Ubuntu 14.04 caffe的Python环境配置

利用anaconda来配置python环境

python环境不能单独配置，必须要先编译好caffe，才能编译python环境。下载anaconda：https://www.continuum.io/downloads
我在这里下载的是Python 2.7 版本。

下载成功后，在终端执行（2.7版本）

$ bash Anaconda2-2.4.1-Linux-x86_64.sh

在安装的过程中，会问你安装路径，直接回车默认就可以了。有个地方问你是否将anaconda安装路径加入到环境变量（.bashrc)中，这个一定要输入yes。安装成功后，会有当前用户根目录下生成一个anaconda2的文件夹，里面就是安装好的内容。
输入conda list 就可以查询，你现在安装了哪些库，常用的numpy, scipy名列其中。如果你还有什么包没有安装上，可以运行conda install * 来进行安装。
安装成功之后，会发现anaconda2文件有个小锁图形，因此我们需要用root权限，chmod 777 -R 解除anaconda2的文件夹保护，这样以后就可以用conda install * 来安装各种你需要库。
回到主目录，执行。

$ cd ~$ sudo chmod 777 -R anaconda2

编译python接口

首先，将caffe根目录下的python文件夹加入到环境变量。
打开配置文件 .bashrc，加入路径，其中XXX根据你的路径进行修改。

$ cd  ~/.bashrc $ export PYTHONPATH="/home/xxx/caffe/python:$PYTHONPATH"

然后修改caffe编译配置文件Makefile.config，我的配置是：

$ cd caffe/$ sudo vim 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.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)/anaconda2PYTHON_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/libPYTHON_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/includeLIBRARY_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 ?= @

修改之后进行编译：

$ sudo make pycaffe$ sudo make test -j4$ sudo make runtest -j4

也许你在编译runtest的时候，会报这样的错误：

.build_release/test/test_all.testbin: error while loading shared libraries: libhdf5.so.10: cannot open shared object file: No such file or directory

这是因为 libhdf5.so的版本问题，可以进入/usr/lib/x86_64-linux-gnu看一下，你的libhdf5.so.x中的那个x是多少，比如我的是libhdf5.so.7
执行下面几行代码解决:

$ cd /usr/lib/x86_64-linux-gnu$ sudo ln -s libhdf5.so.7 libhdf5.so.10$ sudo ln -s libhdf5_hl.so.7 libhdf5_hl.so.10$ sudo ldconfig

进入python环境，进行import操作:

$ import caffe