Windows Scipy安装:找不到Lapack/Blas资源 [英] Windows Scipy Install: No Lapack/Blas Resources Found

问题描述

我正在尝试将python和一系列软件包安装到64位Windows 7桌面上.我已经安装了Python 3.4，已经安装了Microsoft Visual Studio C ++，并且已经成功安装了numpy，pandas和其他一些软件.尝试安装scipy时出现以下错误；

I am trying to install python and a series of packages onto a 64bit windows 7 desktop. I have installed Python 3.4, have Microsoft Visual Studio C++ installed, and have successfully installed numpy, pandas and a few others. I am getting the following error when trying to install scipy;

numpy.distutils.system_info.NotFoundError: no lapack/blas resources found

我正在离线使用pip安装，我正在使用的安装命令是;

I am using pip install offline, the install command I am using is;

pip install --no-index --find-links="S:\python\scipy 0.15.0" scipy

我已经阅读了有关要求编译器的信息，如果我正确理解的话，这是VS C ++编译器.我正在使用2010版本，就像在使用Python 3.4.这已经适用于其他软件包.

I have read the posts on here about requiring a compiler which if I understand correctly is the VS C++ compiler. I am using the 2010 version as I am using Python 3.4. This has worked for other packages.

我是否必须使用窗口二进制文件，或者有什么方法可以使pip安装正常工作?

Do I have to use the window binary or is there a way I can get pip install to work?

非常感谢您的帮助

推荐答案

此处介绍了在Windows 7 64位系统上不安装SciPy的BLAS/LAPACK库的解决方案:

The solution to the absence of BLAS/LAPACK libraries for SciPy installations on Windows 7 64-bit is described here:

http://www.scipy.org/scipylib/building/windows.html

安装Anaconda容易得多，但是如果不付费，您仍然无法获得Intel MKL或GPU的支持(它们在MKL Optimizations和Anaconda的加速附件中-我不确定他们是否使用PLASMA和MAGMA).通过MKL优化，numpy在大型矩阵计算上的性能优于IDL十倍. MATLAB内部使用Intel MKL库并支持GPU计算，因此如果他们是学生，则不妨以一定的价格使用它(MATLAB为50美元，并行计算工具箱为10美元).如果您获得了Intel Parallel Studio的免费试用版，它将附带MKL库以及C ++和FORTRAN编译器，如果您想在Windows上从MKL或ATLAS安装BLAS和LAPACK，将很方便:

Installing Anaconda is much easier, but you still don't get Intel MKL or GPU support without paying for it (they are in the MKL Optimizations and Accelerate add-ons for Anaconda - I'm not sure if they use PLASMA and MAGMA either). With MKL optimization, numpy has outperformed IDL on large matrix computations by 10-fold. MATLAB uses the Intel MKL library internally and supports GPU computing, so one might as well use that for the price if they're a student ($50 for MATLAB + $10 for the Parallel Computing Toolbox). If you get the free trial of Intel Parallel Studio, it comes with the MKL library, as well as C++ and FORTRAN compilers that will come in handy if you want to install BLAS and LAPACK from MKL or ATLAS on Windows:

http://icl.cs.utk.edu/lapack-for -windows/lapack/

Parallel Studio还带有Intel MPI库，对群集计算应用程序及其最新的Xeon处理器很有用.尽管使用MKL优化来构建BLAS和LAPACK的过程并非易事，但针对Python和R这样做的好处却是巨大的，如本英特尔网络研讨会所述:

Parallel Studio also comes with the Intel MPI library, useful for cluster computing applications and their latest Xeon processsors. While the process of building BLAS and LAPACK with MKL optimization is not trivial, the benefits of doing so for Python and R are quite large, as described in this Intel webinar:

https://software.intel.com/zh-CN/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python

Anaconda和Enthought通过使此功能和其他一些功能更易于部署而建立了业务.但是，愿意做一点工作(一点学习)的人可以免费使用它.

Anaconda and Enthought have built businesses out of making this functionality and a few other things easier to deploy. However, it is freely available to those willing to do a little work (and a little learning).

对于使用R的用户，您现在可以免费使用 R开放获得MKL优化的BLAS和LAPACK. 来自Revolution Analytics.

For those who use R, you can now get MKL optimized BLAS and LAPACK for free with R Open from Revolution Analytics.

Anaconda Python现在随附MKL优化，并且通过Intel Python发行版支持许多其他Intel库优化.但是，Accelerate库(以前称为NumbaPro)中对Anaconda的GPU支持仍然超过1万美元！最好的替代方法可能是PyCUDA和scikit-cuda，因为铜头鱼(基本上是Anaconda Accelerate的免费版本)不幸在五年前就停止了开发.如果有人要在他们离开的地方取车，可以在此处找到.

Anaconda Python now ships with MKL optimization, as well as support for a number of other Intel library optimizations through the Intel Python distribution. However, GPU support for Anaconda in the Accelerate library (formerly known as NumbaPro) is still over $10k USD! The best alternatives for that are probably PyCUDA and scikit-cuda, as copperhead (essentially a free version of Anaconda Accelerate) unfortunately ceased development five years ago. It can be found here if anybody wants to pick up where they left off.

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