机器学习应用的奇点-性能影响分析

2019 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2019-09-01 DOI:10.1109/HPEC.2019.8916443

B. R. Jordan, David Barrett, David Burke, Patrick Jardin, Amelia Littrell, P. Monticciolo, Michael Newey, J. Piou, Kara Warner

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引用次数: 2

摘要

一般情况下，软件部署，尤其是深度学习应用，都难以获得可重复的结果。使用容器来缓解这些问题正在成为一种常见的做法。奇点是一种容器技术，针对高性能计算(HPC)中心中存在的独特问题。本文描述了在深度学习应用中同时使用奇点进行训练和推理的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Singularity for Machine Learning Applications - Analysis of Performance Impact

Software deployments in general, and deep learning applications in particular, suffer from difficulty in reproducible results. The use of containers to mitigate these issues is becoming a common practice. Singularity is a container technology which targets the unique issues present in High Performance Computing (HPC) Centers. This paper characterizes the impact of using Singularity for both Training and Inference on deep learning applications.

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2019 IEEE High Performance Extreme Computing Conference (HPEC)

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