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@ -52,9 +52,11 @@ pip install pytorch-lightning
[Copy and run this COLAB!](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
## What is it?
Lightning is a very lightweight wrapper on PyTorch that decouples the science code from the engineering code. It's more of a style-guide than a framework. By refactoring your code, we can automate most of the non-research code.
Lightning is a way to organize your PyTorch code to decouple the science code from the engineering. It's more of a style-guide than a framework. By refactoring your code, we can automate most of the non-research code. Lightning guarantees tested, correct, modern best practices for the automated parts.
To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/PytorchLightning/pytorch-lightning#how-do-i-do-use-it) format (the science) and Lightning will automate the rest (the engineering). Lightning guarantees tested, correct, modern best practices for the automated parts.
Here's an example of how to organize PyTorch code into the LightningModule.
![PT to PL](docs/source/_images/mnist_imgs/pt_to_pl.jpg)
- If you are a researcher, Lightning is infinitely flexible, you can modify everything down to the way .backward is called or distributed is set up.
- If you are a scientist or production team, lightning is very simple to use with best practice defaults.