miniGAN is a generative adversarial network code developed as part of the Exascale Computing Project's (ECP) ExaLearn project at Sandia National Laboratories.
miniGAN v. 1.0.0
For questions, contact J. Austin Ellis ([email protected]) or Siva Rajamanickam ([email protected]).
miniGAN is a proxy application for generative adversarial networks.
The objective of the miniGAN miniapp is to model performance for training generator and discriminator networks. The GAN's generator and discriminator generate plausible 2D/3D maps and identify fake maps, respectively. Related applications exist in cosmology (CosmoGAN, ExaGAN) and wind energy (ExaWind).
Authors: J. Austin Ellis ([email protected]) and Siva Rajamanickam ([email protected])
Coming soon.
- python/3.5.2
- pytorch/1.3.1
- horovod/0.18.2
- Enter desired pytorch directory and prepare python env
- Run
./setup_python_env.sh
- Run
source ./minigan_torch_env/bin/activate
(for pytorch) - (Run
deactivate
to exit python env)
- Generate bird dataset
- Move to ${minigan_dir}/data
- Run
python generate_random_images.py --help
for running options
- Run
python3 minigan_driver.py --help
for running options
- Default dataset is "random". Switch to "--dataset bird" to use generated dataset.
- Run!
- Do not run
setup_python_env.sh
, instead runmodule load ibm-wml-ce/1.7.0-1
to load IBM's Watson ML Community Edition.- This should contain PyTorch, Horovod, Matplotlib
- Have not been successful with Summit's standalone pip / anaconda
- Obtain an interactive session using
bsub --nnodes 1 -Is -W 1:00 -P <ProjID> /bin/bash
- Run using
ddlrun python3 minigan_driver.py --dataset bird
- In development
Please report bugs or feature requests to: https://github.com/SandiaMLMiniApps/miniGAN/issues
miniGAN is licensed under standard 3-clause BSD terms of use. For specifics, please refer to the LICENSE file contained in the repository or distribution. Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software.