在Windows上使用OpenBLAS安装numpy的教程 [英] Tutorial for installing numpy with OpenBLAS on Windows

问题描述

拜托，我这里需要一盏灯.我想在Windows上使用良好的BLAS/LAPACK lib 安装numpy，但绝对没有页面能够很好地解释该过程.看来OpenBLAS是个不错而又快速的选择.

目标是将"theano"与"keras"一起使用，并且"theano"要求库是动态的"，而不是静态的. (不确定我是否明白这是什么意思，但这会导致速度缓慢和内存问题)

请把我当作一个完整的新手.给我一个循序渐进教程，教您如何做！不要忘记告诉我文件"应该去哪里！哪个文件夹应该放在PATH中！我应该确切地调用哪些命令，它们的输出在哪里?它们的结果或编译的文件该怎么办? numpy如何找到它们?等等，我所见过的所有站点似乎都认为我是一名Linux专家，并且已经了解了一切.

我尝试过的事情:

• 从此处下载了numpy + mkl的编译版本- -这确实安装了numpy，可以使用，但是theano除了工作缓慢之外，还存在内存泄漏问题.是在.theanorc文件中设置正确的ldflags的问题吗?如果是这样，哪些标志? -关于MKL库，此答案可能有用吗?

• 试图安装Anaconda-它也不起作用，我也不知道出了什么问题.它给我的消息是建议安装一些额外的东西，它虽然有效，但速度非常慢. 比我上面提到的错误的numpy版本慢10倍以上(因此，无法接受，无法以这种速度工作).如果我必须更改有关Anaconda的所有内容，最好改用常规python并了解发生了什么.

• 找到了这些已经编译的 BLAS/LAPACK库 (.dll和.lib)文件.但是.... 我应该如何处理它们?-只需将它们的文件夹添加到PATH var并安装numpy，就可以得到"numpy-atlas"，而不是我下载的库. 如何让numpy看到它们?

• 试图理解此页面，但是似乎它将使我精确地回到前面的情况，我将如何处理结果?他们建议我使用的库在哪里?建议使用什么quickbuild脚本，它们在哪里?

• 在此处找到了Cygwin选项.我还没有尝试过，但听起来比重新安装我的所有python和软件包(全部来自Cygwin)要容易一些

解决方案

按照> https://github.com/jhvital/build- scipy-openblas .

show_config方法显示了与openblas库的链接，这可以通过将脚本的运行时与安装了mkl软件包的conda install scipy所安装的scipy进行比较来确认.

我仍然不确定是否正确链接了库.反馈将不胜感激.

Please, I do need a light here. I want to install numpy using a good BLAS/LAPACK lib on Windows, but absolutely no page explains the process well enough. It seems OpenBLAS is a good and fast option.

The goal is to use "theano" with "keras", and "theano" requires that the libraries be "dynamic", not static. (Not sure I understand what that means, but it causes slowness and memory issues)

Please treat me as a complete newbie. Give me a step by step tutorial on how to do it! Don't forget to tell me "where" files should go! Which folders should go in PATH! What commands exactly I should call, and what are their output, where? What do I do with their results or with compiled files? How does numpy find them? Etc. All the sites I've seen seem to think I'm a linux expert and already know everything.

What I have tried:

• Downloaded the compiled version of numpy+mkl from here -- This does install numpy, it becomes usable, but theano presents the memory leak problem, besides working slowly. Is it a matter of setting the right ldflags in the .theanorc file? If so, which are the flags? - About the MKL libraries, this answer may be useful?

• Tried installing Anaconda - it doesn't work either, and I had no idea about what went wrong. It gave me messages suggesting installing some extra stuff, it worked but incredibly slow. More than 10 times slower than my bugged numpy version mentioned above (so, unacceptable, impossible to work at that speed). If I have to go changing everything about Anaconda, it's better to use a regular python instead and know what is going on.

• Found these already compiled BLAS/LAPACK libraries (.dll and .lib) files. But.... what am I supposed to do with them? -- Simply adding their folders to the PATH var and installing numpy gives me "numpy-atlas", not the libraries I downloaded. How do I make numpy see them?

• Tried to understand this page, but yet, it seems it will lead me exactly to the previous case, what will I do with the results? Where are the libs they suggest I use? What are the suggested quickbuild scripts, where are they?

• Found the Cygwin option here. I haven't tried it, but it sounds it should be something easier than reinstalling all my python and packages, all from Cygwin

解决方案

Tried following the building instructions in http://scipy.github.io/devdocs/building/windows.html a number of times without success.

[MY SOLUTION]

After extensive reading of the logs and related forums, came up with a method that has worked for me in two windows machines already. Posted a batch file with the steps I used in https://github.com/jhvital/build-scipy-openblas.

The show_config method shows linkage to openblas libraries, and this was confirmed by comparing the runtimes of scripts against the scipy installed with conda install scipy, which installs the mkl package.

Still I'm not entirely sure whether the libraries were linked correctly. Feedback would be appreciated.

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