Re: Using Django and R in a production environment?

[Andrew Farrell]

One option is to use the conda package manager (you can get that here
) to create an environment with
`conda create --name django+r django=1.8`
activate it with
`source activate django+r`
And then install R packages with something like
`conda install --channel https://conda.anaconda.org/r r-data.table`

You can of also use conda to install pandas and, if you want a bunch of
data science libraries at once, you can download the full Anaconda
 distribution.

On Wed, Oct 7, 2015 at 11:42 AM, Mike Covington 
wrote:

> Not sure if this approach is useful for you, but the following is how we
> use R and Django together.
>
> We use R to build Shiny apps. These are served on their default port 3838
> (which users never actually go to directly). I've made a Django app to
> manage the Shiny apps and simply place them in an iframe.
>
> Live site: http://symposium.plb.ucdavis.edu/apps/
> Django App: https://github.com/mfcovington/djangocms-shiny-app
>
> On Saturday, October 3, 2015 at 8:55:38 AM UTC-7, Chanat Praserthdam wrote:
>>
>> Hi Derek,
>>
>> Did you find the solution to your problem?
>> I ran to a similar issue.  I am wondering if RServe will work for me.
>>
>>
>> On Tuesday, April 30, 2013 at 12:40:01 PM UTC-7, Derek wrote:
>>>
>>> Thanks Nick; RStudio looks like a really good tool for development work.
>>>
>>> The impression I get though, is that the server is designed for allowing
>>> interactive R sessions over the web; whereas for us Django is the primary
>>> requirement and calls to R should happen "behind the scenes".
>>>
>>> On Tuesday, 30 April 2013 15:43:01 UTC+2, Nick Santos wrote:

 Is Django a hard and fast requirement? If so, is it just about
 integrating the routines? I think otherwise something like RStudio Server
 would work for you. I've deployed it previously and it was a pretty nice
 setup. It's based on Unix user accounts, so R processes run as if users
 were running them on a desktop, except not. You may be able to share code
 in this environment too, but I haven't played with it a ton.

 http://www.rstudio.com/ide/docs/server/getting_started

 -Nick

 On Tue, Apr 30, 2013 at 12:06 AM, Derek  wrote:

> " if you write your own R  analysis routines, why have them run in a
> web system"
>
> Two issues here:
>
> 1. We are writing R routines for someone else; who needs 'analysis on
> demand'
> 2. A web system means that multiple users in multiple locations can
> all readily access the same analysis routines (but with different data 
> sets)
>
> I agree that if you work on a desktop, with no need to access shared
> data or use the same analysis routines as anyone else, then a web 
> interface
> makes no sense.
>
> Basically we are trying to harness two disparate systems; each of
> which is very powerful in their own sphere, to create an application that
> is really useful for better and more efficient science.
>
>
> On 29 April 2013 16:59, Javier Guerra Giraldez 
> wrote:
>
>> On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
>> > hat no one is actually using R in a production environment
>> themselves (which
>> > is a little surprising to me).
>>
>>
>> well R itself is widely used in production... but the intersection
>> with Django is very small.  (after all, if you write your own R
>> analysis routines, why have them run in a web system)
>>
>> --
>> Javier
>>
>
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Re: Using Django and R in a production environment?

[Mike Covington]

Not sure if this approach is useful for you, but the following is how we 
use R and Django together.

We use R to build Shiny apps. These are served on their default port 3838 
(which users never actually go to directly). I've made a Django app to 
manage the Shiny apps and simply place them in an iframe.

Live site: http://symposium.plb.ucdavis.edu/apps/
Django App: https://github.com/mfcovington/djangocms-shiny-app

