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miniGAN: a proxy application for generative adversarial networks

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/miniGAN
https://doi.org/10.11578/dc.20201030.10

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Abstract

miniGAN is a python-based machine learning proxy application for generative adversarial networks, developed through the Exascale Computing Project's (ECP) ExaLearn project. It will be included in the main ECP proxy application and the machine learning proxy application suite. It is a proxy for ECP cosmological(CosmoFlow, ExaGAN) and wind energy(ExaWind) applications. miniGAN will be distributed to ECP hardware vendors as part of hardware codesign. miniGAN uses the Numpy/PyTorch/TensorFlow/Keras/Horovod frameworks and libraries. It also relies on the Kokkos and Kokkos-Kernels packages developed here at Sandia Labs. SAND2020-2038 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
Developers:
Ellis, John [1] ;  Rajamanickam, Sivasankaran [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Release Date:
2020-02-13
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
Shell
Python
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
46929
Site Accession Number:
SCR#2460.0
Research Org.:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/miniGAN
https://doi.org/10.11578/dc.20201030.10

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Citation Formats

Ellis, John, and Rajamanickam, Sivasankaran. miniGAN: a proxy application for generative adversarial networks. Computer Software. https://github.com/sandialabs/miniGAN. USDOE. 13 Feb. 2020. Web. doi:10.11578/dc.20201030.10.
Ellis, John, & Rajamanickam, Sivasankaran. (2020, February 13). miniGAN: a proxy application for generative adversarial networks. [Computer software]. https://github.com/sandialabs/miniGAN. https://doi.org/10.11578/dc.20201030.10.
Ellis, John, and Rajamanickam, Sivasankaran. "miniGAN: a proxy application for generative adversarial networks." Computer software. February 13, 2020. https://github.com/sandialabs/miniGAN. https://doi.org/10.11578/dc.20201030.10.
@misc{ doecode_46929,
title = {miniGAN: a proxy application for generative adversarial networks},
author = {Ellis, John and Rajamanickam, Sivasankaran},
abstractNote = {miniGAN is a python-based machine learning proxy application for generative adversarial networks, developed through the Exascale Computing Project's (ECP) ExaLearn project. It will be included in the main ECP proxy application and the machine learning proxy application suite. It is a proxy for ECP cosmological(CosmoFlow, ExaGAN) and wind energy(ExaWind) applications. miniGAN will be distributed to ECP hardware vendors as part of hardware codesign. miniGAN uses the Numpy/PyTorch/TensorFlow/Keras/Horovod frameworks and libraries. It also relies on the Kokkos and Kokkos-Kernels packages developed here at Sandia Labs. SAND2020-2038 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.},
doi = {10.11578/dc.20201030.10},
url = {https://doi.org/10.11578/dc.20201030.10},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20201030.10}},
year = {2020},
month = {feb}
}