在 Mac 上配置 Caffe 大概花了半天多的时间，时间主要是花在解决各种奇怪的 error 上面了。在此记录一下配置的过程和遇到的问题，希望今后能少走一些弯路。

安装过程

1. 安装 Cuda。虽说打算无脑运行，但还是安上了。

2. 安装 Homebrew 工具。

3. Homebrew 安装 Caffe 依赖，有些安装速度比较慢，耐心啊。

# general dependencies
$ brew install -vd snappy leveldb gflags glog szip lmdb
$ brew tap homebrew/science
$ brew install hdf5 opencv
# with Python pycaffe needs dependencies built from source
$ brew install --build-from-source --with-python -vd protobuf
$ brew install --build-from-source -vd boost boost-python
$ brew install homebrew/science/openblas

4. 修改文件。可以使用命令：

$ brew edit openCV 

或者由路径 /usr/local/Homebrew/Library/Taps/homebrew/homebrew-science/opencv.rb 直接寻找文件。

替换：
args << "-DPYTHON#{py_ver}_LIBRARY=#{py_lib}/libpython2.7.#{dylib}"
args << "-DPYTHON#{py_ver}_INCLUDE_DIR=#{py_prefix}/include/python2.7"
为：
args << "-DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib"
args << "-DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7"

5. 下载 Caffe 源码并生成配置文件。

$ git clone https://github.com/bvlc/caffe.git
$ cd caffe
$ cp Makefile.config.example Makefile.config

6. 修改文件 Makefile.config。主要修改的地方有：
   1. 去掉注释符，设置为 CPU_ONLY 模式。
   2. 配置 BLAS。
   3. 设置 Anaconda 路径。
   4. 在 mac OS Sierra 环境下设置禁止使用 LevelDB（不兼容）。

## 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 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_50,code=compute_50

# 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 := /usr/local/Cellar/pyenv/20160726/versions/anaconda2-4.1.0
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
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

# 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 ?= @

7. 编译、测试。

$ make all
$ make runtest

发现有很多 warning，但这些不会影响工作。

8. 为了可以在 python 中引入模块，需要编译 pycaffe。在之后 import 时可能出现错误 "No module named google.protobuf.internal"，因此先要安装 protobuf。

pip install protobuf

之后编译 pycaffe。

make pycaffe
make distribute

9. 在 ".bash_profile" 中设置环境变量 PYTHONPATH。

export PYTHONPATH=/Users/Dennis/caffe/python:$PYTHONPATH

10. 完成。