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DOC: Added documentation changes to reflect the addtion of the OAR scheduler plugin
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Next Release
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============
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* ENH: Added an OAR scheduler plugin (https://github.com/nipy/nipype/pull/1259)
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* API: Default model level for the bedpostx workflow has been set to "2" following FSL 5.0.9 lead
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* ENH: New interfaces for interacting with AWS S3: S3DataSink and S3DataGrabber (https://github.com/nipy/nipype/pull/1201)
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doc/users/plugins.rst

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@@ -9,7 +9,7 @@ available plugins allow local and distributed execution of workflows and
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debugging. Each available plugin is described below.
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Current plugins are available for Linear, Multiprocessing, IPython_ distributed
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processing platforms and for direct processing on SGE_, PBS_, HTCondor_, LSF_, and SLURM_. We
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processing platforms and for direct processing on SGE_, PBS_, HTCondor_, LSF_, OAR_, and SLURM_. We
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anticipate future plugins for the Soma_ workflow.
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.. note::
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wrapper_args=shim_args)
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)
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OAR
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---
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In order to use nipype with OAR_ you simply need to call::
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workflow.run(plugin='OAR')
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Optional arguments::
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template: custom template file to use
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oar_args: any other command line args to be passed to qsub.
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max_jobname_len: (PBS only) maximum length of the job name. Default 15.
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For example, the following snippet executes the workflow on myqueue with
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a custom template::
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workflow.run(plugin='oar',
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plugin_args=dict(template='mytemplate.sh', oarsub_args='-q myqueue')
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In addition to overall workflow configuration, you can use node level
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configuration for OAR::
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node.plugin_args = {'oarsub_args': '-l "nodes=1/cores=3"'}
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this would apply only to the node and is useful in situations, where a
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particular node might use more resources than other nodes in a workflow.
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``qsub`` emulation
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~~~~~~~~~~~~~~~~~~
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