Catalyst

High-level utils for PyTorch DL & RL research. 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!
Catalyst is compatible with: Python 3.6+. PyTorch 0.4.1+.

Docs and examples

• Detailed classification tutorial (colab link)
• Comprehensive classification pipeline
• API documentation
• Examples

Overview

Catalyst helps you write compact but full-featured DL & RL pipelines in a few lines of code.
You get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate.

Features

- Universal train/inference loop.
- Configuration files for model/data hyperparameters.
- Reproducibility – even source code will be saved.
- Callbacks – reusable train/inference pipeline parts.
- Training stages support.
- Easy customization.
- PyTorch best practices (SWA, AdamW, 1Cycle, FP16 and more).

Structure

- DL – runner for training and inference, all of the classic machine learning and computer vision metrics and a variety of callbacks for training, validation and inference of neural networks.
- RL – scalable Reinforcement Learning, on-policy & off-policy algorithms and their improvements with distributed training support.
- contrib - additional modules contributed by Catalyst users.
- data - useful tools and scripts for data processing.

Getting started: 30 seconds with Catalyst