https://github.com/tjyuyao/t3w

Typed Thin (Py)Torch Wrapper
https://github.com/tjyuyao/t3w

Last synced: 26 days ago
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Typed Thin (Py)Torch Wrapper

• Host: GitHub
• URL: https://github.com/tjyuyao/t3w
• Owner: tjyuyao
• License: apache-2.0
• Created: 2023-07-27T08:14:39.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-04T05:58:50.000Z (11 months ago)
• Last Synced: 2024-12-29T18:35:52.699Z (about 1 month ago)
• Language: Python
• Homepage: https://tjyuyao.github.io/t3w/
• Size: 1.83 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# `t3w.py` - Typed Thin (Py)Torch Wrapper

T3W is a lightweight framework for training PyTorch models written by Yuyao Huang during his PhD at Tongji University.

- T3W is "typed". It leverages stronger and static typing compared to normal python code for clearer architecture and less bugs. The programming model is object-oriented. Users (you) are required to implement interfaces as subclasses and inject them as dependencies.

- T3W is "thin". With the philosophy "less is more" in mind, it leverages a minimal self-contained codebase that basically only depends on PyTorch to run. The plugin system under interface `ISideEffect` makes T3W not only thin, but also highly extensible.

- T3W stands with "PyTorch".

See the concise example [mnist_example.py](https://github.com/tjyuyao/t3w/blob/main/mnist_example.py).

If you feel like using `t3w.py`, you can install it with `pip install t3w`.

API documentation is currently available at https://tjyuyao.github.io/t3w/api/. We are going to add more detailed user guide in the future.