Oct 19 – 23, 2020
Europe/Zurich timezone

Hyperparameter Optimisation for Machine Learning using ATLAS Grid and HPC

Oct 22, 2020, 11:30 AM
Lightning talk 6 ML infrastructure : Hardware and software for Machine Learning Workshop


Rui Zhang (University of Wisconsin Madison (US))


With the emerging of more and more sophisticated machine learning models in high energy physics, optimising the parameters of the models (hyperparameters) is becoming more and more crucial in order to get the best performance for physics analysis. This requires a lot of computing resources. So far, many of the training results are worked out in a personal computer or a local institution cluster, which prevents people from going to a wider search space or testing more brave ideas. We minimise the obstacle by implementing a hyperparameter optimisation (HPO) infrastructure into the ATLAS Computing Grid. Users submit one task of HPO and the Grid will take care of the optimisation procedure to return the best hyperparameter back to users. I will discuss about using High Performance Computers under this context as well.

Primary authors

Rui Zhang (University of Wisconsin Madison (US)) Wen Guan (University of Wisconsin (US)) Tadashi Maeno (Brookhaven National Laboratory (US)) Fa-Hui Lin (University of Texas at Arlington (US)) Torre Wenaus (Brookhaven National Laboratory (US)) Alkaid Cheng (University of Wisconsin Madison (US))

Presentation materials