花了一天踩各种坑，记录以后自己好参考，简记几个主要的
1. 先装几个依赖库
- 网上随便搜搜都有
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
sudo apt-get install libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
2. anaconda来配置python环境（python2.7）
- 先使用pip install -r requirements.txt 来安装pycaffe所需依赖库
对比python/requirements.txt 安装还没安装的，其中leveldb不要装，不然会和上面的冲突。其中protobuf千万不要用conda install来安装，要用~/anaconda2/bin/pip install protobuf 安装，不然import caffe会出现ImportError: No module named google.protobuf.internal
3. 文件Makefile.config与Makefile
- 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 #需手动编译opencv3
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
# 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.这里注意各个不要少，否则有你受的，对应Makefile有个地方要改（后面说）
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include /usr/include/hdf5/serial/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /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)
# 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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
- Makefile
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
4. ~/.bashrc与/etc/profile
- ~/bashrc
export PYTHONPATH=/home/tuxiang/caffe/python:$PYTHONPATH
LD_LIBRARY_PATH=$HOME/caffe/build/lib:/usr/lib/x86_64-linux-gnu:$HOME/anaconda2/lib:$LD_LIBRARY_PATH
# added by Anaconda2 4.3.0 installer
export PATH="/home/tuxiang/anaconda2/bin:$PATH"
- /etc/profile
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/lib
export LANG=zh_CN.UTF-8
参考
Caffe学习系列(13)：数据可视化环境（python接口)配置
另：关于python3.5的caffe安装可参考
Python3.5 Anaconda3 Caffe深度学习框架搭建
在 python3.5 下使用 Caffe
Ubuntu 16.04 or 15.10 Installation Guide wiki的右侧有一些对应的情况，如GeForce GTX 1080, CUDA 8.0, Ubuntu 16.04, Caffe下安装的建议
附一些python3.6遇到的错误
我用的anoconda4.3（python3.6），实验室两个账户安装2.7和3.6
- 如果使用的cuda-8.0，在Makefile.config中写成 CUDA_DIR := /usr/local/cuda-8.0
- protobuf的问题如上面链接的博客所说，cpp和python的版本应该保持一致，实在不行去github下源码编译安装。
- libboost的问题，参见上面博客
- matplotlib和dateutil的问题，这个是我自己遇到的。
anaconda里有默认安装的python-dateutil，删除旧的版本（1.5）。
另外后面编译通过，import出现问题，显示ModuleNotFoundError: No module named ‘matplotlib’ 和 ImportError: matplotlib requires dateutil,但是已经安装了这两个版本。
解决方法： 将/anaconda3/pkgs/matplotlib-2.0.0-np111py36_0/lib/python3.6/site-packages/下的所有matplotlib文件（夹）复制到/anaconda3/lib/python3.6/site-packages/下，对于python-dateutil也是如此。不知有没有其他方法，欢迎留言讨论
