpenBlas
  45. BLAS := open
  46. # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
  47. # Leave commented to accept the defaults for your choice of BLAS
  48. # (which should work)!
  49. # BLAS_INCLUDE := /path/to/your/blas
  50. # BLAS_LIB := /path/to/your/blas
  52. # Homebrew puts openblas in a directory that is not on the standard search path
  53. # BLAS_INCLUDE := $(shell brew --prefix openblas)/include
  54. # BLAS_LIB := $(shell brew --prefix openblas)/lib
  56. # This is required only if you will compile the matlab interface.
  57. # MATLAB directory should contain the mex binary in /bin.
  58. # MATLAB_DIR := /usr/local
  59. # MATLAB_DIR := /Applications/MATLAB_R2012b.app
  61. # NOTE: this is required only if you will compile the python interface.
  62. # We need to be able to find Python.h and numpy/arrayobject.h.
  63. #PYTHON_INCLUDE := /usr/include/python2.7 \
  64. 		/usr/lib/python2.7/dist-packages/numpy/core/include
  65. # Anaconda Python distribution is quite popular. Include path:
  66. # Verify anaconda location, sometimes it's in root.
  67. ANACONDA_HOME := /home/guan/anaconda2
  68. PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
  69. 		$(ANACONDA_HOME)/include/python2.7 \
  70. 		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
  72. # Uncomment to use Python 3 (default is Python 2)
  73. # PYTHON_LIBRARIES := boost_python3 python3.5m
  74. # PYTHON_INCLUDE := /usr/include/python3.5m \
  75. #                 /usr/lib/python3.5/dist-packages/numpy/core/include
  77. # We need to be able to find libpythonX.X.so or .dylib.
  78. #PYTHON_LIB := /usr/lib
  79. PYTHON_LIB := $(ANACONDA_HOME)/lib
  81. # Homebrew installs numpy in a non standard path (keg only)
  82. # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
  83. # PYTHON_LIB += $(shell brew --prefix numpy)/lib
  85. # Uncomment to support layers written in Python (will link against Python libs)
  86. WITH_PYTHON_LAYER := 1
  88. # Whatever else you find you need goes here.
  89. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
  90. LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial/
  92. # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
  93. # INCLUDE_DIRS += $(shell brew --prefix)/include
  94. # LIBRARY_DIRS += $(shell brew --prefix)/lib
  96. # Uncomment to use `pkg-config` to specify OpenCV library paths.
  97. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
  98. USE_PKG_CONFIG := 1
  100. # N.B. both build and distribute dirs are cleared on `make clean`
  101. BUILD_DIR := build
  102. DISTRIBUTE_DIR := distribute
  104. # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
  105. # DEBUG := 1
  107. # The ID of the GPU that 'make runtest' will use to run unit tests.
  108. TEST_GPUID := 0
  110. # enable pretty build (comment to see full commands)
  111. Q ?= @

注意修改路径。

执行

  1. make all -j
  2. make runtest
  3. make pycaffe

执行echo "export PYTHONPATH=/opt/cat-dogs/repo/caffe/python:$PYTHONPATH" >> ~/.bashrc这句也可以不添加到.bashrc,可以自己写个env_caffe.sh每次用caffe的时候source env_caffe.sh

编译caffe时出现的问题和解决方法（本地ubuntu16.04和服务器centos）

1.编译caffe时出现的错误

错误

  1. .build_release/src/caffe/proto/caffe.pb.h:12:2: error: #error This file was generated by a newer version of protoc which is

解决方法下载新版本的、编译安装

  1. sudo apt-get install autoconf automake libtool
  2. git clone https://github.com/google/protobuf
  3. ./autogen.sh
  4.  ./configure
  5. make
  6. make check
  7. sudo make install

错误

  1. /usr/include/boost/python/detail/wrap_python.hpp:50:23: fatal error: pyconfig.h: No such file or directory

解决方法

  1. export CPLUS_INCLUDE_PATH=/usr/include/python2.7
  2. make clean
  3. make all -j2

错误

  1. fatal error: caffe/proto/caffe.pb.h: No such file or directory

解决方法

  1. protoc src/caffe/proto/caffe.proto --cpp_out=.
  2. mkdir include/caffe/proto
  3. mv src/caffe/proto/caffe.pb.h include/caffe/proto

2.make runtest出现的错误

错误

  1. .build_release/tools/caffe: error while loading shared libraries: libprotobuf.so.14: cannot open shared object file: No such file or directory
  2. Makefile:526: recipe for target 'runtest' failed

解决方法添加链接路径

  1. export LD_LIBRARY_PATH=/usr/local/lib/

3.import caffe时出现的错误

错误

No module named google.protobuf.internal

解决方法

/home/guan/anaconda2/bin/pip install protobuf

错误

  1. /home/guan/anaconda2/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.21' not found

解决方法

conda install libgcc

错误

No module named google.protobuf.internal

解决方法

/home/guan/anaconda2/bin/pip install protobuf

4.runtest出现的错误

错误

  1. src/caffe/test/test_gradient_based_solver.cpp:373: Failure
  2. The difference between expected_updated_weight and solver_updated_weight is 1.7136335372924805e-07, which exceeds error_margin, where
  3. expected_updated_weight evaluates to 9.6857547760009766e-06,
  4. solver_updated_weight evaluates to 9.8571181297302246e-06, and
  5. error_margin evaluates to 1.0000000116860974e-07.
  6. [  FAILED  ] NesterovSolverTest/2.TestNesterovLeastSquaresUpdateWithEverything, where TypeParam = caffe::GPUDevice<float> (6484 ms)

解决方法

执行export CUDA_VISIBLE_DEVICES=0，重新执行测试。

参考runtest出现的问题

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