Hi Nate,

Has there been any progress on this? This enhancement would actually be very 
useful for our local Galaxy instance.

Cheers,
Tony

-----Original Message-----
From: galaxy-dev-boun...@lists.bx.psu.edu 
[mailto:galaxy-dev-boun...@lists.bx.psu.edu] On Behalf Of Nate Coraor
Sent: Wednesday, January 25, 2012 10:56 AM
To: John Chilton
Cc: galaxy-dev@lists.bx.psu.edu
Subject: Re: [galaxy-dev] Defining Job Runners Dynamically

Hey John,

This hasn't been forgotten.  I appreciate the code submission, and will review 
it as soon as possible.  I also created a relevant issue in Bitbucket:

https://bitbucket.org/galaxy/galaxy-central/issue/709/add-more-control-over-where-jobs-run

--nate

On Oct 15, 2011, at 10:42 PM, John Chilton wrote:

> Hello All,
> 
>   I just issued a pull request that augments Galaxy to allow defining job 
> runners dynamically at runtime 
> (https://bitbucket.org/galaxy/galaxy-central/pull-request/12/dynamic-job-runners).
>  Whether it makes the cut or not, I thought I would describe enhancements 
> here in case anyone else would find it useful.
> 
>   There a couple use cases we hope this will help us address for our 
> institution - one is dynamically switching queues based on user (we have a 
> very nice shared memory resource that can only be used by researchers with 
> NIH funding) and the other is inspecting input sizes to give more accurate 
> max walltimes to pbs (a small number of cufflinks jobs for instance take over 
> three days on our cluster but defining max walltimes in excess of that for 
> all jobs could result in our queue sitting idle around our monthly 
> downtimes). You might also imagine using this to dynamically switch queues 
> entirely based on input sizes or parameters, or alter queue priorities based 
> on the submitting user or input sizes/parameters.
> 
>   There are two steps to use this - you must add a line in universe.ini and 
> define a function to compute the true job runner string in the new file 
> lib/galaxy/jobs/rules.py. 
> 
>   This first step is similar to what you would do to statically assign a tool 
> to a particular job runner. If you would like to dynamically assign a job 
> runner for cufflinks you would start by adding a line like one of the 
> following to universe.ini
> 
> cufflinks = dynamic:///python
> -or-
> cufflinks = dynamic:///python/compute_runner
> 
> If you use the first form, a function called cufflinks must be defined in 
> rules.py, adding the extra argument after python/  lets you specify a 
> particular function by name (compute_runner in this example). This second 
> option could let you assign job runners with the same function for multiple 
> tools.
> 
> The only other step is to define a python function in rules.py that produces 
> a string corresponding to a valid job runner such as "local:///" or 
> "pbs:///queue/-l walltime=48:00:00/". 
> 
> If the functions defined in this file take in arguments, these arguments 
> should have names from the follow list: job_wrapper, user_email, app, job, 
> tool, tool_id, job_id, user. The plumbing will map these arguments to the 
> implied galaxy object. For instance, job_wrapper is the JobWrapper instance 
> for the job that gets passed to the job runner, user_email is the user's 
> email address or None, app is the main application configuration object used 
> throughout the code base that can be used for instance to get values defined 
> in universe.ini, job, tool, and user are model objects, and job_id and 
> tool_id the relevant ids.
> 
> If you are writing a function that routes a certain list of users to a 
> particular queue or increases their priority, you will probably only need to 
> take in one argument - user_email. However, if you are going to look at input 
> file sizes you may want to take in an argument called job and use the 
> following piece of code to find the input size for input named "input1" in 
> the tool xml. 
> 
>     inp_data = dict( [ ( da.name, da.dataset ) for da in job.input_datasets ] 
> )
>     inp_data.update( [ ( da.name, da.dataset ) for da in 
> job.input_library_datasets ] )
>     input1_file = inp_data[ "input1" ].file_name
>     input1_size = os.path.getsize( input1_file )
> 
> This whole concept works for a couple of small tests on my local machine, but 
> there are certain aspects of the job runner code that makes me feel there may 
> be corner cases I am not seeing where this approach may not work - so your 
> millage may vary.
> 
> -John
> 
> ------------------------------------------------ 
> John Chilton 
> Software Developer 
> University of Minnesota Supercomputing Institute 
> Office: 612-625-0917 
> Cell: 612-226-9223 
> E-Mail: chil...@msi.umn.edu 
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