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Fast Inference for Machine Learning in ROOT/TMVA

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 seaml...

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Detalles Bibliográficos
Autores principales: Albertsson, Kim, An, Sitong, Moneta, Lorenzo, Wunsch, Stefan, Zampieri, Luca
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/202024506008
http://cds.cern.ch/record/2752851
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author Albertsson, Kim
An, Sitong
Moneta, Lorenzo
Wunsch, Stefan
Zampieri, Luca
author_facet Albertsson, Kim
An, Sitong
Moneta, Lorenzo
Wunsch, Stefan
Zampieri, Luca
author_sort Albertsson, Kim
collection CERN
description 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.
id oai-inspirehep.net-1832184
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
record_format invenio
spelling oai-inspirehep.net-18321842021-03-01T08:02:34Zdoi:10.1051/epjconf/202024506008http://cds.cern.ch/record/2752851engAlbertsson, KimAn, SitongMoneta, LorenzoWunsch, StefanZampieri, LucaFast Inference for Machine Learning in ROOT/TMVAComputing and ComputersROOT 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.oai:inspirehep.net:18321842020
spellingShingle Computing and Computers
Albertsson, Kim
An, Sitong
Moneta, Lorenzo
Wunsch, Stefan
Zampieri, Luca
Fast Inference for Machine Learning in ROOT/TMVA
title Fast Inference for Machine Learning in ROOT/TMVA
title_full Fast Inference for Machine Learning in ROOT/TMVA
title_fullStr Fast Inference for Machine Learning in ROOT/TMVA
title_full_unstemmed Fast Inference for Machine Learning in ROOT/TMVA
title_short Fast Inference for Machine Learning in ROOT/TMVA
title_sort fast inference for machine learning in root/tmva
topic Computing and Computers
url https://dx.doi.org/10.1051/epjconf/202024506008
http://cds.cern.ch/record/2752851
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AT ansitong fastinferenceformachinelearninginroottmva
AT monetalorenzo fastinferenceformachinelearninginroottmva
AT wunschstefan fastinferenceformachinelearninginroottmva
AT zampieriluca fastinferenceformachinelearninginroottmva