Using GANGA with AMAAthena
Introduction
This document gives an step-by-step instruction for running AMAAthena within GANGA on a NIKHEF desktop (e.g. ribble.nikhef.nl). AMAAthena is an Athena package providing a framework for modular analysis. GANGA is an official ATLAS grid utility for distributed data analysis.
Preparation
- follow the AMAAthena guide to setup your cmthome directory
- checkout the AMAAthena package from CVS
- make sure you will start GANGA with a clear environment without any Athena and CMT setup
Starting GANGA
Typing the following commands within the directory: PhysicsAnalysis/AnalysisCommon/AMA/AMAAthena/cmt
% source /project/atlas/nikhef/dq2/dq2_setup.sh.NIKHEF % export DPNS_HOST=tbn18.nikhef.nl % export LFC_HOST=lfc-atlas.grid.sara.nl % source /project/atlas/nikhef/ganga/etc/setup.[c]sh % ganga --config-path=/project/atlas/nikhef/ganga/config/Atlas.ini.nikhef
GANGA magic functions for cmtsetup
Inside GANGA, one could deal with the complex CMT setup with two magic functions.
The following example shows how to setup the CMT environment for Athena 14.2.0 in 32 bit mode.
In [n]: config.Athena.CMTHOME = '/your/cmthome' In [n]: cmtsetup 14.2.0,32 In [n]: setup
Running AMAAthena in GANGA
The example below assumes:
-
Users have the following Athena job option files in the run directory of the AMAAthena package
-
AMAAthena_jobOptions.py
Trigger_jobOptions.py
-
exampleaod.conf
reader.conf
Creating new GANGA job
In [n]: j = Job()
Setting application
In [n]: j.application = AMAAthena() In [n]: j.application.option_files += [ File('../run/AMAAthena_jobOptions.py'), File('../run/Trigger_jobOptions.py') ] In [n]: j.application.driver_config.config_file = File('../run/exampleaod.conf') In [n]: j.application.driver_config.include_file += [ File('../run/reader.conf') ] In [n]: j.application.prepare()
Setting input data
-
StagerDataset
When using the StagerDataset, the AMAAthena job will use the Athena FileStager to copy dataset files from a grid storage.
In [n]: j.inputdata = StagerDataset() In [n]: j.inputdata.dataset += [ 'fdr08_run2.0052280.physics_Muon.merge.AOD.o3_f8_m10' ]DQ2Dataset When using the DQ2Dataset, GANGA will handle the file access externally from Athena.
In [n]: j.inputdata = DQ2Dataset() In [n]: j.inputdata.dataset += [ 'fdr08_run2.0052280.physics_Muon.merge.AOD.o3_f8_m10' ] In [n]: j.inputdata.type = 'DQ2_DOWNLOAD'
Setting job splitter (optional)
The examples below ask each subjob to process on 2 files in maximum.
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using StagerJobSplitter with StagerDataset
In [n]: j.splitter = StagerJobSplitter() In [n]: j.splitter.numfiles = 2using DQ2JobSplitter with DQ2Dataset
In [n]: j.splitter = DQ2JobSplitter() In [n]: j.splitter.numfiles = 2
Setting computing backend
-
using Stoomboot cluster
In [n]: j.backend = PBS()using LCG
In [n]: j.backend = LCG()
Submitting job
In [n]: j.submit()
After job submission
Checking job status
GANGA automatically polls the up-to-date status of your jobs and updates local repository accordingly. A notification will pop up to the user when the job status is changed.
In addition, you can get a job summary table by:
In [n]: jobs
or a summary table for subjobs:
In [n]: j.subjobs
Result and output merging
For the moment, the completed (sub-)job returns an root summary file. The file is stored in the summary sub-directory in the job's output directory.
For jobs using StagerJobSplitter, the RootMerger is automatically assigned to the job so that when the whole job is completed, the summary root files from subjobs are merged together.
For jobs using DQ2Dataset, the merging process can be done manually when the whole job is completed. For example, assuming each subjob produces a root summary file called summary/summary_mySample_confFile_exampleaod.conf_nEvts_1000.root. To merge them, one can do:
In [n]: merger = RootMerger() In [n]: merger.files += ['summary/summary_mySample_confFile_exampleaod.conf_nEvts_1000.root'] In [n]: merger.overwrite = True In [n]: merger.ignorefailed = True In [n]: merger.merge(j)
The merged root file with the same name will be created in the job's outputdir.
Killing and removing jobs
You can kill a job by calling
In [n]: j.kill()
or remove a job by
In [n]: j.remove()
Advanced usage
More information
Known issues/ToDo items
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StagerDataset not supported for jobs on LCG