Re: Using Django and R in a production environment?

Again, thanks!

But it still seems that no one is actually using R in a production environment themselves (which is a little surprising to me).

On Wednesday, 24 April 2013 19:52:19 UTC+2, Alex wrote:

There is another large potential gotcha, R is very memory heavy.
I do think the route of using Celery or other job management tools makes
sense, especially if you can use R across multiple backend machines.
Would celery mean one rpy2 per celery? You don't really want all your
users using the same R session anyways.

Thanks,
Alex

On 04/23/2013 11:08 PM, Derek wrote:
> Thanks Per-Olof
>
> No, it has more to do with the issue raised here:
> https://github.com/Sleepingwell/DjangoRpyDemo/blob/master/README.md#django-configuration
>
> Possibly Celery could solve that (?) but I really would like to hear from
> someone who actually has a production configuration set up and working.
> Perhaps there are less people in the sciences using Django than I thought...
>
> Derek
>
> On Tuesday, 23 April 2013 21:32:12 UTC+2, Per-Olof �strand wrote:
>>
>> 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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