Physics Community Needs, Tools, and Resources for Machine Learning
Philip C. Harris
E. Katsavounidis
W. McCormack
D. Rankin
Yongbin Feng
A. Gandrakota
C. Herwig
B. Holzman
K. Pedro
Nhan Tran
Tingjun Yang
J. Ngadiuba
Michael W. Coughlin
Scott Hauck
Shih-Chieh Hsu
Elham E Khoda
De-huai Chen
Mark S. Neubauer
Javier Mauricio Duarte
G. Karagiorgi
Miaoyuan Liu

Abstract
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.
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