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Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3

A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at...

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Detalles Bibliográficos
Autores principales: Stan, Ioan-Mihail, Padolski, Siarhei, Lee, Christopher Jon
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/202125102009
http://cds.cern.ch/record/2823418
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author Stan, Ioan-Mihail
Padolski, Siarhei
Lee, Christopher Jon
author_facet Stan, Ioan-Mihail
Padolski, Siarhei
Lee, Christopher Jon
author_sort Stan, Ioan-Mihail
collection CERN
description A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning algorithms, the BigPanDA community desires to expand the platform’s capabilities and fill the gap between Machine Learning processing and data visualization. In this regard, BigPanDA partially adopts the cloud-native paradigm and entrusts the data presentation to MLFlow services running on Openshift OKD. Thus, BigPanDA interacts with the OKD API and instructs the containers orchestrator how to locate and expose the results of the Machine Learning analysis. The proposed architecture also introduces various DevOps-specific patterns, including continuous integration for MLFlow middleware configuration and continuous deployment pipelines that implement rolling upgrades. The Machine Learning data visualization services operate on demand and run for a limited time, thus optimizing the resource consumption.
id cern-2823418
institution Organización Europea para la Investigación Nuclear
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publishDate 2021
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spelling cern-28234182022-07-30T20:07:29Zdoi:10.1051/epjconf/202125102009http://cds.cern.ch/record/2823418engStan, Ioan-MihailPadolski, SiarheiLee, Christopher JonExploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3Computing and ComputersA large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning algorithms, the BigPanDA community desires to expand the platform’s capabilities and fill the gap between Machine Learning processing and data visualization. In this regard, BigPanDA partially adopts the cloud-native paradigm and entrusts the data presentation to MLFlow services running on Openshift OKD. Thus, BigPanDA interacts with the OKD API and instructs the containers orchestrator how to locate and expose the results of the Machine Learning analysis. The proposed architecture also introduces various DevOps-specific patterns, including continuous integration for MLFlow middleware configuration and continuous deployment pipelines that implement rolling upgrades. The Machine Learning data visualization services operate on demand and run for a limited time, thus optimizing the resource consumption.oai:cds.cern.ch:28234182021
spellingShingle Computing and Computers
Stan, Ioan-Mihail
Padolski, Siarhei
Lee, Christopher Jon
Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title_full Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title_fullStr Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title_full_unstemmed Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title_short Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3
title_sort exploring the self-service model to visualize the results of the atlas machine learning analysis jobs in bigpanda with openshift okd3
topic Computing and Computers
url https://dx.doi.org/10.1051/epjconf/202125102009
http://cds.cern.ch/record/2823418
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AT padolskisiarhei exploringtheselfservicemodeltovisualizetheresultsoftheatlasmachinelearninganalysisjobsinbigpandawithopenshiftokd3
AT leechristopherjon exploringtheselfservicemodeltovisualizetheresultsoftheatlasmachinelearninganalysisjobsinbigpandawithopenshiftokd3