Blas Library Mac
深度学习框架-caffe安装
2019-12-11 I use numpy and scipy in different environments (MacOS, Ubuntu, RedHat). Usually I install numpy by using the package manager that is available (e.g., mac ports, apt, yum). However, if you don't compile Numpy manually, how can you be sure that it uses a BLAS library? Using mac ports, ATLAS is installed as a dependency. 2014-1-8 You can also use both: Take the Accelerate Framework as BLAS library and Take Reference LAPACK for LAPACK library The BLAS included in the Accelerate Framework will give you very good performance as it is an optimized and multi-threaded BLAS.Thread are controlled with the VECLIBMAXIMUMTHREAD environment variable. Blas、lapack、cblas在Ubuntu上的安装 这是整合借鉴几篇别人的文章，但是做了一点修改。（最初是谁的，忘记了） 1.确保机器上安装了gfortran编译器，如果没有安装的话，可以使用 sudo apt-get install gfortran 2.下载blas, cblas, lapack 源代码. 2016-11-28 最近想使用atlas，可是在网上找了好些教程，自己编译源码atlas总是报错。经过Google，查看运维 由于我之前一直强调数据结构以及算法学习的重要性，所以就有一些读者经常问我，数据结构与算法应该要学习到哪个程度呢？. 2019-11-28 Calling LAPACK, BLAS, and CBLAS Routines from C/C Language Environments To use Intel MKL Fortran-style functions in C/C environments, you should observe certain conventions, which are discussed for LAPACK and BLAS in the subsections below.
[Mac OSX 10.12]
【参考资源】
Openblast
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
- 根据 http://brew.sh/上面的说明安装Homebrew包管理
- Anaconda Python
- 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）
- export PATH=~/anaconda/bin:$PATH
- BLAS - Intel MKL
- 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）
- 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）
- cd /opt/intel/mkl/lib/
- 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
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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
- 设置BLAS := mkl（BLAS (使用intel mkl还是OpenBLAS)）
- 取消USE_CUDNN := 1注释
- 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)
Atlas Ubuntu
# 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
R Blas
# '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
Blis Blas
-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
Windows Cblas
# 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
Blas Library Macomb
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
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# 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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
- 设置环境变量
- export DYLD_FALLBACK_LIBRARY_PATH=/usr/local/cuda/lib:$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。