Blas Library Mac

2019-5-19  Mac BLAS. GitHub Gist: instantly share code, notes, and snippets. Install LAPACK and BLAS on Linux-Based Systems On July 27, 2016, in C/C, GNU, Mac/Linux, by jild13 Two of the most common used computational libraries are LAPACK and BLAS. Plaonline：不用能。 Linux下FFmpeg安装教程 weixin43892167：自己编译的目的是什么？应该有编译好的啊 SVM垃圾短信分类 wangwei5201314：你的文件里面数据集没有放吧 blas、lapack. 2020-4-2  Developer Reference for Intel® Math Kernel Library - C. Submitted March 30, 2020.

深度学习框架-caffe安装

[Mac OSX 10.12]

2019-11-14  Unable to use compiled netlib BLAS on mac OS X. Ask Question Asked 2 years, 11 months ago. It seems that by adding the flags -pipe -c to the make.inc file now the BLAS library is compiled correctly and I can use it to compile other C code. I'm going to try it more. 2017-4-21  This does not work in the case NumPy was compiled with a static library (e.g. ATLAS is compiled by default only as a static library). 1) Disable the usage of BLAS and fall back on NumPy for dot products. To do this, set the value of blas.ldflags as the empty string (ex: export THEANOFLAGS=blas.ldflags=). Depending on the kind of matrix. 博文 来自： 无形的风专栏 Mac + python 3.7 + 安装 sklearn 07-12 阅读数 3157. 在Github上看到有人用BLAS library优化自己的源码，对此产生了强烈兴趣。准备自己动手实践一下，网上搜索了一大堆编译安装.

【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】

As you can see, the icon for the Library folder is faded, which means the folder itself is still hidden. How to unhide files. To do this, open Finder, and head to your Home folder. Unhide the Library Folder PermanentlyIf you don’t want to open “Go To Folder” every time you want to access the Library, you can unhide the folder for good.

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

Openblast

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

Blis Blas

# 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.

If you want to sync your computer at home with the computer at work, you can use an iPhone, iPad or iPod to sync each of them. Open itunes accounts.

# 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

Cblas Ubuntu

-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

Mac

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 ?= @

Atlas Ubuntu

  • 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

Blas Library Machine

  1. make clean
  2. make all
  3. make test
  4. make runtest
  5. make pycaffe
  6. 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。

Amd Blas

posted @ 2017-09-02 00:40sold_out 阅读(..) 评论(..) 编辑收藏