06.12.2021 · Lightning vs. Vanilla. PyTorch Lightning is built on top of ordinary (vanilla) PyTorch. The purpose of Lightning is to provide a research framework that allows for fast experimentation and scalability, which it achieves via an OOP approach that removes boilerplate and hardware-reference code.This approach yields a litany of benefits.
PyTorch-lightning is a recently released library which is a Kera-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest. (BTW, by Keras I mean no boilerplate, not overly-simplified). As the core author of lightning, I’ve been asked a few times about the core differences between lightning and ...
05.08.2019 · Both Lightning and Ignite have very simple interfaces, as most of the work is still done in pure PyTorch by the user. The main work happens inside the …
03.12.2021 · Code comparison: Pytorch vs Lightning Source From the above example, you can see, Lightning provides a more dedicated functionality for each operation : building model, loading data, configuring optimizers et cetera, along with that it takes care of the boilerplate code like the configuring the training loop.
27.02.2020 · This post answers the most frequent question about why you need Lightning if you’re using PyTorch. PyTorch is extremely easy to use to build complex AI models. But once the research gets complicated and things like multi-GPU training, 16-bit precision and TPU training get mixed in, users are likely to introduce bugs.