之前的博客中比较详尽地讲解了和GPU相关的一些环境的配置，下面讲解caffe的python环境及其依赖项的安装。
step3
step3:安装Caffe的python环境
（1）Caffe的官方网站推荐使用Anaconda，在Anaconda的官方网站（Downloads）下载安装文件（我下的是Anaconda2-4.1.1-Linux-x86_64.sh，其中4.1.1是Anaconda版本号），切换到该安装文件所在的目录，执行：
bash Anaconda2-4.1.1-Linux-x86_64.sh整个安装过程选择默认即可。
（2）添加Anaconda Library Path
打开~/.bashrc文件：
sudo gedit ~/.bashrc 将以下内容写入到~/.bashrc尾部：
export LD_LIBRARY_PATH="/home/username/anaconda2/lib:$LD_LIBRARY_PATH"注：username为用户名。
保存后执行：
source ~/.bashrc使配置生效。
（3）查看Anaconda是否安装完毕
终端输入：
python --version若显示：
Python 2.7.12 :: Anaconda custom (64-bit)则安装完毕。
附：Anaconda 的 gcc 版本问题
Anaconda的python对应的gcc版本是gcc4.4.7，ubuntu自带的Python对应的gcc版本是gcc5或gcc6（通常和系统当前的gcc版本一致）。那么，需改变anaconda的Python对应的gcc版本，使其和系统当前的gcc版本一致。命令如下：
conda install libgccstep4
step4:安装Caffe的依赖项
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler protobuf-c-compiler
sudo apt-get install libboost-all-dev build-essential libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags cython ipython step5
step5:Caffe编译及其python接口配置
（1）安装Caffe的python依赖库
访问并打开Caffe的github网站：BVLC/caffe，下载并解压Caffe源码包到Home目录下，进入caffe-master下的python目录，执行：
pip install -r requirements.txt这样做的目的是安装requirements.txt中所罗列出的python包，也可以执行以下命令来完成安装：
for req in $(cat requirements.txt); do pip install $req; done（2）编译Caffe及其python接口
进入caffe-master目录，复制一份Makefile.config.examples并重命名为Makefile.config，命令如下：
cp Makefile.config.examples Makefile.config打开Makefile.config，修改其中的一些配置并保存，修改后的文件如下：
## Refer to Caffe | Installation
# 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.
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.
ANACONDA_HOME := $(HOME)/anaconda2
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 /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial
# 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)
# 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 Removing -DNDEBUG from COMMON_FLAGS in Makefile breaks OS X build · Issue #171 · BVLC/caffe
# 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 ?= @现对修改的地方做一下讲解：
1.若要使用cuDNN加速，则USE_CUDNN := 1这一行取消注释；
2.若要使用Anaconda的python环境，则与python相关的一些路径需要换成Anaconda的，对应的配置如下：
# 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)/anaconda2
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)/lib3.若后续使用Caffe时需要用到python类型的层，则WITH_PYTHON_LAYER := 1这一行取消注释；
4.由于Ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，因此需要在Makefile.config中添加这一路径，否则在编译Caffe的时候会提醒找不到hdf5.h或者hdf5_hl.h，错误提示为：
./include/caffe/util/hdf5.hpp:6:18:fatal error:hdf5.h:No such file or directory对应的配置如下：
# 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 /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial修改完成后，在caffe-master目录下执行编译命令：
make all -j8若出现错误提示：
fatal error:caffe/proto/caffe.pb.h:No such file or directory则有可能是编译太快出现的错误，执行：
make clean后再进行编译。
编译成功后，继续执行：
make test -j8
make runtest -j8
make pycaffe # 编译Caffe的python接口
make distribute此时，Caffe及其python接口配置完成。
（3）Caffe的python接口测试
执行python进入python环境，输入以下命令：
import sys
sys.path.append('/home/username/caffe-master/python')
import caffe注：username为用户名。
若不报错，则Caffe的python接口配置完成。