Windows Scipy 安装:未找到 Lapack/Blas 资源 [英] Windows Scipy Install: No Lapack/Blas Resources Found

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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,安装了 Microsoft Visual Studio C++,并成功安装了 numpy、pandas 和其他一些.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

我离线使用pip install,我使用的安装命令是;

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

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

我已经阅读了这里关于需要编译器的帖子,如果我理解正确的话就是 VS C++ 编译器.我使用的是 2010 版本,因为我使用的是 Python 3.4.这适用于其他软件包.

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 windows 二进制文件,或者有什么方法可以让 pip install 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

此处描述了在 Windows 7 64 位上安装 SciPy 时缺少 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,但您仍然无法在不付费的情况下获得 Intel MKL 或 GPU 支持(它们位于 Anaconda 的 MKL 优化和加速附加组件中 - 我不确定它们是否使用 PLASMA 和岩浆).通过 MKL 优化,numpy 在大型矩阵计算上的性能比 IDL 高 10 倍.MATLAB 在内部使用 Intel MKL 库并支持 GPU 计算,因此如果他们是学生,不妨以这个价格使用它(MATLAB 50 美元 + Parallel Computing Toolbox 10 美元).如果您获得英特尔 Parallel Studio 的免费试用版,它会附带 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,这些编译器会派上用场:

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 还附带英特尔 MPI 库,可用于集群计算应用程序及其最新的至强处理器.虽然使用 MKL 优化构建 BLAS 和 LAPACK 的过程并非微不足道,但如本英特尔网络研讨会所述,这样做对 Python 和 R 的好处非常大:

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/en-us/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更易于部署的功能建立了业务.但是,对于那些愿意做一些工作(和一些学习)的人来说,它是免费的.

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).

对于那些使用 R 的人,您现在可以通过 R Open 免费获得 MKL 优化的 BLAS 和 LAPACK 来自革命分析.

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

Anaconda Python 现在附带 MKL 优化,并通过英特尔 Python 发行版支持许多其他英特尔库优化.但是,Accelerate 库(以前称为 NumbaPro)中对 Anaconda 的 GPU 支持仍然超过 1 万美元!最好的替代方案可能是 PyCUDA 和 scikit-cuda,因为 Copperhead(本质上是 Anaconda Accelerate 的免费版本)不幸在五年前停止了开发.可以在这里找到它,如果有人想从他们上次停下的地方继续.

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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