Hi Olivier,
A very good substitute for Hadoop to consider would be discoproject
http://discoproject.org/ It's core is implemented in erlang, but the jobs
are written in python.
It's much easier to configure than Hadoop, and would be relatively easy to
parallelize the sklearn algorithms. With this sklearn could easily beat
mahout on large scale machine learning.
Cheers,
OLiver
On 26 January 2012 09:26, Olivier Grisel <[email protected]> wrote:
> 2012/1/26 Fernando Perez <[email protected]>:
> > On Wed, Jan 25, 2012 at 7:07 PM, Wes McKinney <[email protected]>
> wrote:
> >
> > Wes, you're welcome to add this to the ipython/sklearn one and turn it
> > into a 'pydata: ipython+sklearn+statsmodels' so that people can flow
> > between the three tools as desired:
> >
> > https://us.pycon.org/2012/community/sprints/projects/
> >
> >
> > You can also use our planning page if you want and adjust it accordingly:
> >
> > http://wiki.ipython.org/PyCon12Sprint
> >
> > Since we'll have enough 'core' people from each of the three projects,
> > if there are participants who want to focus on only one of them, we
> > can help them out, while the larger objectives remain:
> >
> > - ipython/sklearn integration for parallel analyses
> > - sklearn/statsmodels as per this thread.
>
> As a pandas user I would really like to take the opportunity of this
> print to work on (or at least discuss the design of) multi-core then
> distributed sort / groupby / merge as I mentioned in the original
> proposal (which is still on the ipython wiki page).
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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