Difference: RunningGangaWithPanda (5 vs. 6)

Revision 62009-10-30 - ThomasDoherty

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 -- ThomasDoherty - 2009-10-26

Using Ganga to submit jobs to the Panda backend

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  Data preparation reprocessing - using Ganga
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1. In a clean shell, setup Ganga.
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1. In a clean afs shell, setup Ganga.
 
  source /afs/cern.ch/sw/ganga/install/etc/setup-atlas.sh

2. Setup the athena release.

Changed:
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NOTE: To set up for any release one must be familar with using CMT (bootstrap procedures and requirement files) - see here for more information. In this case once your requirements file is set up and a directory in your test area for 14.5.2.6 is created for example (for reprocessing see the reference page above for the necessary release) then try:
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NOTE: To set up for any release one must be familar with using CMT (bootstrap procedures and requirement files) - see here for more information. In this example (for reprocessing see the reference page above for the necessary release) once the requirements file is set up and a release directory in your test area is created then try:
 
  source ~/cmthome/setup.sh -tag=14.5.2.6,32,AtlasProduction
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4. Go to run directory and start ganga.
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NOTE: For demonstration purposes (to show that this setup does indeed pull in the code changes made to a checked out package) - I have appended to a comment in AnalysisSkeleton.cxx (i.e I changed "No AOD MC truth particle container found in TDS" to "No AOD MC truth particle container found in TDS - This comment changed by Tom")

4. Go to run directory and start ganga (once the code is compiled).

 
   cd ../run
   ganga
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Added:
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or simply from the command line run ganga with the name of the Ganga JO appended:

    ganga pandaBackend_test.py
 6. You can monitor your job's progress by typing jobs inside Ganga or, if you submitted to the Panda backend by http://panda.cern.ch:25880/server/pandamon/query.

7. Once your job has finished you can copy the output data using the dq2 tools.

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Changed:
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Where "your_dataset_name" is given to you by Ganga once the job completes. And 'pandaBackend_test.py' could look like this (without line numbers):
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Where "your_dataset_name" is given to you by Ganga once the job completes. Also once the job completes Panda in particular sends you an email like this. In the email if you click on the 'PandaMonURL' link - in my case for job id 81.Then click on any of the sub-job pandaID's for example ' 1026613035' then scroll down to the link 'Find and view log files' - you can look at the Ganga log for your subjob which is named 'athena_stdout.txt' (only look at 'athena_stderr.txt if your job does not complete). I can then find that"No AOD MC truth particle container found in TDS - This comment changed by Tom" appears in this log.

The Ganga JO 'pandaBackend_test.py' could look like this (without line numbers):

 
1    j = Job()
2    j.application = Athena()
3    j.application.atlas_dbrelease = 'ddo.000001.Atlas.Ideal.DBRelease.v06060101:DBRelease-6.6.1.1.tar.gz'
Line: 88 to 98
 

Back to the Ganga JO script:

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Line 5 is set to False because we have already compiled the packages locally.
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Line 5 is set to False because we have already compiled the packages locally if you want your job to compile 
your checked out code before submitting then simply change this to True
 Line 6 tells Ganga to tar your user area and send it with the job. Line 10 specifies the backend to which you are sending your job. There are three options: LCG, Panda and NorduGrid. In the example above Panda was chosen because the data existed only in BNLPANDA,
 
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