Quickly and easily parallelize Python functions using IPython on a cluster, supporting multiple schedulers. Optimizes IPython defaults to handle larger clusters and simultaneous processes.
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:])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.
Platform LSF ("lsf"), Sun Grid Engine ("sge"), Torque ("torque"), SLURM ("slurm").
The cool parts of this were ripped from bcbio-nextgen.
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