William Bert

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:

{% codeblock lang:python %}python -c ‘import numpy; numpy.show_config()’{% endcodeblock %}

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Unfortunately, the Scipy docs are out of date regarding installing accelerated BLAS libraries on Ubuntu.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):

{% codeblock lang:bash %} $ sudo apt-get install liblapack-dev $ sudo apt-get install gfortran {% endcodeblock %}

Instructions

Install the accelerated linear algebra libraries (ATLAS/LAPACK) in your virtualenv on Ubutu:

{% codeblock lang:bash %} #!/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 {% endcodeblock %}

Now you can install numpy and scipy into the same virtualenv and be confident they will perform operations using the accelerated BLAS routines:

{% codeblock lang:bash %} $ pip install numpy $ pip install scipy {% endcodeblock %}