Windows Scipy Install: No Lapack/Blas Resources Found

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.

Error in installing scipy on Windows 10

Thankyou for your answers.

But I found a better solution to this.
My problem got resolved by downloading numpy wheel file, scipy wheel file and the scikit-learn wheel file and installing it the same way as we install any other python file.

Trouble installing scipy via pyCharm windows 8 - no lapack / blas resources found

As long as you're using the python.org version(s) of Python, the easiest way to install packages is to first check if they are in Christoph Gohlke's Python Extension Packages for Windows repository. There are pre-compiled packages for both numpy and scipy, along with many many others. You'll need to install numpy from there, as it is statically-linked to Intel's MKL, and is a necessary dependency for many of the other packages there, including scipy.

Scipy error in python:3.8-alpine3.11 - No lapack/blas resources found

I later used apk add py3-scipy, and it worked.

pip install scipy was throwing error in alpine because of missing of lapack package.
py3-scipy installing with all the dependent package.

lapack/blas resources not found when installing matplotlib

It appears that your setuptools are not properly installed. Try this:

python -m pip install --upgrade pip setuptools wheel

And then again try installing matplotlib.

What is the easiest way to install BLAS and LAPACK for scipy?

The SciPy installation page already recommends several ways of installing python with SciPy already included, such as WinPython.

Another way is to use wheels (a built-package format):

pip install SomePackage-1.0-py2.py3-none-any.whl

The wheel packages you can find on: http://www.lfd.uci.edu/~gohlke/pythonlibs/

For SciPy you need:

the NumPy wheel packages
and the SciPy wheel packages

Windows Scipy Install: No Lapack/Blas Resources Found