在这个版本安装之前，要先装好opencv，openmpi等。
下载地址：https://github.com/yjxiong/caffe.git
我的opencv是2.4.12版本
编译是用了：
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -DCUDA_CUDA_LIBRARY=/usr/local/cuda/lib64/stubs/libcuda.so -D CUDA_ARCH_BIN=5.2 -D CUDA_ARCH_PTX="" -D WITH_CUDA=ON -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D ENABLE_FAST_MATH=1 -D CUDA_FAST_MATH=1 -D WITH_CUBLAS=1 -D WITH_NVCUVID:BOOL="1" .
caffe的编译是：
cmake -DUSE_MPI=ON -DMPI_CXX_COMPILER=/data/dog123/openmpi/bin/mpicxx ..
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（还是写完整些比较好）
到你要存放是目录下，使用命名（git clone https://github.com/yjxiong/caffe.git）下载软件包。
将Makefile.config.example 另存一份名为Makefile.config
修改Makefile.config，最终的样子如下：
## 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 := atlas # 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. # 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)/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 # 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 $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @
（红色部分是要核对下的）
然后在caffe目录下执行如下命令：
创建build文件夹并进入：
mkdir build
cd build
编译：
cmake -DUSE_MPI=ON -DMPI_CXX_COMPILER=/data/dog123/openmpi/bin/mpicxx ..
编译的结果是：
og@asus:/data/dog123/caffe/build$ cmake -DUSE_MPI=ON -DMPI_CXX_COMPILER=/data/dog123/openmpi/bin/mpicxx .. -- The C compiler identification is GNU 4.7.3 -- The CXX compiler identification is GNU 4.7.3 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Boost version: 1.64.0 -- Found the following Boost libraries: -- system -- thread -- Looking for include file pthread.h -- Looking for include file pthread.h - found -- Looking for pthread_create -- Looking for pthread_create - not found -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - found -- Found Threads: TRUE -- Found GFlags: /usr/include -- Found gflags (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so) -- Found Glog: /usr/include -- Found glog (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so) -- Found PROTOBUF: /usr/lib/x86_64-linux-gnu/libprotobuf.so -- Found PROTOBUF Compiler: /usr/bin/protoc -- Found HDF5: /usr/lib/x86_64-linux-gnu/libhdf5_hl.so;/usr/lib/x86_64-linux-gnu/libhdf5.so -- Found LMDB: /usr/include -- Found lmdb (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/liblmdb.so) -- Found LevelDB: /usr/include -- Found LevelDB (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libleveldb.so) -- Found Snappy: /usr/include -- Found Snappy (include: /usr/include, library: /usr/lib/libsnappy.so) -- CUDA detected: 8.0 -- Found cuDNN (include: /usr/local/cuda/include, library: /usr/local/cuda/lib64/libcudnn.so) -- Added CUDA NVCC flags for: sm_52 -- OpenCV found (/usr/local/share/OpenCV) -- Found Atlas: /usr/include -- Found Atlas (include: /usr/include, library: /usr/lib/libatlas.so) -- Found PythonInterp: /usr/bin/python2.7 (found suitable version "2.7.6", minimum required is "2.7") -- Found PythonLibs: /usr/lib/x86_64-linux-gnu/libpython2.7.so (found suitable version "2.7.6", minimum required is "2.7") -- Found NumPy: /usr/local/lib/python2.7/dist-packages/numpy/core/include (found suitable version "1.12.1", minimum required is "1.7.1") -- NumPy ver. 1.12.1 found (include: /usr/local/lib/python2.7/dist-packages/numpy/core/include) -- Boost version: 1.64.0 -- Found the following Boost libraries: -- python -- Found Doxygen: /usr/bin/doxygen (found version "1.8.6") -- Found MPI_C: /data/dog123/openmpi/lib/libmpi.so -- Found MPI_CXX: /data/dog123/openmpi/lib/libmpi.so -- Detected Doxygen OUTPUT_DIRECTORY: ./doxygen/ -- Found Git: /usr/bin/git (found version "1.9.1") -- -- ******************* Caffe Configuration Summary ******************* -- General: -- Version : <TODO> (Caffe doesn't declare its version in headers) -- Git : v0.9999-1628-gfd7458e -- System : Linux -- C++ compiler : /usr/bin/c++ -- Release CXX flags : -O3 -DNDEBUG -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -- Debug CXX flags : -g -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -- Build type : Release -- -- BUILD_SHARED_LIBS : ON -- BUILD_python : ON -- BUILD_matlab : OFF -- BUILD_docs : ON -- CPU_ONLY : OFF -- -- Dependencies: -- BLAS : Yes (Atlas) -- Boost : Yes (ver. 1.64) -- glog : Yes -- gflags : Yes -- protobuf : Yes (ver. 2.5.0) -- lmdb : Yes (ver. 0.9.10) -- Snappy : Yes (ver. 1.1.0) -- LevelDB : Yes (ver. 1.15) -- OpenCV : Yes (ver. 2.4.12) -- CUDA : Yes (ver. 8.0) -- -- NVIDIA CUDA: -- Target GPU(s) : Auto -- GPU arch(s) : sm_52 -- cuDNN : Yes -- -- Python: -- Interpreter : /usr/bin/python2.7 (ver. 2.7.6) -- Libraries : /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.6) -- NumPy : /usr/local/lib/python2.7/dist-packages/numpy/core/include (ver 1.12.1) -- -- Documentaion: -- Doxygen : /usr/bin/doxygen (1.8.6) -- config_file : /data/dog123/caffe/.Doxyfile -- -- Install: -- Install path : /data/dog123/caffe/build/install -- -- Configuring done -- Generating done -- Build files have been written to: /data/dog123/caffe/build dog@asus:/data/dog123/caffe/build$
安装：
make all -j8 (j8 是为了加快安装速度，可以去掉)
sudo make install (注意 sudo权限)
最后就是测试：
make runtest (我这里有2个test不过，但是我还没找到原因（因为没看到错误在哪，都在输出的前面覆盖了），因为装好多遍都有2个不过，所以先将就。也就是这样，感觉自己跟一个炸弹绑在一起，我不知道它什么时候会不爽然后炸我1炸，哈哈哈哈哈)
最后就是python和matlab接口。
这2者都是caffe装之前就装好了的。
编译python接口：
添加环境变量：
vi ~/.bashrc
写入：
export PYTHONPATH=/your/path/caffe/python:$PYTHONPATH
保存，退出，执行sourc使文件生效：
source ~/.bashrc
接着在caffe目录下：
sudo make pycaffe
如果报错，点这里。一般再执行一遍上面命令即可。
最后就是：输入命令：
python
import caffe
没报错就是成功了。
编译matlab接口：
同理，在~/.bashrc中添加环境变量：
export PATH=$PATH:/usr/local/MATLAB/R2014a/bin
然后在caffe目录下执行：
sudo make matcaffe
没报错的话,就用下面命令测试下：
make matcaffe
如果报错，就点这里
嗯，就这些。