Metadata-Version: 1.0
Name: ipython-cluster-helper
Version: 0.3.0
Summary: Simplify IPython cluster start up and use for multiple schedulers.
Home-page: https://github.com/roryk/ipython-cluster-helper
Author: Rory Kirchner
Author-email: rory.kirchner@gmail.com
License: MIT
Description: ipython-cluster-helper
        ======================
        
        Quickly and easily parallelize Python functions using IPython on a
        cluster, supporting multiple schedulers. Optimizes IPython defaults to
        handle larger clusters and simultaneous processes.
        
        Example
        -------
        
        Lets say you wrote a program that takes several files in as arguments
        and performs some kind of long running computation on them. Your
        original implementation used a loop but it was way too slow::
        
            from yourmodule import long_running_function
            import sys
        
            if __name__ == "__main__":
                for f in sys.argv[1:]:
                    long_running_function(f)
        
        If you have access to one of the supported schedulers you can easily
        parallelize your program across 5 nodes with ipython-cluster-helper::
        
            from cluster_helper.cluster import cluster_view
            from yourmodule import long_running_function
            import sys
        
            if __name__ == "__main__":
                with cluster_view(scheduler="lsf", queue="hsph", num_jobs=5) as view:
                    view.map(long_running_function, sys.argv[1:])
        
        That's it! No setup required.
        
        To run a local cluster for testing purposes pass `run_local` as an extra
        parameter to the cluster_view function::
        
            with cluster_view(scheduler=None, queue=None, num_jobs=5,
                              extra_params={"run_local": True}) as view:
                view.map(long_running_function, sys.argv[1:])
        
        How it works
        ------------
        
        ipython-cluster-helper creates a throwaway parallel IPython profile,
        launches a cluster and returns a view. On program exit it shuts the
        cluster down and deletes the throwaway profile.
        
        Supported schedulers
        --------------------
        
        Platform LSF ("lsf"), Sun Grid Engine ("sge"), Torque ("torque") and
        SLURM ("slurm").
        
        More to come?
        
        Problems pickling
        -----------------
        If you are having problems pickling the pieces you want to parallelize (you will see errors complaining your
        item cannot be pickled), you might want to install the dill module: https://github.com/uqfoundation/dill. If dill is importable, ipython-cluster-helper
        will use the dill pickle method, which can pickle many items that the Python pickle cannot. This is currently not functional as dill has some issues pickling objects that IPython can pickle.
        
        Credits
        -------
        
        The cool parts of this were ripped from `bcbio-nextgen`_.
        
        Contributors
        ------------
        * Brad Chapman (@chapmanb)
        * Mario Giovacchini (@mariogiov)
        * Valentine Svensson (@vals)
        * Roman Valls (@brainstorm)
        * Rory Kirchner (@roryk)
        * Luca Beltrame (@lbeltrame)
        * James Porter (@porterjamesj)
        * Billy Ziege (@billyziege)
        * ink1 (@ink1)
        
        .. _bcbio-nextgen: https://github.com/chapmanb/bcbio-nextgen
        
Platform: UNKNOWN
