| Article | |
| Title | Machine Learning with ROOT/TMVA |
| Author(s) | Albertsson, Kim (CERN ; Lulea U.) ; An, Sitong (CERN ; Carnegie Mellon U.) ; Gleyzer, Sergei (Alabama U.) ; Moneta, Lorenzo (CERN) ; Niermann, Joana (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zampieri, Luca (CERN) ; Mesa, Omar Andres Zapata (CERN) |
| Publication | 2020 |
| Number of pages | 7 |
| In: | EPJ Web Conf. 245 (2020) 06019 |
| In: | 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06019 |
| DOI | 10.1051/epjconf/202024506019 |
| Subject category | Computing and Computers |
| Accelerator/Facility, Experiment | ROOT |
| Abstract | ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow. |
| Copyright/License | © 2020-2024 The Authors (License: CC-BY-4.0) |
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Zapis kreiran 2021-02-17, zadnja izmjena 2021-02-18