安装python caffe过程中遇到的一些问题以及对应的解决方案

关于系统环境：

• Ubuntu 16.04 LTS
• cuda 8.0
• cudnn 6.5
• Anaconda3

编译pycaffe之前需要配置文件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 := 

 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 

 # uncomment to disable IO dependencies and corresponding data layers
 # USE_OPENCV :=
 # USE_LEVELDB :=
 # USE_LMDB := 

 # 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 := 

 # Uncomment if you're using OpenCV 3
 # OPENCV_VERSION := 

 # 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. \
 #        /usr/lib/python2./dist-packages/numpy/core/include
 # Anaconda Python distribution is quite popular. Include path:
 # Verify anaconda location, sometimes it's in root.
  ANACONDA_HOME := /home/ipc/anaconda3
 # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 #         $(ANACONDA_HOME)/include/python3.6m \
 #         $(ANACONDA_HOME)/lib/python3./site-packages/numpy/core/include
 # 关键点1：根据自己的情况设置好ANACONDA的路径
 # Uncomment to use Python  (default is Python )
 PYTHON_LIBRARIES := boost_python3 python3.6m
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include $(ANACONDA_HOME)/include/python3.6m $(ANACONDA_HOME)/lib/python3./site-packages/numpy/core/include
 # 关键点2：需要注意其中的版本号，原始文件是3.5的，但是我的anaconda是3.6的，因此如果直接uncomment，就会出现问题，需要根据自己的情况设置好
 # 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 := 

 # Whatever else you find you need goes here.
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include /usr/local/cuda/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial /usr/local/cuda/lib64

 # 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 := 

 # 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 := 

 # 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 := 

 # The ID of the GPU that 'make runtest' will use to run unit tests.
 TEST_GPUID := 

 # enable pretty build (comment to see full commands)
 Q ?= @

1. 如上方式配置文件Makefile.config（路径问题）
   • 可避免Python.h 和 numpy/arrayobject.h文件找不到的问题
2. cannot find -lboost_python3的问题（版本问题）（参考 http://blog.csdn.net/u012675539/article/details/51351553）
   • 检查是否有文件存在：ls /usr/lib/x86_64-linux-gnu/libboost_python-py35.so
   • 建立软链接：sudo ln -s libboost_python-py35.so libboost_python3.so
3. libstdc++.so.6: version 'GLIBCXX_3.4.20' not found的问题 (版本问题)
   • conda install libgcc（conda不能也无需使用sudo）
4. No module named 'google'的问题 （版本问题）
   • conda install protobuf
5. 以sudo用户（如sudoxxx）进行安装，其他用户（如otherxxx）进行运行
   • sudo chown otherxxx caffe（权限问题）
   • .bashrc修改环境变量：最后面加入export PYTHONPATH=(你的caffe/python路径，如/home/otherxxx/caffe/python):$PYTHONPATH

上述即为在安装pycaffe过程中所踩过的坑！