Catalyst

Founded 6 years ago

Catalyst is PyTorch framework for Deep Learning research and development. You get a training loop with metrics, model checkpointing, advanced logging and distributed training support without the boilerplate.

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

Getting started

pip install -U catalyst

More minimal examples here.

Overview

• Catalyst is pure PyTorch framework without any custom modules.
• With Catalyst you get the same PyTorch pipelines but in much more readable and fully reproducible way.
• Catalyst is easily extendable with Runner and Callback abstractions.
• You focus on key parts of your deep learning R&D.

Catalyst is rigorously tested and supports extra subpackages for Computer Vision and Natural Language Processing. 

Catalyst.Team and Catalyst.Contributors consist of industrial and academia researches, kaggle masters and professional software engineers. You can check out Catalyst-based kaggle competitions, pipelines and projects here.

Tutorials

• Customizing what happens in train
• Demo with minimal examples for CV, NLP, GANs and RecSys
• Detailed classification tutorial
• Advanced segmentation tutorial
• Comprehensive classification pipeline
• Binary and semantic segmentation pipeline
• Beyond fashion: Deep Learning with Catalyst (Config API)
• Tutorial from Notebook API to Config API (RU)

More than a framework — Ecosystem

Catalyst.Ecosystem manifesto

Catalyst was designed by researchers for researchers,

• to get the foundation for DL & RL research, which would be well tested and verified
• to generalize and develop best practices that work with all DL areas
• to think less about tech-side and focus on research hypothesis and their value
• to accelerate your research

Catalyst.Ecosystem,

• combines research best practices and helps knowledge sharing
• connects DL/RL with Software Engineer for maximum performance
• allows quickly and efficiently validate your hypotheses

Catalyst main principles,

• open — it’s fully Open Source Ecosystem
• equivalently — everyone can contribute and propose new ideas
• expertise — we are gathering top deep learning knowledge into one place
• high-performance — we are developing the ecosystem with software engineering best practices

Open Source Research Community

Last but not least, Catalyst.Friends community connects different startups, companies and research labs all over the world. For example, Catalyst.Team is working with Translational Research in Neuroimaging and Data Science (TReNDS) on DL brain image analysis. Feel free to write us, if you are also interested in such collaborations.

Slack ODS.ai: #tool_catalyst