miniGAN: a proxy application for generative adversarial networks
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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.
- Project Type:
- Open Source, Publicly Available Repository
- Site Accession Number:
- SCR#2460
- Software Type:
- Scientific
- Version:
- 1.0.0
- License(s):
- BSD 3-clause "New" or "Revised" License
- Programming Language(s):
- C++
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOEPrimary Award/Contract Number:NA0003525
- DOE Contract Number:
- NA0003525
- Code ID:
- 46929
- OSTI ID:
- 1700663
- Country of Origin:
- United States
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