No, this is not configurable via XML; you'd have to extend the Galaxy codebase 
to implement this behavior.

Best,
J.

On Jun 1, 2012, at 1:50 PM, Anthonius deBoer wrote:

> Hi Jeremy,
> 
> Thanks for the info...But it's not clear from your message if I could use 
> this Key/Value approach at the moment to distinguish if a job should be run 
> in the fast or in the slow queue.
> 
> I would like to add a parameter to every tool that would have it determine if 
> it should be in the fast queue or in the slow queue...
> It would be checked for interactive jobs and if someone created a workflow 
> with this tool, they could turn it off and it would run in the slow/high 
> memory queue....
> Could I add this today and what would the XML look like or are you saying it 
> only works for the trackster example you gave...
> 
> Thanks
> 
> Thon
> 
> On Jun 01, 2012, at 05:44 AM, Jeremy Goecks <jeremy.goe...@emory.edu> wrote:
> 
>> >> Is there a way for a tool to sometimes be placed in the fast queue and
>> >> sometimes in the long queue?
>> > 
>> > Not through Galaxy as far as I know.
>> 
>> Yes, this is possible using job parameterization. From universe.ini.sample:
>> 
>> --
>> # Per-tool job handler and runner overrides. Parameters can be included to 
>> define multiple
>> # runners per tool. E.g. to run Cufflinks jobs initiated from Trackster
>> # differently than standard Cufflinks jobs:
>> #
>> # cufflinks = local:///
>> # cufflinks[source@trackster] = local:///
>> --
>> 
>> This approach is definitely a beta feature, but the idea is that any set of 
>> key@value parameters should be able to be used to direct jobs to different 
>> queues as needed. 
>> 
>> Job parameterization is done in only one place right now, the tracks.py 
>> controller in rerun_tool The idea is that jobs run via Trackster are short, 
>> so they get a different queue:
>> 
>> --
>> subset_job, subset_job_outputs = tool.execute( trans, incoming=tool_params, 
>> history=target_history, 
>> job_params={ "source" : "trackster" } )
>> --
>> 
>> 
>> > Right now I'd like to be able to allocate jobs to different queues
>> > based on the input data size (and thus the expected compute time
>> > and resource needed), but that is rather complicated. e.g. If you
>> > have a low memory queue and a high memory query.
>> 
>> To make this work, you'd want to modify the execute() method in the 
>> DefaultToolAction class (/lib/galaxy/tools/actions/__init__.py) to add job 
>> parameters based on either tool parameters and/or input dataset size.
>> 
>> > You might even want different queues according to the user
>> > (e.g. one group might have paid for part of the cluster and get
>> > priority access).
>> 
>> This could also be done in the same location as trans.user will give you the 
>> user running the tool/job.
>> 
>> Best,
>> J.
>> 

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