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10-davinci-grid.md

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First Steps in LHCb
Running DaVinci on the grid
10

Learning Objectives {.objectives}

  • Create a ganga job
  • Submit a ganga job
  • Waiting for ganga
  • Find the job output

This lesson will teach you how to take our minimal DaVinci job and run it on the grid.

ganga is a program which you can use to interact with your grid jobs. Start it with:

$ SetupProject Ganga
$ ganga

After ganga has started you will be dropped into something that looks very much like an ipython session. ganga is built on top of ipython so you can type anything that is legal python in addition to some special commands provided by ganga.

To create your first ganga job type the following:

j = Job(application=DaVinci(version='v36r6'))
j.backend = Dirac()
j.name = 'First ganga job'
j.inputdata = j.application.readInputData('data/MC_2012_27163003_Beam4000GeV2012MagDownNu2.5Pythia8_Sim08e_Digi13_Trig0x409f0045_Reco14a_Stripping20NoPrescalingFlagged_ALLSTREAMS.DST.py')
j.application.optsfile = 'code/11-davinci-grid/ntuple_options_grid.py'

This will create a Job object that will execute DaVinci using a backend called Dirac, which is "the grid". Instead of specifying the files to process as part of the options file you have now tell the Job about it. This means allows ganga to split your job up, processing different files simultaneously. The final property to set is the j.application.optsfile which specifies the options file to use to configure the job.

Now you have created your first job, however it has not started running yet. To submit it type j.submit(). Now ganga will do the equivalent of SetupProject DaVinci v36r6, prepare your job and then ship it off to the grid.

While it runs, let's submit an identical job via slightly different method. Having to type in the details of each job every time you want to run it is error prone and tedious. Instead you can place all the lines that define a job in a file and simply run that.

Place the following in a file called first-job.py:

j = Job(application=DaVinci(version='v36r6'))
j.backend = Dirac()
j.name = 'First ganga job'
j.inputdata = j.application.readInputData('data/MC_2012_27163003_Beam4000GeV2012MagDownNu2.5Pythia8_Sim08e_Digi13_Trig0x409f0045_Reco14a_Stripping20NoPrescalingFlagged_ALLSTREAMS.DST.py')
j.application.optsfile = 'code/11-davinci-grid/ntuple_options_grid.py'
j.submit()

Which you can execute and submit like so, from within a ganga session:

%ganga first-job.py

This will print output similar to submitting the job from with in ganga.

You can check on your jobs by typing jobs into a ganga console. This will list all of your jobs, their status, what kind of application they are and more.

You can get more detailed information about your job by typing jobs($jobid). Replacing $jobid with the id of the job you are interested in. For concretness we will assume you are interested in a job with jobid 787 in this example.

Once your job has finished its status will be completed. Check this by typing jobs or by printing out the status of one particular job:

print 'Status of my job:', jobs(787).status

The next thing to do is to find the output of your job. Two things can happen to files your job creates:

  • They get downloaded by ganga, or
  • they are stored "on the grid".

By default ganga will download most files below a size of XX MB. The rest will remain on the grid. Log files will almost always be downloaded.

To find where the files ganga downloaded are you can check the outputdir property of your job.

output = job(787).outputdir
print 'Job output stored in:', output

Take a look at the contents of this directory.

Using the Shell from IPython {.callout}

IPython lets you execute shell commands from within the ganga session. This means you can list the contents of a directory without leaving ganga by typing !ls /tmp/. This will list the contents of the /tmp directory. In our case we can use this to list the contents of the job output directory with !ls $output as we stored the path in the variable output.

To look at the root file produced by the job start a new terminal, and type:

$ lb-run DaVinci v36r6 $SHELL
$ root -l path/to/the/job/output

You need to setup DaVinci as we need ROOT version 6 to read the nTuple.

Getting help with ganga {.callout}

To find out more take a look at the Ganga FAQ