Windows Scipy Install: No Lapack/Blas Resources Fo

Question

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

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

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.

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

Many thanks for the help

Answer 1

My 5 cents; You can just install the entire (pre-compiled) SciPy from https://github.com/scipy/scipy/releases

Good Luck!

Answer 2

Intel now provides a Python distribution for Linux / Windows / OS X for free called "Intel distribution for Python".

Its a complete Python distribution (e.g. python.exe is included in the package) which includes some pre-installed modules compiled against Intel's MKL (Math Kernel Library) and thus optimized for faster performance.

The distribution includes the modules NumPy, SciPy, scikit-learn, pandas, matplotlib, Numba, tbb, pyDAAL, Jupyter, and others. The drawback is a bit of lateness in upgrading to more recent versions of Python. For example as of today (1 May 2017) the distribution provides CPython 3.5 while the 3.6 version is already out. But if you don't need the new features they should be perfectly fine.

Answer 3

Using resources at http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy will solve the problem. However, you should be careful about versions compatibility. After trying for several times, finally I decided to uninstall python and then installed a fresh version of python along with numpy and then installed scipy and this resolved my problem.

Answer 4

install intel's distribution of python https://software.intel.com/en-us/intel-distribution-for-python

better of for distribution of python should contain them initially

Answer 5

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

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 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 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).

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

EDIT: 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.

Answer 6

This was the order I got everything working. The second point is the most important one. Scipy needs Numpy+MKL, not just vanilla Numpy.

1. Install python 3.5
2. pip install "file path" (download Numpy+MKL wheel from here http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy)
3. pip install scipy