Does Python Scipy Need Blas

Does Python SciPy need BLAS?

The SciPy webpage used to provide build and installation instructions, but the instructions there now rely on OS binary distributions. To build SciPy (and NumPy) on operating systems without precompiled packages of the required libraries, you must build and then statically link to the Fortran libraries BLAS and LAPACK:

mkdir -p ~/src/
cd ~/src/
wget http://www.netlib.org/blas/blas.tgz
tar xzf blas.tgz
cd BLAS-*

## NOTE: The selected Fortran compiler must be consistent for BLAS, LAPACK, NumPy, and SciPy.
## For GNU compiler on 32-bit systems:
#g77 -O2 -fno-second-underscore -c *.f                     # with g77
#gfortran -O2 -std=legacy -fno-second-underscore -c *.f    # with gfortran
## OR for GNU compiler on 64-bit systems:
#g77 -O3 -m64 -fno-second-underscore -fPIC -c *.f                     # with g77
gfortran -O3 -std=legacy -m64 -fno-second-underscore -fPIC -c *.f    # with gfortran
## OR for Intel compiler:
#ifort -FI -w90 -w95 -cm -O3 -unroll -c *.f

# Continue below irrespective of compiler:
ar r libfblas.a *.o
ranlib libfblas.a
rm -rf *.o
export BLAS=~/src/BLAS-*/libfblas.a

Execute only one of the five g77/gfortran/ifort commands. I have commented out all, but the gfortran which I use. The subsequent LAPACK installation requires a Fortran 90 compiler, and since both installs should use the same Fortran compiler, g77 should not be used for BLAS.

Next, you'll need to install the LAPACK stuff. The SciPy webpage's instructions helped me here as well, but I had to modify them to suit my environment:

mkdir -p ~/src
cd ~/src/
wget http://www.netlib.org/lapack/lapack.tgz
tar xzf lapack.tgz
cd lapack-*/
cp INSTALL/make.inc.gfortran make.inc          # On Linux with lapack-3.2.1 or newer
make lapacklib
make clean
export LAPACK=~/src/lapack-*/liblapack.a

Update on 3-Sep-2015:
Verified some comments today (thanks to all): Before running make lapacklib edit the make.inc file and add -fPIC option to OPTS and NOOPT settings. If you are on a 64bit architecture or want to compile for one, also add -m64. It is important that BLAS and LAPACK are compiled with these options set to the same values. If you forget the -fPIC SciPy will actually give you an error about missing symbols and will recommend this switch. The specific section of make.inc looks like this in my setup:

FORTRAN  = gfortran 
OPTS     = -O2 -frecursive -fPIC -m64
DRVOPTS  = $(OPTS)
NOOPT    = -O0 -frecursive -fPIC -m64
LOADER   = gfortran

On old machines (e.g. RedHat 5), gfortran might be installed in an older version (e.g. 4.1.2) and does not understand option -frecursive. Simply remove it from the make.inc file in such cases.

The lapack test target of the Makefile fails in my setup because it cannot find the blas libraries. If you are thorough you can temporarily move the blas library to the specified location to test the lapack. I'm a lazy person, so I trust the devs to have it working and verify only in SciPy.

Python scipy needs BLAS

As far as I can tell, you cannot use the setup.py to install system libraries. This mechanism is only thought for Python packages and their C extensions (which may define their own dependencies on C shared libraries, but this is not a solution here).

There are a number of solutions you may try but they are all outside the setup.py file:

• create an installer (RPM, DEB or Windows package) where you define your dependency on the Atlas package. The system package manager (yum, apt-get, hmmm... nothing on Windows) is then in charge of finding the lib and installing it for your package.
  
  • RPM: remember you get a bdist_rpm command from the setup.py directly
  • Windows: remember you also get a bdist_msi command from the setup.py
  • DEB: the stdeb package create a bdist_deb command
• use buildout as build mechanism
  
  • define Atlas as a part
  • use the cmmi recipe in order to download, configure, make and "make install" the Atlas tarball
  • set the os.environ ATLAS variable to the parts directory of your buildout inside your setup.py before you define your package's setup.
• simply list Atlas as a dependency of your package and let your users install it. Keep the scipy dependency within your setup.py; that is the correct place, since it is a Python package.

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.

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

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.

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