DLHub: Simplifying publication, discovery, and use of machine learning models in science
DLHub: Simplifying publication, discovery, and use of machine learning models in science
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DOI：
10.1016/j.jpdc.2020.08.006
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发表时间：
2021-01-01
影响因子：
3.8
通讯作者：
Foster, Ian
中科院分区：
文献类型：
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作者：
Li, Zhuozhao;Chard, Ryan;Foster, Ian
机器学习（ML）已经成为一种关键工具，可以实现新的分析方法，并推动对跨科学学科现象的更深入理解。越来越需要“学习系统”来支持ML生命周期中的各个阶段。虽然其他人专注于支持模型开发，培训和推理，但很少有人关注科学固有的独特挑战，例如需要发布和共享模型并在一系列可用的计算资源上提供服务。在本文中，我们介绍了科学数据和学习中心（DLHub），这是一个旨在支持这些用例的学习系统。具体来说，DLHub支持模型的发布，包括描述性元数据、持久标识符和灵活的访问控制。它将任意模型打包到可移植的可服务容器中，并在异构计算资源上实现这些模型的低延迟分布式服务。我们表明，DLHub支持与其他模型服务系统（包括TensorFlow Serving，SageMaker和Clipper）相当的低延迟模型推理，并通过启用数据挖掘和记忆化将性能提高了95%。我们还展示了DLHub可以扩展到在500个容器上并发服务模型。最后，我们描述了五个案例研究，突出使用DLHub的科学应用。(C)2020爱思唯尔公司All rights reserved.
Machine Learning (ML) has become a critical tool enabling new methods of analysis and driving deeper understanding of phenomena across scientific disciplines. There is a growing need for "learning systems" to support various phases in the ML lifecycle. While others have focused on supporting model development, training, and inference, few have focused on the unique challenges inherent in science, such as the need to publish and share models and to serve them on a range of available computing resources. In this paper, we present the Data and Learning Hub for science (DLHub), a learning system designed to support these use cases. Specifically, DLHub enables publication of models, with descriptive metadata, persistent identifiers, and flexible access control. It packages arbitrary models into portable servable containers, and enables low-latency, distributed serving of these models on heterogeneous compute resources. We show that DLHub supports low-latency model inference comparable to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, and improved performance, by up to 95%, with batching and memoization enabled. We also show that DLHub can scale to concurrently serve models on 500 containers. Finally, we describe five case studies that highlight the use of DLHub for scientific applications. (C) 2020 Elsevier Inc. All rights reserved.