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# Using Nipype Plugins

The workflow engine supports a plugin architecture for workflow execution. The available plugins allow local and distributed execution of workflows and debugging. Each available plugin is described below.

Current plugins are available for Linear, Multiprocessing, IPython distributed processing platforms and for direct processing on SGE, PBS, HTCondor, LSF, and SLURM. We anticipate future plugins for the Soma workflow.

Note

The current distributed processing plugins rely on the availability of a shared filesystem across computational nodes.

A variety of config options can control how execution behaves in this distributed context. These are listed later on in this page.

All plugins can be executed with:

    workflow.run(plugin=PLUGIN_NAME, plugin_args=ARGS_DICT)

Optional arguments:

    status_callback : a function handle
    max_jobs : maximum number of concurrent jobs
    max_tries : number of times to try submitting a job
    retry_timeout : amount of time to wait between tries

Note

Except for the status_callback, the remaining arguments only apply to the distributed plugins: MultiProc/IPython(X)/SGE/PBS/HTCondor/HTCondorDAGMan/LSF

For example:

## Plugins

### Debug

This plugin provides a simple mechanism to debug certain components of a workflow without executing any node.

Mandatory arguments:

    callable :  A function handle that receives as arguments a node and a graph

The function callable will called for every node from a topological sort of the execution graph.

### Linear

This plugin runs the workflow one node at a time in a single process locally. The order of the nodes is determined by a topological sort of the workflow:

    workflow.run(plugin='Linear')

### MultiProc

Uses the Python multiprocessing library to distribute jobs as new processes on a local system.

Optional arguments:

    n_procs :  Number of processes to launch in parallel, if not set number of
    processors/threads will be automatically detected

To distribute processing on a multicore machine, simply call:

    workflow.run(plugin='MultiProc')

This will use all available CPUs. If on the other hand you would like to restrict the number of used resources (to say 2 CPUs), you can call:

    workflow.run(plugin='MultiProc', plugin_args={'n_procs' : 2}

### IPython

This plugin provide access to distributed computing using IPython parallel machinery.

Note

We provide backward compatibility with IPython versions earlier than 0.10.1 using the IPythonX plugin.

Please read the IPython documentation to determine how to setup your cluster for distributed processing. This typically involves calling ipcluster.

Once the clients have been started, any pipeline executed with:

    workflow.run(plugin='IPython')

### SGE/PBS

In order to use nipype with SGE or PBS you simply need to call:

    workflow.run(plugin='SGE')
    workflow.run(plugin='PBS')

Optional arguments:

    template: custom template file to use
    qsub_args: any other command line args to be passed to qsub.
    max_jobname_len: (PBS only) maximum length of the job name.  Default 15.

For example, the following snippet executes the workflow on myqueue with a custom template:

    workflow.run(plugin='SGE',
       plugin_args=dict(template='mytemplate.sh', qsub_args='-q myqueue')

In addition to overall workflow configuration, you can use node level configuration for PBS/SGE:

    node.plugin_args = {'qsub_args': '-l nodes=1:ppn=3'}

this would apply only to the node and is useful in situations, where a particular node might use more resources than other nodes in a workflow.

Note

Setting the keyword overwrite would overwrite any global configuration with this local configuration:

    node.plugin_args = {'qsub_args': '-l nodes=1:ppn=3', 'overwrite': True}

### LSF

Submitting via LSF is almost identical to SGE above:

> workflow.run(plugin=’LSF’)

Optional arguments:

    template: custom template file to use
    bsub_args: any other command line args to be passed to bsub.

### HTCondor

#### DAGMan

With its DAGMan component HTCondor (previously Condor) allows for submitting entire graphs of dependent jobs at once. With the `CondorDAGMan` plug-in Nipype can utilize this functionality to submit complete workflows directly and in a single step. Consequently, and in contrast to other plug-ins, workflow execution returns almost instantaneously – Nipype is only used to generate the workflow graph, while job scheduling and dependency resolution are entirely managed by HTCondor.

Please note that although DAGMan supports specification of data dependencies as well as data provisioning on compute nodes this functionality is currently not supported by this plug-in. As with all other batch systems supported by Nipype, only HTCondor pools with a shared file system can be used to process Nipype workflows.

Workflow execution with HTCondor DAGMan is done by calling:

    workflow.run(plugin='CondorDAGMan')

Job execution behavior can be tweaked with the following optional plug-in arguments. The value of most arguments can be a literal string or a filename, where in the latter case the content of the file will be used as the argument value:

    submit_template : submit spec template for individual jobs in a DAG (see
                 CondorDAGManPlugin.default_submit_template for the default.
    initial_specs : additional submit specs that are prepended to any job's
                 submit file
    override_specs : additional submit specs that are appended to any job's
                 submit file
    wrapper_cmd : path to an exectuable that will be started instead of a node
                 script. This is useful for wrapper script that execute certain
                 functionality prior or after a node runs. If this option is
                 given the wrapper command is called with the respective Python
                 exectuable and the path to the node script as final arguments
    wrapper_args : optional additional arguments to a wrapper command
    dagman_args : arguments to be prepended to the job execution script in the
                  dagman call
    block : if True the plugin call will block until Condor has finished
            prcoessing the entire workflow (default: False)

Please see the HTCondor documentation for details on possible configuration options and command line arguments.

Using the `wrapper_cmd` argument it is possible to combine Nipype workflow execution with checkpoint/migration functionality offered by, for example, DMTCP. This is especially useful in the case of workflows with long running nodes, such as Freesurfer’s recon-all pipeline, where Condor’s job prioritization algorithm could lead to jobs being evicted from compute nodes in order to maximize overall troughput. With checkpoint/migration enabled such a job would be checkpointed prior eviction and resume work from the checkpointed state after being rescheduled – instead of restarting from scratch.

On a Debian system, executing a workflow with support for checkpoint/migration for all nodes could look like this:

    # define common parameters
    dmtcp_hdr = """
    should_transfer_files = YES
    when_to_transfer_output = ON_EXIT_OR_EVICT
    kill_sig = 2
    environment = DMTCP_TMPDIR=./;JALIB_STDERR_PATH=/dev/null;DMTCP_PREFIX_ID=$(CLUSTER)_$(PROCESS)
    """
    shim_args = "--log %(basename)s.shimlog --stdout %(basename)s.shimout --stderr %(basename)s.shimerr"
    # run workflow
    workflow.run(
          plugin='CondorDAGMan',
          plugin_args=dict(initial_specs=dmtcp_hdr,
                           wrapper_cmd='/usr/lib/condor/shim_dmtcp',
                           wrapper_args=shim_args)
          )

#### `qsub` emulation

Note

This plug-in is deprecated and users should migrate to the more robust and more versatile `CondorDAGMan` plug-in.

Despite the differences between HTCondor and SGE-like batch systems the plugin usage (incl. supported arguments) is almost identical. The HTCondor plugin relies on a `qsub` emulation script for HTCondor, called `condor_qsub` that can be obtained from a Git repository on git.debian.org. This script is currently not shipped with a standard HTCondor distribution, but is included in the HTCondor package from http://neuro.debian.net. It is sufficient to download this script and install it in any location on a system that is included in the `PATH` configuration.

Running a workflow in a HTCondor pool is done by calling:

    workflow.run(plugin='Condor')

The plugin supports a limited set of qsub arguments (`qsub_args`) that cover the most common use cases. The `condor_qsub` emulation script translates qsub arguments into the corresponding HTCondor terminology and handles the actual job submission. For details on supported options see the manpage of `condor_qsub`.

Optional arguments:

    qsub_args: any other command line args to be passed to condor_qsub.