How to install accelerated BLAS into a Python virtualenv
Some mathematically intense operations that use Numpy/Scipy can run faster with accelerated basic linear algebra subroutine (BLAS) libraries installed on your system (e.g., gensim's corpus processing).
To see what BLAS libraries you are using, do:
python -c 'import numpy; numpy.show_config()'
If none of them are installed, you probably want to install one or
more. [ATLAS](http://math-atlas.sourceforge.net/) is always a good bet, since
it's portable and self-optimizing. There are others out there targeted at
particular CPU architectures.
Unfortunately, the [Scipy
docs](http://docs.scipy.org/doc/numpy/user/install.html) are out of date
regarding installing accelerated BLAS libraries on Ubuntu. The instructions I
have written below work for Ubuntu 10.04, the current LTS (long-term support)
version, and though I haven't tried to run them on a more recent version, it's
possible they work with those as well.
## Prereqs #
On Ubuntu 10.04, and possibly other versions, you need liblapack-dev and gfortran (yes, fortran):
Install the accelerated linear algebra libraries (ATLAS/LAPACK) in your virtualenv on Ubutu:
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#!/bin/bash workon [envname] pip uninstall numpy ## only if numpy is already installed pip uninstall scipy ## only if scipy is already installed export LAPACK=/usr/lib/liblapack.so export ATLAS=/usr/lib/libatlas.so export BLAS=/usr/lib/libblas.so
Now you can install numpy and scipy into the same virtualenv and be confident they will perform operations using the accelerated BLAS routines: