PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.
Break the cycle - use the Catalyst!

Project manifest. Part of PyTorch Ecosystem. Part of Catalyst Ecosystem:

• Alchemy - Experiments logging & visualization
• Catalyst - Accelerated Deep Learning Research and Development
• Reaction - Convenient Deep Learning models serving

Catalyst at AI Landscape.

Note: this repo uses advanced Catalyst Config API and could be a bit out-of-day right now. Use Catalyst's minimal examples section for a starting point and up-to-day use cases, please.

You will learn how to train your GAN using the Catalyst framework. The main advantage is to customize your experiments in the yaml config instead of the code.

pip install -r requirements.txt

# (Goodfellow et. al., 2014: https://arxiv.org/pdf/1406.2661.pdf)
catalyst-dl run -C examples/mnist/configs/vanilla_gan.yml
# (Arjovsky et. al., 2017: https://arxiv.org/abs/1701.07875)
catalyst-dl run -C examples/mnist/configs/wasserstein_gan.yml
# (Gulrahani et. al., 2017: https://arxiv.org/abs/1704.00028)
catalyst-dl run -C examples/mnist/configs/wasserstein_gan_gp.yml
# (Mirza and Osindero, 2014: https://arxiv.org/abs/1411.1784)
catalyst-dl run -C examples/mnist/configs/conditional_gan.yml

If you want to try right now run from console

(you should download FFHQ dataset before that and specify path in examples/advanced/tconfigs/data/FFHQ.yml)

./examples/advanced/experiments_setup/run.sh