深度学习框架-caffe安装-环境[Mac OSX 10.12]

深度学习框架-caffe安装
[Mac OSX 10.12]

【参考资源】
1.英文原文：（使用GPU）
[http://hoondy.com/2015/04/03/how-to-install-caffe-on-mac-os-x-10-10-for-dummies-like-me/]
2.基于1的两篇中文博客：
[http://ylzhao.blogspot.kr/2015/04/mac-os-x-1010caffe.html]
[http://www.jianshu.com/p/8795b882ea67]
3.无GPU，仅使用CPU的情况下的配置
[http://blog.csdn.net/u014696921/article/details/52156552]
[http://www.phperz.com/article/16/1006/298567.html]

---

我的电脑配置

系统：MacBook Pro OS X Sierra 版本10.12.2
CPU：2.7 GHz Intel Core i5
显卡：Intel Iris Graphics 6100 1536 MB

如果显卡是NVIDIA的，可以使用GPU，需要安装cuda，cuda driver和cuDNN GPU库，并且在Makefile配置成使用GPU。参考资源中【1】【2】是有NVIDIA显卡的所以安装了cuda，cuda driver和cuDNN GPU库，最后的caffe的Makefile.config文件中配置成使用GPU。

由于我电脑配置的不是NVIDIA显卡，所以不能使用cuda加速了，所以只能安装个CPU模式。可以忽略安装cuda，cuda driver和cuDNN的安装步骤，最后的caffe的Makefile.config文件中配置成仅使用CPU。

详细安装步骤

Homebrew

1. 根据 http://brew.sh/ 上面的说明安装Homebrew包管理

Anaconda Python

1. 从https://store.continuum.io/cshop/anaconda/下载和安装Anaconda Python包（其中包括Caffe框架用到的hdf5）
2. export PATH=~/anaconda/bin:$PATH

BLAS - Intel MKL

1. 由于Mac OS X操作系统自带的BLAS库存在一些不稳定的问题，因此我选择安装Intel MKL库。如果你是在校大学生，可以使用学校邮箱从https://software.intel.com/en-us/qualify-for-free-software/student页面申请Intel Parallel Studio XE 2017安装包（后面不要忘记在Makefile.config中设置BLAS:=MKL）
2. 确保在安装Intel Parallel XE时选择每一个组件（因为缺省情况下不会安装MKL组件）
3. cd /opt/intel/mkl/lib/
4. sudo ln -s . /opt/intel/mkl/lib/intel64（因为在编译Caffe时Caffe会从MKL的intel64目录中去搜索mkl的库，但是在安装MKL后，MKL的lib目录下并没有intel64这个目录，所以需要建立一个intel64目录到lib目录的软链接）

通过Homebrew安装依赖项

brew edit opencv 在自动打开的vim编辑器中将下面两行
args << "-DPYTHON#{py_ver}_LIBRARY=#{py_lib}/libpython2.7.#{dylib}"
args << "-DPYTHON#{py_ver}_INCLUDE_DIR=#{py_prefix}/include/python2.7"
替换为
args << "-DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib"
args << "-DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7"

vim中具体操作是：
i 从当前光标处进入插入模式，开始修改内容，esc 退出插入模式，:wq 保存修改并退出。

brew install --fresh -vd snappy leveldb gflags glog szip lmdb homebrew/science/opencv
brew install --build-from-source --with-python --fresh -vd protobuf
brew install --build-from-source --fresh -vd boost boost-python

从Github上面克隆Caffe的代码

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

配置Makefile.config

1. 设置BLAS := mkl（BLAS (使用intel mkl还是OpenBLAS)）
2. 取消USE_CUDNN := 1注释
3. 检查并设置Python路径
- 首先修改文件权限：chmod g+w Makefile.config
- 打开文件进行修改：sudo vim Makefile.config 	；按“i”键开始修改，修改 ：将# CPU_ONLY = 1前面的#去掉（ 由于我没有NVIDIA的显卡，就没有安装CUDA，因此需要打开这个选项） 并按“tab”键，（默认从tab处执行），设置BLAS := mkl，检查并设置python路径，修改结束后按esc键，键入“：wq”保存并退出；

以下是我的Makefile.config中的所有配置：（可以先在命令行中验证一下自己的文件路径，一定要根据自己路径进行设置！）

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 := mkl
# 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)/anaconda
PYTHON_INCLUDE :=  $(ANACONDA_HOME)/include/python2.7 
		   $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include 
		   $(ANACONDA_HOME)/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 ?= @

设置环境变量

1. export DYLD_FALLBACK_LIBRARY_PATH=/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib:/opt/intel/composer_xe_2015.2.132/compiler/lib:/opt/intel/composer_xe_2015.2.132/mkl/lib

• 必须手动查看自己的文件路径！根据自己的路径添加环境变量，我的路径如下：

export DYLD_FALLBACK_LIBRARY_PATH=$HOME/caffe/.build_release/lib:/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib:/opt/intel/compilers_and_libraries_2017.1.126/mac/compiler/lib:/opt/intel/compilers_and_libraries_2017.1.126/mac/mkl/lib/

编译Caffe

make clean
make all
make test
make runtest
make pycaffe
make distribute

• make all的时候注意库的链接路径，make runtest注意，会有这样的一个问题DYLD_FALLBACK_LIBRARY_PATH is cleared by the new System Integrity Protection ，所以要把System Integrity Protection禁止掉：具体操作：电脑重新开机同时按住command+r，进入恢复模式，然后打开终端，输入csrutil disable，就关闭SIP了，重新启动电脑即可。