lightning/.github/ISSUE_TEMPLATE/code_improvement.md

---
name: Code improvement
about: Suggest a code improvement, i.e. refactoring, deprecation, etc.
title: ''
labels: refactors / code health
assignees: ''
---

## Proposed refactoring or deprecation

<!-- A clear and concise description of the code improvement -->

### Motivation

<!-- Please outline the motivation for the proposal. If this is related to another GitHub issue, please link here too -->

### Pitch

<!-- A clear and concise description of what you want to happen. -->

### Additional context

<!-- Add any other context or screenshots here. -->

______________________________________________________________________

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- [**Metrics**](https://github.com/PyTorchLightning/metrics): Machine learning metrics for distributed, scalable PyTorch applications.

- [**Lite**](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html): enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

- [**Flash**](https://github.com/PyTorchLightning/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

- [**Bolts**](https://github.com/PyTorchLightning/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

- [**Lightning Transformers**](https://github.com/PyTorchLightning/lightning-transformers): Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.