On Saturday, October 3, 2015 at 8:55:38 AM UTC-7, Chanat Praserthdam wrote:
>
> Hi Derek,
>
> Did you find the solution to your problem?
> I ran to a similar issue.  I am wondering if RServe will work for me.
>
>
> On Tuesday, April 30, 2013 at 12:40:01 PM UTC-7, Derek wrote:
>>
>> Thanks Nick; RStudio looks like a really good tool for development work.
>>
>> The impression I get though, is that the server is designed for allowing 
>> interactive R sessions over the web; whereas for us Django is the primary 
>> requirement and calls to R should happen "behind the scenes".  
>>
>> On Tuesday, 30 April 2013 15:43:01 UTC+2, Nick Santos wrote:
>>>
>>> Is Django a hard and fast requirement? If so, is it just about 
>>> integrating the routines? I think otherwise something like RStudio Server 
>>> would work for you. I've deployed it previously and it was a pretty nice 
>>> setup. It's based on Unix user accounts, so R processes run as if users 
>>> were running them on a desktop, except not. You may be able to share code 
>>> in this environment too, but I haven't played with it a ton.
>>>
>>> http://www.rstudio.com/ide/docs/server/getting_started
>>>
>>> -Nick
>>>
>>> On Tue, Apr 30, 2013 at 12:06 AM, Derek  wrote:
>>>
 " if you write your own R  analysis routines, why have them run in a 
 web system"

 Two issues here:

 1. We are writing R routines for someone else; who needs 'analysis on 
 demand'
 2. A web system means that multiple users in multiple locations can all 
 readily access the same analysis routines (but with different data sets)

 I agree that if you work on a desktop, with no need to access shared 
 data or use the same analysis routines as anyone else, then a web 
 interface 
 makes no sense.

 Basically we are trying to harness two disparate systems; each of which 
 is very powerful in their own sphere, to create an application that is 
 really useful for better and more efficient science.


 On 29 April 2013 16:59, Javier Guerra Giraldez  
 wrote:

> On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
> > hat no one is actually using R in a production environment 
> themselves (which
> > is a little surprising to me).
>
>
> well R itself is widely used in production... but the intersection
> with Django is very small.  (after all, if you write your own R
> analysis routines, why have them run in a web system)
>
> --
> Javier
>

 -- 
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>>>
>>>

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Re: Using Django and R in a production environment?

[Derek]

We have up on R altogether and switched to using pandas and related Python 
modules.

Our conclusion was that R is great for the desktop but not well suited to a 
dynamic web environment (happy to be proved wrong, of course).

On Saturday, 3 October 2015 17:55:38 UTC+2, Chanat Praserthdam wrote:
>
> Hi Derek,
>
> Did you find the solution to your problem?
> I ran to a similar issue.  I am wondering if RServe will work for me.
>
>
> On Tuesday, April 30, 2013 at 12:40:01 PM UTC-7, Derek wrote:
>>
>> Thanks Nick; RStudio looks like a really good tool for development work.
>>
>> The impression I get though, is that the server is designed for allowing 
>> interactive R sessions over the web; whereas for us Django is the primary 
>> requirement and calls to R should happen "behind the scenes".  
>>
>> On Tuesday, 30 April 2013 15:43:01 UTC+2, Nick Santos wrote:
>>>
>>> Is Django a hard and fast requirement? If so, is it just about 
>>> integrating the routines? I think otherwise something like RStudio Server 
>>> would work for you. I've deployed it previously and it was a pretty nice 
>>> setup. It's based on Unix user accounts, so R processes run as if users 
>>> were running them on a desktop, except not. You may be able to share code 
>>> in this environment too, but I haven't played with it a ton.
>>>
>>> http://www.rstudio.com/ide/docs/server/getting_started
>>>
>>> -Nick
>>>
>>> On Tue, Apr 30, 2013 at 12:06 AM, Derek  wrote:
>>>
 " if you write your own R  analysis routines, why have them run in a 
 web system"

 Two issues here:

 1. We are writing R routines for someone else; who needs 'analysis on 
 demand'
 2. A web system means that multiple users in multiple locations can all 
 readily access the same analysis routines (but with different data sets)

 I agree that if you work on a desktop, with no need to access shared 
 data or use the same analysis routines as anyone else, then a web 
 interface 
 makes no sense.

 Basically we are trying to harness two disparate systems; each of which 
 is very powerful in their own sphere, to create an application that is 
 really useful for better and more efficient science.


 On 29 April 2013 16:59, Javier Guerra Giraldez  
 wrote:

> On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
> > hat no one is actually using R in a production environment 
> themselves (which
> > is a little surprising to me).
>
>
> well R itself is widely used in production... but the intersection
> with Django is very small.  (after all, if you write your own R
> analysis routines, why have them run in a web system)
>
> --
> Javier
>

 -- 
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>>>
>>>

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Re: Using Django and R in a production environment?

