SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Workshops Archive

MLExchange: A Web-Based Platform Enabling Exchangeable Machine Learning Workflows for Scientific Studies

Workshop: The 4th Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing

Authors: Zhuowen Zhao, Tanny Chavez, Elizabeth Holman, Guanhua Hao, Adam Green, Harinarayan Krishnan, Dylan McReynolds, Ronald Pandolfi, Eric Roberts, and Petrus Zwart (Lawrence Berkeley National Laboratory (LBNL)); Howard Yanxon and Nicholas Schwarz (Argonne National Laboratory (ANL)); Subramanian Sankaranarayanan (University of Illinois, Chicago; Argonne National Laboratory (ANL)); Sergei Kalinin (Oak Ridge National Laboratory (ORNL)); Apurva Mehta (SLAC National Accelerator Laboratory); Stuart Campbell (Brookhaven National Laboratory); and Alexander Hexemer (Lawrence Berkeley National Laboratory (LBNL))

Abstract: Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales. Thus, MLExchange renders flexible using scenarios---users could either access the platform from a remote server or run its individual service(s) within their local network.

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