https://github.com/ornl/distgans
Distributed computed training fo Generative Adversarial Neural Networks
https://github.com/ornl/distgans
Last synced: 6 months ago
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Distributed computed training fo Generative Adversarial Neural Networks
- Host: GitHub
- URL: https://github.com/ornl/distgans
- Owner: ORNL
- Created: 2021-03-02T02:04:51.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-01-06T23:27:46.000Z (almost 4 years ago)
- Last Synced: 2025-07-08T23:03:47.143Z (6 months ago)
- Language: Python
- Size: 253 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Distributed Generative Adversarial Neural Networks (DistGANs)
DistGANs is a Python package to perform distributed training of conditional generative adversarial neural networks for multi-class labeled image data.\
DistGANs partitionins the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label.
This is a code implemented in collaboration with:
- Massimiliano Lupo Pasini at Oak Ridge National Laboratory (lupopasinim@ornl.gov)
- Nouamane Laanait at Oak Ridge National Laboratory (laanaitn@ornl.gov; nlaanait@gmail.com)
- Vittorio Gabbi at Politecnico di Milano (vittorio.gabbi@mail.polimi.it)
- Debangshu Mukherjee at Oak Ridge National Laboratory (mukherjeed@ornl.gov)
- Vitaliy Starchenko at Oak Ridge National Laboratory (starchenkov@ornl.gov)
- Junqi Yin at Oak Ridge National Laboratory (yinj@ornl.gov)
- Andrey Prokpenko at Oak Ridge National Laboratory (prokopenkoav@ornl.gov)
## Requirements
Python 3.5 or greater\
PyTorch (any version works)
Optional, if NVIDIA gpu is present:
```
pip install pycuda
```
## Code style
To keep similar code style, it should be formatted using [black](https://github.com/psf/black):
```
black -S -l 79 {source_file_or_directory}
```
## Quick start conda setup
```
conda create --name {env_name} python=3.7
conda install -n {env_name} matplotlib docopt ipython mpi4py
conda install -n {env_name} -c anaconda pyyaml
conda install -n {env_name} pytorch torchvision -c pytorch
conda install -n {env_name} tensorboardx -c conda-forge
```
## Models
All models should be located in `GANs_dir`. The class and the file that contains the class should have identic name. For example, class:
```
class CNN_model(GANs_abstract_object.GANs_model):
...
```
File:
```
CNN_model.py
```