[Chanat Praserthdam]

Hi Derek,

Did you find the solution to your problem?
I ran to a similar issue.  I am wondering if RServe will work for me.


On Tuesday, April 30, 2013 at 12:40:01 PM UTC-7, Derek wrote:
>
> Thanks Nick; RStudio looks like a really good tool for development work.
>
> The impression I get though, is that the server is designed for allowing 
> interactive R sessions over the web; whereas for us Django is the primary 
> requirement and calls to R should happen "behind the scenes".  
>
> On Tuesday, 30 April 2013 15:43:01 UTC+2, Nick Santos wrote:
>>
>> Is Django a hard and fast requirement? If so, is it just about 
>> integrating the routines? I think otherwise something like RStudio Server 
>> would work for you. I've deployed it previously and it was a pretty nice 
>> setup. It's based on Unix user accounts, so R processes run as if users 
>> were running them on a desktop, except not. You may be able to share code 
>> in this environment too, but I haven't played with it a ton.
>>
>> http://www.rstudio.com/ide/docs/server/getting_started
>>
>> -Nick
>>
>> On Tue, Apr 30, 2013 at 12:06 AM, Derek  wrote:
>>
>>> " if you write your own R  analysis routines, why have them run in a web 
>>> system"
>>>
>>> Two issues here:
>>>
>>> 1. We are writing R routines for someone else; who needs 'analysis on 
>>> demand'
>>> 2. A web system means that multiple users in multiple locations can all 
>>> readily access the same analysis routines (but with different data sets)
>>>
>>> I agree that if you work on a desktop, with no need to access shared 
>>> data or use the same analysis routines as anyone else, then a web interface 
>>> makes no sense.
>>>
>>> Basically we are trying to harness two disparate systems; each of which 
>>> is very powerful in their own sphere, to create an application that is 
>>> really useful for better and more efficient science.
>>>
>>>
>>> On 29 April 2013 16:59, Javier Guerra Giraldez  
>>> wrote:
>>>
 On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
 > hat no one is actually using R in a production environment themselves 
 (which
 > is a little surprising to me).


 well R itself is widely used in production... but the intersection
 with Django is very small.  (after all, if you write your own R
 analysis routines, why have them run in a web system)

 --
 Javier

>>>
>>> -- 
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>>>  
>>>  
>>>
>>
>>

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Re: Using Django and R in a production environment?

[Derek]

Thanks Nick; RStudio looks like a really good tool for development work.

The impression I get though, is that the server is designed for allowing 
interactive R sessions over the web; whereas for us Django is the primary 
requirement and calls to R should happen "behind the scenes".  

On Tuesday, 30 April 2013 15:43:01 UTC+2, Nick Santos wrote:
>
> Is Django a hard and fast requirement? If so, is it just about integrating 
> the routines? I think otherwise something like RStudio Server would work 
> for you. I've deployed it previously and it was a pretty nice setup. It's 
> based on Unix user accounts, so R processes run as if users were running 
> them on a desktop, except not. You may be able to share code in this 
> environment too, but I haven't played with it a ton.
>
> http://www.rstudio.com/ide/docs/server/getting_started
>
> -Nick
>
> On Tue, Apr 30, 2013 at 12:06 AM, Derek  >wrote:
>
>> " if you write your own R  analysis routines, why have them run in a web 
>> system"
>>
>> Two issues here:
>>
>> 1. We are writing R routines for someone else; who needs 'analysis on 
>> demand'
>> 2. A web system means that multiple users in multiple locations can all 
>> readily access the same analysis routines (but with different data sets)
>>
>> I agree that if you work on a desktop, with no need to access shared data 
>> or use the same analysis routines as anyone else, then a web interface 
>> makes no sense.
>>
>> Basically we are trying to harness two disparate systems; each of which 
>> is very powerful in their own sphere, to create an application that is 
>> really useful for better and more efficient science.
>>
>>
>> On 29 April 2013 16:59, Javier Guerra Giraldez 
>> > > wrote:
>>
>>> On Mon, Apr 29, 2013 at 2:13 AM, Derek  
>>> wrote:
>>> > hat no one is actually using R in a production environment themselves 
>>> (which
>>> > is a little surprising to me).
>>>
>>>
>>> well R itself is widely used in production... but the intersection
>>> with Django is very small.  (after all, if you write your own R
>>> analysis routines, why have them run in a web system)
>>>
>>> --
>>> Javier
>>>
>>
>>  -- 
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>> email to django-users...@googlegroups.com .
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>> .
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>> For more options, visit https://groups.google.com/groups/opt_out.
>>  
>>  
>>
>
>

