I am not sure I understand your question, but is it really related to using 
specifically R? Could it be any kind of heavy number-crunching that needs 
to be done in the background by a scheduler/task manager? In that case, 
django-celery may be an option: 
http://docs.celeryproject.org/en/latest/index.html

Per-Olof

On Monday, April 22, 2013 9:26:05 PM UTC+2, Derek wrote:
>
> Based on googling around this topic, it seems that using RPy2 is the most 
> common way to interface with R from Python.  However all the discussions on 
> this seem to centre around working in a desktop (single user) environment.
>
> The one discussion I could find that deals with the issue of working with 
> R "at scale" is this one - 
> https://github.com/Sleepingwell/DjangoRpyDemo/blob/master/README.md#django-configuration
>   
> - which indicates problems with this approach; and suggests it might be 
> able to be overcome via creating distinct processes dedicated to run a WSGI 
> application (although this article does not give any steps on how to do 
> this, or whether it would work in practice).
>
> Another approach seems to be to use RPy2, with Twisted to enable multiple 
> sessions: 
> https://docs.google.com/presentation/d/11LJxej6jnbYKzJftpDudYFfVKjaB0BhOzrBSKaxJ2ME/edit#slide=id.p
> .
>
> Yet another approach might be to use Rserve (http://www.rforge.net/Rserve/) 
> and PyRserve (http://pythonhosted.org/pyRserve/manual.html), but the 
> latter seems to currently be in beta.
>  
> Question is: does anyone have any practical experience actually using 
> Django with R in a production environment (i.e dozens or hundreds of users 
> doing high volume number crunching)?
>
> Thanks
> Derek
>
> PS Yes, we do need R and not one of the Python-based alternatives, as R 
> offers many routines simply not available in those as yet (also, the client 
> needs to re-use, and create new, R scripts themselves)
>

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