DeepSpark：一个基于Spark的分布式商品集群深度学习框架
DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters
摘要：
深度神经网络（DNN）的复杂性日益增加，使得利用现有的大规模数据处理管道来处理DNN训练中涉及的大量数据和参数变得具有挑战性。分布式计算平台和基于GPGPU的加速为这一计算挑战提供了主流解决方案。在本文中，我们提出了DeepSpark，一个分布式和并行的深度学习框架，利用Apache Spark在商品集群上。为了支持并行操作，DeepSpark使用Spark自动将工作负载和参数分发到运行Caffe/Tensorflow的节点，并通过流行的基于弹性平均随机梯度下降的更新方案的新型无锁异步变体迭代聚合训练结果，有效地补充了Spark的同步处理能力。DeepSpark是一个正在进行的项目，当前版本可在http://deepspark.snu.ac.kr上获得。
英文摘要：
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe/Tensorflow-running nodes using Spark, and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent based update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at http://deepspark.snu.ac.kr.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
