本文共 6213 字，大约阅读时间需要 20 分钟。

说明

采用caffe源码编译安装方式

Caffe编译仅CPU支持版本

下载

可以通过登陆官网下载：

git clone https://github.com/BVLC/caffe.git

但是因为github国内下载慢，所以在gitee上fork了一份

git clone https://gitee.com/cuibixuan/caffe.git

推荐git下载，后续开发或者查看修改，可以git log 文件名称查看修改变更历史。

主机环境配置

个人系统:ubuntu 14.04

1.安装依赖库和python2.7

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-devsudo apt-get install libopenblas-dev liblapack-dev libatlas-base-devsudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-devsudo 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 libatlas-base-devsudo apt-get install python-dev python-pip gfortran

2.安装cuda

cuda是GPU使用，但是我仅编译CPU版本caffe，但是不装编译报错。

sudo apt-cache search cuda （这一步可search apt源是否存在cuda包）sudo apt-get install nvidia-cuda-dev nvidia-cuda-toolkit

编译Caffe

1.修改Make.config

cd caffe;cp Makefile.config.example Makefile.config

详细Makefile.config配置说明请移步本人另一篇博客（）

这里直接粘贴最终结果文件：

cat Makefile.config## 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# 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 through *_61 lines for compatibility.# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.# For CUDA >= 9.0, comment the *_20 and *_21 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 OpenBlasBLAS := 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_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# 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 /usr/include/hdf5/serial/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 := buildDISTRIBUTE_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 ?= @

2.make编译

make –j6make pycaffe  (编译python接口，如果直接使用C++学习，可跳过)export PYTHONPATH=/home/cuibixuan/git/caffe/python/ （将caffe导入到环境变量）

测试通过

3.报错解决

ImportError: No module named skimage.io

sudo apt-get install python-skimage

ImportError: No module named google.protobuf.internal

sudo apt-get install python-protobuf

更多错误解决办法，移步另一个博客（）