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distributed processing for eppyy

Project description

zeppy Documentation Status

distributed processing for eppyy


To run eppy on multiple nodes in parallel and collect the results.

So what is a node and why would you want to do this ?

A node can be any or all of the following:

  • a process (such E+ running on a single core on a multi-core computer)
    • so we can do multi-processing and run it on many cores on a single computer
  • a computer
    • so we can run it on multiple computers that are on the same network
  • a group of group of computer in a local network
    • So we can run multiple groups of machines that may be at different locations on different local networks
    • This can also be computers at different cloud locations
    • a single computer in the local network may act as an access node


Do the distributed processing with a single function call and get all the results back.

Sample code

import zeppy

result = zeppy.zmq_parallelpipe(runfunction,

# runfunction is a function you will write,
    # that may run,
    # gather the total energy use and retrn it
# args_list = {args: [idf1, idf2, idf3, ...]}
    # list of files to run
# if nworkers=None:
    # it will start up as many nodes as there are items in args_list
    # if you don't have enough nodes avaliable, you can set nworkers=n.
    # it will start up n nodes and queue up the runs evenly on the nodes

For example the above code can do the following:

  • runfunction will run the idf file, and return the total energy usage
  • result will be a list total energy usage in the same order as the items in args_list
  • see the comments in the code for greater clarity

Why is the function called zmq_parallelpipe ?

  • This package uses ZeroMQ Library
  • Parallel Pipeline is a fundamental pattern in it

This is the first pass at the API. It doesn’t work yet - Ha!. But there is hope :-)


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.


0.1.0 (2020-04-19)

  • First release on PyPI.

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