EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||8|
|Section||6 - Physics Analysis|
|Published online||16 November 2020|
Fast Inference for Machine Learning in ROOT/TMVA
2 Lulea University of Technology
3 Carnegie Mellon University
4 Karlsruhe Institute of Technology
5 École polytechnique fédérale de Lausanne
Published online: 16 November 2020
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. However, with the rapidly evolving ecosystem for machine learning, the focus of TMVA is shifting. We present the new developments and strategy of TMVA, which will allow the analyst to integrate seamlessly, and effectively, different workflows in the diversified machine-learning landscape. Focus is put on a fast machine learning inference system, which will enable analysts to deploy their machine learning models rapidly on large scale datasets. We present the technical details of a fast inference system for decision tree algorithms, included in the next ROOT release (6.20). We further present development status and proposal for a fast inference interface and code generator for ONNX-based Deep Learning models.
© The Authors, published by EDP Sciences, 2020
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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