Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep learning approach to identify various particles using low-level detector information from high-energy collisions. These models will be incorporated in the CMS software framework (CMSSW) to enable their use for particle reconstruction or for trigger operation in real-time. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports an implementation of the end-to-end deep learning inference with the CMS software framework. The inference has been implemented on GPU for faster computation using ONNX. We have benchmarked the ONNX inference with GPU and CPU using NERSCs Perlmutter cluster by building a docker image of the CMS software framework.
@article{arxiv.2309.14254,
title = {End-to-end deep learning inference with CMSSW via ONNX using docker},
author = {Purva Chaudhari and Shravan Chaudhari and Ruchi Chudasama and Sergei Gleyzer},
journal= {arXiv preprint arXiv:2309.14254},
year = {2023}
}
Comments
9 pages, 7 figures, CHEP2023 proceedings, submitted to EPJ Web of Conferences