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本文參考caffe官網教程以及網上的兩篇教程：Ubuntu14.04+CPU+Python的Caffe安裝教程、Caffe學習系列(13)：數據可視化環境（python接口)配置編寫而成，因為過程比較波折，記錄下來以備日后查用

安裝編譯caffe的各種依賴

安裝基礎依賴

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-dev

安裝BLAS

sudo apt-get install libatlas-base-dev

其他的一些依賴

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Python Caffe

安裝Python

sudo apt-get install python-dev

###安裝anaconda 從清華的鏡像下載anaconda，根據anaconda官網提供的版本號，從清華鏡像的目錄中查找到對應的版本為Anaconda2-5.0.1-Linux-x86_64.sh（python2.7版本）

wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-5.0.1-Linux-x86_64.sh

下載完成后，運行這個sh。在安裝的過程中，會問你安裝路徑，直接回車默認就可以了。有個地方問你是否將anaconda安裝路徑加入到環境變量（.bashrc)中，這個一定要輸入yes。

安裝成功后，會有當前用戶根目錄下生成一個anaconda2的文件夾，里面就是安裝好的內容。

下載Caffe

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

配置python

將caffe根目錄下的python文件夾加入到環境變量，等編譯好了以后就可以import caffe來使用了

打開配置文件bashrc

sudo vi ~/.bashrc

在最后面加入

export PYTHONPATH=$(caffe_path)/python:$PYTHONPATH

注意 $(caffe_path) 需要根據自己caffe安裝路徑的實際情況配置

保存退出，更新配置文件

source ~/.bashrc

修改Makefile.config

首先，在caffe的根目錄下復制Makefile.config.example 成 Makefile.config

cp Makefile.config.example Makefile.config

然后修改Makefile.config，我的配置如下所示

## 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). #因為使用的是cpu模式，所以要放開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 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_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. #因為使用了anaconda所以要配置anaconda的地址，如果不配置則會出現找不到*.h的情況ANACONDA_HOME := $(HOME)/anaconda2 #使用anaconda的頭文件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 #使用anaconda的庫 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 ?= @

編譯caffe

make pycaffe -j8 make all -j8 make test -j8 make runtest -j8

運行python

進入caffe/python ，運行python

python Python 2.7.14 |Anaconda custom (64-bit)| (default, Oct 16 2017, 17:29:19) [GCC 7.2.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import caffe >>>

輸入import caffe沒有反應則說明成功，但如果出現問題error ：No module named google.protobuf.internal，則運行以下代碼：

conda install protobuf

接下來，運行caffe自帶的例子，可參考運行caffe自帶的兩個簡單例子

轉載于:https://my.oschina.net/u/2480851/blog/1606956

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