来源： http://blog.csdn.net/daaikuaichuan/article/details/61414219
配置Makefile.config(参考：http://blog.csdn.net/autocyz/article/details/51783857 )
折腾到这一步，离成功就不远了，接下来就是配置之前搁置的Makefile.config，进入caffe根目录，使用vim编辑器打开Makefile.config。
在打开的Makefile.config修改如下内容(我自己的配置)：
USE_OPENCV := 1 USE_LEVELDB := 1 USE_LMDB := 1 CUSTOM_CXX := g++ CUDA_DIR := /usr/local/cuda-7.5 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 := atlas MATLAB_DIR := /home/eric/MATLAB2014/R2014a PYTHON_INCLUDE := /usr/include/python2.7 /usr/lib/python2.7/dist-packages/numpy/core/include PYTHON_LIB := /usr/local/lib WITH_PYTHON_LAYER := 1 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial BUILD_DIR := build DISTRIBUTE_DIR := distribute
9、make所有文件
进入caffe根目录，输入如下命令：
sudo make clean sudo make all -j4 sudo make test -j4 sudo make runtest -j4 sudo make pycaffe -j4 sudo make matcaffe -j4
在命令行下输入Python，会出现Python的一些信息，然后输入import caffe，没有报错说明配置成功。在命令行下输入matlab，会打开MATLAB软件。
如果前面所有的配置过程都没有问题的话，最后一步应该是不会出错的。至此，caffe所有的配置项都完成了，接下来就可以愉快地使用这个强大的深度学习框架了。
下面的是我的实际用的：
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome! BUILD_PYTHON:=1 BUILD_MATLAB:=1 BUILD_docs:=1 BUILD_SHARELIB:=1 # 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 := 1 USE_LEVELDB := 1 USE_LMDB := 1 # 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 through *_61 lines for compatibility. # For CUDA < 8.0, comment the *_60 and *_61 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_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61 # 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 := /usr/include BLAS_LIB := /usr/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/R2016b # 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 #PYTHON_LIB:=/usr/lib/x86_64-linux-gnu/libpython2.7.so # 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 # 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 /usr/local/lib /usr/lib/x86_64-linux-gnu/ # 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 # NCCL acceleration switch (uncomment to build with NCCL) # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0) # USE_NCCL := 1 # 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 ?= @ #INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ INCLUDE_DIRS := $(INCLUDE_DIRS) /usr/local/include /usr/include/hdf5/serial/ LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial LIBRARY_DIRS:=$(LIBRARIES_DIRS) /usr/lib/x86_64-linux-gnu/hdf5/serial
sea@sea-X550JK:~/caffeM/caffe$ ll matlab/+caffe/ 总用量 76 drwxrwxr-x 5 sea sea 4096 11月 9 17:26 ./ drwxrwxr-x 5 sea sea 4096 11月 9 17:26 ../ -rw-rw-r-- 1 sea sea 2930 11月 9 17:26 Blob.m -rw-rw-r-- 1 sea sea 1207 11月 9 17:26 get_net.m -rw-rw-r-- 1 sea sea 298 11月 9 17:26 get_solver.m drwxrwxr-x 2 sea sea 4096 11月 9 17:26 imagenet/ -rw-rw-r-- 1 sea sea 1742 11月 9 17:26 io.m -rw-rw-r-- 1 sea sea 841 11月 9 17:26 Layer.m -rw-rw-r-- 1 sea sea 4912 11月 9 17:26 Net.m drwxrwxr-x 2 sea sea 4096 11月 10 19:48 private/ -rw-rw-r-- 1 sea sea 172 11月 9 17:26 reset_all.m -rw-rw-r-- 1 sea sea 393 11月 9 17:26 run_tests.m -rw-rw-r-- 1 sea sea 250 11月 9 17:26 set_device.m -rw-rw-r-- 1 sea sea 97 11月 9 17:26 set_mode_cpu.m -rw-rw-r-- 1 sea sea 97 11月 9 17:26 set_mode_gpu.m -rw-rw-r-- 1 sea sea 1872 11月 9 17:26 Solver.m drwxrwxr-x 2 sea sea 4096 11月 9 17:26 +test/ -rw-rw-r-- 1 sea sea 110 11月 9 17:26 version.m sea@sea-X550JK:~/caffeM/caffe$
# /etc/profile: system-wide .profile file for the Bourne shell (sh(1)) # and Bourne compatible shells (bash(1), ksh(1), ash(1), ...). if [ "$PS1" ]; then if [ "$BASH" ] && [ "$BASH" != "/bin/sh" ]; then # The file bash.bashrc already sets the default PS1. # PS1='h:w$ ' if [ -f /etc/bash.bashrc ]; then . /etc/bash.bashrc fi else if [ "`id -u`" -eq 0 ]; then PS1='# ' else PS1='$ ' fi fi fi if [ -d /etc/profile.d ]; then for i in /etc/profile.d/*.sh; do if [ -r $i ]; then . $i fi done unset i fi export PYTHONPATH=/usr/local:$PYTHONPATH export PYTHONPATH=$PYTHONPATH:/home/sea/caffe2/build export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH export PYTHONPATH=/home/sea/caffeM/caffe/python:$PYTHONPATH export PATH=$PATH:/home/sea/caffeM/caffe/build/tools/:/usr/local/cuda-8.0/bin export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib:$LD_LIBRARY_PATH export PYTHONPATH=$PYTHONPATH:/home/sea/caffeM/caffe/python export PATH=$PATH:/usr/local/MATLAB/R2016b/bin export MATLABDIR=/usr/local/MATLAB/R2016b export Matlab_mex=/usr/local/MATLAB/R2016b/bin/mex export Matlab_mexext=/usr/local/MATLAB/R2016b/bin/mexext