Simplify IPython cluster start up and use for multiple schedulers.
Project description
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”), SLURM (“slurm”).
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)
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