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This module provides a Python concurrent.futures
_ executor that lets you run
functions on remote systems in your HTCondor
_ or Slurm
_ cluster. Stop worrying
about writing job files, scattering/gathering, and serialization---this module
does it all for you.
It uses the cloudpickle
_ library to allow (most) closures to be used
transparently, so you're not limited to "pure" functions.
Installation::
pip install clusterfutures
Usage:
.. code-block:: python
import cfut
def square(n):
return n * n
with cfut.SlurmExecutor() as executor:
for result in executor.map(square, [5, 7, 11]):
print(result)
See slurm_example.py
_ and condor_example.py
_ for further examples.
The easiest way to get started is to
ignore the fact that futures are being used at all and just use the provided
map
function, which behaves like itertools.imap
_ but transparently
distributes your work across the cluster.
clusterfutures is a simple wrapper to run Python functions in batch jobs on an HPC cluster. Each future corresponds to one batch job. The functions that you run through clusterfutures should normally run for at least a few seconds each: running smaller functions will be inefficient because of the overhead of launching jobs and moving data.
Functions, parameters and return values are sent by creating files; this assumes that the control process and the worker nodes have a shared filesystem. This mechanism is convenient for relatively small amounts of data; it's probably not the best way to transfer large amounts of data to & from workers.
.. _concurrent.futures: https://docs.python.org/3/library/concurrent.futures.html .. _HTCondor: https://research.cs.wisc.edu/htcondor/ .. _cloudpickle: https://github.com/cloudpipe/cloudpickle .. _itertools.imap: https://docs.python.org/3/library/itertools.html#itertools.imap .. _Slurm: https://slurm.schedmd.com/ .. _slurm_example.py: https://github.com/sampsyo/clusterfutures/blob/master/slurm_example.py .. _condor_example.py: https://github.com/sampsyo/clusterfutures/blob/master/condor_example.py
FAQs
futures for remote execution on clusters
We found that clusterfutures demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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