Background

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:

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python -c 'import numpy; numpy.show_config()'

If none of them are installed, you probably want to install one or more. ATLAS 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Unfortunately, the Scipy docs are out of date regarding installing accelerated BLAS libraries on Ubuntu.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):

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$ sudo apt-get install liblapack-dev
$ sudo apt-get install gfortran

Instructions

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:

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$ pip install numpy
$ pip install scipy