blocks|key|262301|text|在您的例子中，您应该查看/usr/lib64，看看libblas等是否以.so或.so.X文件的形式提供。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|262302|重新编译BLAS并非易事，但您可以尝试为您的发行版安装相关的ATLAS包。|262303|entityMap^0|C|A|P|7|10|3|14|5|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@$9|K|A|L|B|C]|$9|M|A|N|B|C]|$9|O|A|P|B|C]|$9|Q|A|R|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|S|8|@]|D|@]|E|$]]|$1|H|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|I|$]]

In your case you should look in <code>/usr/lib64</code> and see if <code>libblas</code>, etc. are available as <code>.so</code> or <code>.so.X</code> files.

Recompiling BLAS is not trivial, but you can maybe try installing the relevant ATLAS packages for your distribution.

blocks|key|2950386|text|对于ubuntu，在包管理器中，libblas.so附带了libblas3gf包。如果不知何故它没有创建libblas.so，而是创建了libblas.so.X，则手动创建一个符号链接，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2950387|cd+/usr/lib64
sudo+ln+-s+libblas.so.3gf+libblas.so|code-block|syntax|javascript|2950388|注意:这对我来说工作得很好，但请阅读下面的评论。请记住，此软件包不会针对您的特定硬件进行优化(例如，请阅读其他建议使用ATLAS的答案)。|2950389|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

With ubuntu, in the package manager, libblas.so comes with the libblas3gf package.
If somehow it doesn't create libblas.so, but creates libblas.so.X, manually create a symlink like:

<pre><code>cd /usr/lib64
sudo ln -s libblas.so.3gf libblas.so
</code></pre>

NB: This has been working fine for me, but read the comment below though. And keep in mind this package will not be optimized to your specific hardware (read other answers that suggest ATLAS for instance).

blocks|key|2950406|text|如果安装了最新的足够版本的numpy，theano在任何情况下都能正常工作。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2950407|从那时起，关心所使用的blas的唯一原因是速度。默认的blas非常慢。许多发行版再次编译numpy这个缓慢的blas版本。|2950408|一个更快实现blas的简单/快速的方法是安装关于atlas和atlas+devel的发行版软件包。这是一个优化的blas实现。|2950409|在Unbuntu的更新版本中，在完成中安装atlas是这样一种方式，numpy将开始使用它。所以没有必要在Theano上对此做任何更改。我不知道其他发行版是否也这样做。|2950410|要检查Theano使用的blas是否快，最好的方法就是给它计时。为此，在bash下运行以下命令：|2950411|X=`python+-c+"import+theano;import+os.path;+print+os.path.split(theano.__file__)[0]"`
python+${X}/misc/check_blas.py|code-block|syntax|javascript|2950412|然后将运行速度与打印的其他比较结果进行比较。|2950413|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|U|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|V|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|W|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|X|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|Y|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

If a recent enought version of numpy is installed, theano will work correctly in all case­.

From there, the only reason to care about the blas used is about speed. The default blas is very slow. Many distro compile numpy again this slow blas version.

An easy/fast way to have a faster blas implementation is to install the distro package about atlas and atlas devel. This is an optimized blas implementation.

Newer version of Unbuntu, the installation of atlas in done is such a way that numpy will start using it. So there is no need to change anything on Theano about this. I don't know if other distro do this.

The best way to check the blas that Theano use is fast or not is to time it. To do so, run this under bash:

<pre><code>X=`python -c "import theano;import os.path; print os.path.split(theano.__file__)[0]"`
python ${X}/misc/check_blas.py
</code></pre>

Then compare the speed it took to run with some other comparison result printed.

blocks|key|263770|text|您的主要问题基本上是发行版维护者是否具有正确安装的依赖项--对于这个问题，我没有答案或解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|263771|然而，我确实有一个建议。要构建ATLAS并不是很难。获取源代码，解包，确保您已经满足了它的依赖关系，然后开始配置和制作步骤。依赖部分可能是该过程中最耗时的手动部分。|263772|当然，然后你必须重新链接numpy，theano等。虽然我意识到这是一种痛苦(相信我，我在Theano和Hannes+Shulz+&+Andy+Mueller的CSV中都经历过)，但你得到的好处是优化了你的硬件。|263773|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Your main question is essentially one of whether the distro maintainers have the dependencies installing correctly or not -- and for that I don't have an answer or a solution.

However, I do have a recommendation. ATLAS isn't very hard to get to build. Get the source, unpack, ensure you've satisfied its dependencies, then kick off the configure &amp; make steps. The dependency part is probably the most time consuming manual portion of the process.

Of course, then you have to relink numpy, theano, etc. While I recognize this is a pain (believe me, I went through it for both Theano and Hannes Shulz &amp; Andy Mueller's CSV), the benefit you get is a BLAS tuned to run optimally on your hardware.

blocks|key|262378|text|在下载了python库的Anaconda发行版之后，我在Ubuntu+12.04+LTS+64位上也遇到了同样的问题。将Theano指向包含libblas.so的目录可以解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|262379|$+THEANO_FLAGS=blas.ldflags="-L/usr/lib/+-lblas"+python+rbm.py|offset|length|style|CODE|262380|entityMap^0|0|0|1Q|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

After downloading the Anaconda distro of python libraries, I just encountered the same issue on Ubuntu 12.04 LTS 64-bit. Pointing Theano to the directory containing libblas.so took care of it.

<code>$ THEANO_FLAGS=blas.ldflags="-L/usr/lib/ -lblas" python rbm.py</code>

The <a href="http://www.deeplearning.net/software/theano/install.html#troubleshooting-make-sure-you-have-a-blas-library" rel="noreferrer">theano installation documentation</a> states, that <a href="http://www.deeplearning.net/software/theano/" rel="noreferrer">theano</a> will as a default use the BLAS libraries from numpy, if the "BLAS libraries are available as dynamically-loadable libraries". This seems not to be working on my machine, see error message.

<ul>
<li>How do I find out, if the numpy BLAS libraries are availalbe as dynamically-loadable?</li>
<li>How do I recompile the numpy BLAS libraries, if they are not dynamically-loadable?</li>
</ul>

Please indicate, if you would need more information!

Error message

<code>We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.
/usr/bin/ld: cannot find -lblas</code>

Appendix

The <a href="http://www.deeplearning.net/software/theano/" rel="noreferrer">theano</a> library needs among other things numpy and a BLAS library. I figured numpy comes with BLAS if you install it with <a href="http://www.scipy.org/Installing_SciPy/Linux#head-fb320be917b02f8fbe70e3fb2c9fe6f5f5f06fc2" rel="noreferrer"><code>sudo apt-get install python-numpy python-scipy</code></a> under Ubuntu. 

This is the filelisting of <code>/usr/lib64/python2.6/dist-packages/scipy/lib/blas</code>

<pre><code>cblas.so info.py __init__.py scons_support.py setup.py 
fblas.so info.pyc __init__.pyc scons_support.pyc setup.pyc 
setupscons.py test
setupscons.pyc
</code></pre>

This is the output of <code>distutils.__config__.show()</code> is as follows

<pre><code>blas_info:
 libraries = ['blas']
 library_dirs = ['/usr/lib64']
 language = f77

lapack_info:
 libraries = ['lapack']
 library_dirs = ['/usr/lib64']
 language = f77

atlas_threads_info:
 NOT AVAILABLE

blas_opt_info:
 libraries = ['blas']
 library_dirs = ['/usr/lib64']
 language = f77
 define_macros = [('NO_ATLAS_INFO', 1)]

atlas_blas_threads_info:
 NOT AVAILABLE

lapack_opt_info:
 libraries = ['lapack', 'blas']
 library_dirs = ['/usr/lib64']
 language = f77
 define_macros = [('NO_ATLAS_INFO', 1)]

atlas_info:
 NOT AVAILABLE

lapack_mkl_info:
 NOT AVAILABLE

blas_mkl_info:
 NOT AVAILABLE

atlas_blas_info:
 NOT AVAILABLE

mkl_info:
 NOT AVAILABLE
</code></pre>

How to make sure the numpy BLAS libraries are available as dynamically-loadable libraries?

包管理器

Ubuntu

声明，将默认使用来自numpy BLAS库，如果这些BLAS库可作为动态加载的库可用的话。这似乎不能在我的机器上工作，请参阅错误消息。如果numpy BLAS库不是dynamically-loadable?How的，我该如何找出它们是否可用为dynamically-loadable?，我该如何重新编译 BLAS库如果您...

问如何确保numpy BLAS库可以作为可动态加载的库？
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