In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academic research purposes and can be used as a framework or as a
standalone tool which supports a flexible configuration. The software allows to
train state-of-the-art deep bidirectional long short-term memory (LSTM) models
on both one dimensional data like speech or two dimensional data like
handwritten text and was used to develop successful submission systems in
several evaluation campaigns.

本文介绍了一种可扩展和易于配置的神经网络训练软件，该软件提供了丰富的功能层，特别关注在多个 GPU 上高效训练循环神经网络拓扑结构。该软件支持灵活配置，可用作框架或独立工具，能够训练最先进的深度双向长 - 短时记忆（LSTM）模型，并且已被用于开发成功的评估提交系统。

RETURNN：RWTH 可扩展通用循环神经网络训练框架

RETURNN: The RWTH Extensible Training framework for Universal Recurrent  Neural Networks

I present a new way to parallelize the training of convolutional neural
networks across multiple GPUs. The method scales significantly better than all
alternatives when applied to modern convolutional neural networks.

介绍了一种新的方法，可在多个 GPU 上并行训练卷积神经网络，该方法在应用于现代卷积神经网络时比所有其他方法的扩展性都更好。