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Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new.
Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving).
Features
- Universal train/inference loop
- Configuration files for model/data hyperparameters
- All source code and environment variables are saved for reproducibility
- Callbacks – reusable train/inference pipeline parts with easy customization
- Support for training stages
- Deep Learning best practices - SWA, AdamW, Ranger optimizer, OneCycle, and more
- Developments best practices - fp16 support, distributed training, slurm support
Project Samples
