使用用于HPC的CoE RAISE独特AI框架为医疗保健启用AI模型的超参数调优

Morris Riedel, C. Barakat, S. Fritsch, Marcel Aach, J. Busch, A. Lintermann, A. Schuppert, S. Brynjólfsson, Helmut Neukirchen, Matthias Book
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引用次数: 0

摘要

欧洲百亿亿次计算卓越中心“百亿亿次人工智能和基于仿真的工程研究”(CoE RAISE)是由欧盟委员会资助的一个项目。其核心目标之一是开发一个独特的人工智能框架(UAIF),简化尖端超级计算机上人工智能模型的开发。然而,这些超级计算机的高性能计算(HPC)环境需要许多低级模块的知识,这些模块都需要在不同的软件版本(例如,TensorFlow, Python, NCCL, PyTorch)和各种具体的超级计算机硬件部署(例如,JUWELS, JURECA, DEEP, JUPITER和其他EuroHPC Joint Undertaking HPC资源)中协同工作。本文将描述我们对使用这些环境的人工智能研究人员所面临的复杂挑战的分析,并解释如何使用UAIF来克服这些挑战。此外,它将展示使用UAIF超调功能的好处,通过使用HPC使AI模型更好(即更好的参数)和更快。此外,为了证明UAIF方法确实很简单,我们描述了医疗保健应用程序对选定的UAIF构建块的采用。这些例子包括急性呼吸窘迫综合征(ARDS)的人工智能模型。最后,我们强调了共同设计UAIF的用例的其他AI模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Hyperparameter-Tuning of AI Models for Healthcare using the CoE RAISE Unique AI Framework for HPC
The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE) is a project funded by the European Commission. One of its central goals is to develop a Unique AI Framework (UAIF) that simplifies the development of AI models on cutting-edge supercomputers. However, those supercomputers’ High-Performance Computing (HPC) environments require the knowledge of many low-level modules that all need to work together in different software versions (e.g., TensorFlow, Python, NCCL, PyTorch) and various concrete supercomputer hardware deployments (e.g., JUWELS, JURECA, DEEP, JUPITER and other EuroHPC Joint Undertaking HPC resources). This paper will describe our analyzed complex challenges for AI researchers using those environments and explain how to overcome them using the UAIF. In addition, it will show the benefits of using the UAIF hypertuning capability to make AI models better (i.e., better parameters) and faster by using HPC. Also, to demonstrate that the UAIF approach is indeed simple, we describe the adoption of selected UAIF building blocks by healthcare applications. The examples include AI models for the Acute Respiratory Distress Syndrome (ARDS). Finally, we highlight other AI models of use cases that co-designed the UAIF.
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