Knowledge sharing on deep learning in physics research using VISPA
EPJ Web of Conferences, vol. 245, pp. 05040
Abstract
The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. One specific example is to meet knowledge sharing needs in deep learning by combining paper, code and data at a central place. Additionally the possibility to run it directly from the web browser is a key feature of this development. Any SSH reachable resource can be accessed via the VISPA web interface. This enables a flexible and experiment agnostic computing experience. The user interface is based on JupyterLab and is extended with analysis specific tools, such as a parametric file browser and TensorBoard. Our VISPA instance is backed by extensive GPU resources and a rich software environment. We present the current status of the VISPA project and its upcoming new features.
Authors 11
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Max Beer Aachen
Affiliation as printed
RWTH Aachen University
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N. Eich Aachen
Affiliation as printed
RWTH Aachen University
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Martin Erdmann Aachen
Affiliation as printed
RWTH Aachen University
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P. Fackeldey Aachen
Affiliation as printed
RWTH Aachen University
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Katharina Hafner Aachen
Affiliation as printed
RWTH Aachen University
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D. Noll Aachen
Affiliation as printed
RWTH Aachen University
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Yannik Alexander Rath Aachen
Affiliation as printed
RWTH Aachen University
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M. Rieger Aachen
Affiliation as printed
RWTH Aachen University, now at CERN
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Alexander Temme Aachen
Affiliation as printed
RWTH Aachen University
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Maximilian Vieweg Aachen
Affiliation as printed
RWTH Aachen University
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Martin Urban Aachen
Affiliation as printed
RWTH Aachen University
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