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Re: Using Django and R in a production environment?

[Nick Santos]

Is Django a hard and fast requirement? If so, is it just about integrating
the routines? I think otherwise something like RStudio Server would work
for you. I've deployed it previously and it was a pretty nice setup. It's
based on Unix user accounts, so R processes run as if users were running
them on a desktop, except not. You may be able to share code in this
environment too, but I haven't played with it a ton.

http://www.rstudio.com/ide/docs/server/getting_started

-Nick

On Tue, Apr 30, 2013 at 12:06 AM, Derek  wrote:

> " if you write your own R  analysis routines, why have them run in a web
> system"
>
> Two issues here:
>
> 1. We are writing R routines for someone else; who needs 'analysis on
> demand'
> 2. A web system means that multiple users in multiple locations can all
> readily access the same analysis routines (but with different data sets)
>
> I agree that if you work on a desktop, with no need to access shared data
> or use the same analysis routines as anyone else, then a web interface
> makes no sense.
>
> Basically we are trying to harness two disparate systems; each of which is
> very powerful in their own sphere, to create an application that is really
> useful for better and more efficient science.
>
>
> On 29 April 2013 16:59, Javier Guerra Giraldez  wrote:
>
>> On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
>> > hat no one is actually using R in a production environment themselves
>> (which
>> > is a little surprising to me).
>>
>>
>> well R itself is widely used in production... but the intersection
>> with Django is very small.  (after all, if you write your own R
>> analysis routines, why have them run in a web system)
>>
>> --
>> Javier
>>
>
>  --
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>
>
>

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Re: Using Django and R in a production environment?

[Derek]

" if you write your own R  analysis routines, why have them run in a web
system"

Two issues here:

1. We are writing R routines for someone else; who needs 'analysis on
demand'
2. A web system means that multiple users in multiple locations can all
readily access the same analysis routines (but with different data sets)

I agree that if you work on a desktop, with no need to access shared data
or use the same analysis routines as anyone else, then a web interface
makes no sense.

Basically we are trying to harness two disparate systems; each of which is
very powerful in their own sphere, to create an application that is really
useful for better and more efficient science.

On 29 April 2013 16:59, Javier Guerra Giraldez  wrote:

> On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
> > hat no one is actually using R in a production environment themselves
> (which
> > is a little surprising to me).
>
>
> well R itself is widely used in production... but the intersection
> with Django is very small.  (after all, if you write your own R
> analysis routines, why have them run in a web system)
>
> --
> Javier
>

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Re: Using Django and R in a production environment?

[Javier Guerra Giraldez]

On Mon, Apr 29, 2013 at 2:13 AM, Derek  wrote:
> hat no one is actually using R in a production environment themselves (which
> is a little surprising to me).


well R itself is widely used in production... but the intersection
with Django is very small.  (after all, if you write your own R
analysis routines, why have them run in a web system)

--
Javier

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Re: Using Django and R in a production environment?

[Derek]

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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Re: Using Django and R in a production environment?

[lgautier]

> (...) You don't 
really want all your 
> users using the same R session anyways. 

Particularly is users are allowed to run their own code, as the original 
post suggested it.
A sandboxing+quota mechanism would also be necessary.


L.


On Wednesday, April 24, 2013 7:52:19 PM 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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Re: Using Django and R in a production environment?

[Alex Mandel]

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 Åstrand 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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Re: Using Django and R in a production environment?

[Derek]

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 Åstrand 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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Re: Using Django and R in a production environment?

[Per-Olof Åstrand]

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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