论文标题
perastai：一个用于创建稀疏神经网络的轻量级库
FasterAI: A Lightweight Library for Creating Sparse Neural Networks
论文作者
论文摘要
Forperai是一个基于Pytorch的库，旨在促进深度神经网络压缩技术的利用，例如稀疏，修剪，知识蒸馏或正则化。该库的构建是为了实现快速实施和实验。尤其是，压缩技术是利用Fastai和Pytorch Lightning等库的回调系统来带来用户友好和高级API。 Forperai的主要资产是其轻巧但功能强大，使用的简单性。的确，由于它是以非常颗粒的方式开发的，因此用户可以使用不同的参数组合来创建数千个独特的实验。在本文中，我们专注于代表图书馆核心的porastai的稀疏功能。在porterai中对神经网络进行稀疏只需要在传统培训循环中进行单一的代码，但允许执行最先进的技术，例如彩票票证假设实验
FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging Callback systems of libraries such as fastai and Pytorch Lightning to bring a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it was developed in a very granular way, users can create thousands of unique experiments by using different combinations of parameters. In this paper, we focus on the sparsifying capabilities of FasterAI, which represents the core of the library. Performing sparsification of a neural network in FasterAI only requires a single additional line of code in the traditional training loop, yet allows to perform state-of-the-art techniques such as Lottery Ticket Hypothesis experiments
