From deeb82d28ffdac9d5a41be13e7a3277e9fe94df9 Mon Sep 17 00:00:00 2001 From: William Falcon Date: Thu, 25 Jul 2019 10:23:51 -0400 Subject: [PATCH] cleaned readme --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 24e0c731c8..7a1c7c80ee 100644 --- a/README.md +++ b/README.md @@ -29,13 +29,13 @@ pip install pytorch-lightning **[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)** ## What is it? -Keras and fast.ai are too abstract for researchers. Lightning abstracts the full training loop but gives you control in the critical points. +Lightning defers training and validation loop logic to you. It guarantees correct, modern best practices for the core training logic. ## Why do I want to use lightning? -Because you don't want to define a training loop, validation loop, gradient clipping, checkpointing, loading, -gpu training, etc... every time you start a project. Let lightning handle all of that for you! Just define your -data and what happens in the training, testing and validation loop and lightning will do the rest. +When starting a new project the last thing you want to do is recode a training loop, model loading/saving, distributed training, when to validate, etc... You're likely to spend a long time ironing out all the bugs without even getting to the core of your research. + +With lightning, you guarantee those parts of your code work, and focus on what the meat of the research is, what is the data and to do insie a training and validation loop. Don't worry about multiple gpus or speeding up your code, lightning will do that for you! To use lightning do 2 things: 1. [Define a LightningModel](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)