Zero

Zero is a general-purpose library for PyTorch users. Zero simplifies training loops, facilitates reproducibility, helps with models evaluation and other typical Deep Learning tasks. Zero is a toolbox, not a framework:

• your training loop stays the same (regardless of whether it is powered by a simple Python loop or by a specialized framework)

• you can start by using any single tool you need, there is no “central concept”

• you can replace tools from Zero with custom solutions at any moment

NOTE: Zero is tested (and battle-tested in research projects), but the interface is not stable yet, so backward-incompatible changes in future releases are possible.

Overview

• Website

• Learn Zero

• Classification task example (MNIST)

• The first release announcement

• Discussion

• Code

Installation

We recommend installing PyTorch before installing Zero.

pip install libzero

Dependencies: see pyproject.toml in the repository.

How to contribute

• See issues, especially with the labels “discussion” and “help wanted”

• Open issues with bugs, ideas and any other kind of feedback

If your contribution includes pull requests, see CONTRIBUTING.md

How to cite

@article{gorishniy2020zero,
    title={Zero: a Zero-Overhead Library for PyTorch Users},
    author={Yury Gorishniy},
    journal={GitHub},
    volume={Yura52/zero},
    url={https://github.com/Yura52/zero},
    year={2020},
}

Why “Zero”?

Zero aims to be zero-overhead in terms of mental overhead: solutions, provided by Zero, try to be as minimal, intuitive and easy to learn, as possible. Well, all these things can be pretty subjective, so don’t take it too seriously :wink:

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Indices and Tables

• Index

• Module Index