distributed processing for eppyy
- Free software: Mozilla Public License 2.0 (MPL-2.0)
- Documentation: https://zeppy.readthedocs.io.
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 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 import ppipes
result = ppipes.ipc_parallelpipe(runfunction,
args_list,
nworkers=None)
# runfunction is a function you will write,
# that may run idf.run(),
# gather the total energy use and return 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:
runfunctionwill run the idf file, and return the total energy usageresultwill be a list total energy usage in the same order as the items inargs_list- see the comments in the code for greater clarity
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.