| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03040 | |
| Number of page(s) | 8 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103040 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103040
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
1 CERN, Esplanade des Particules 1, 1211 Meyrin, Geneva, Switzerland
2 Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, U.S.
Published online: 23 August 2021
We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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