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poster-acat2022-hopaas

:globe_with_meridians: Repository to generate the HTML poster about Hopaas for ACAT 2022

https://github.com/mbarbetti/poster-acat2022-hopaas

Science Score: 41.0%

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    Found 3 DOI reference(s) in README
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    Links to: arxiv.org
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Keywords

academic academic-poster acat2022 hopaas-service infn-cloud jinja2 jinja2-template
Last synced: 9 months ago · JSON representation ·

Repository

:globe_with_meridians: Repository to generate the HTML poster about Hopaas for ACAT 2022

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academic academic-poster acat2022 hopaas-service infn-cloud jinja2 jinja2-template
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme Citation

README.md

Hyperparameter Optimization as a Service on INFN Cloud

in 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)

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Abstract

The simplest and often most effective way of parallelizing the training of complex machine learning models is to execute several training instances on multiple machines, possibly scanning the hyperparameter space to optimize the underlying statistical model and the learning procedure. Often, such a meta learning procedure is limited by the ability of accessing securely a common database organizing the knowledge of the previous and ongoing trials. Exploiting opportunistic GPUs provided in different environments represents a further challenge when designing such optimization campaigns. In this contribution we discuss how a set of RestAPIs can be used to access a dedicated service based on INFN Cloud to monitor and possibly coordinate multiple training instances, with gradientless optimization techniques, via simple HTTP requests. The service, named Hopaas (Hyperparameter OPtimization As A Service), is made of web interface and sets of APIs implemented with a FastAPI back-end running through Uvicorn and NGINX in a virtual instance of INFN Cloud. The optimization algorithms are currently based on Bayesian techniques as provided by Optuna. A Python front-end is also made available for quick prototyping. We present applications to hyperparameter optimization campaigns performed combining private, INFN Cloud and CINECA resources.

Authors:

M. Barbetti [1,2], L. Anderlini [2]

Affiliations: 1. Department of Information Engineering, University of Florence, via Santa Marta, 3, Firenze, Italy 2. Istituto Nazionale di Fisica Nucleare, Sezione di Firenze, via G. Sansonse 1, Sesto Fiorentino (FI), Italy

Cite us

Are you using Hopaas in your research project? Please cite us!

M. Barbetti and L. Anderlini, Hyperparameter Optimization as a Service on INFN Cloud, in 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022), arXiv:2301.05522

bibtex @inproceedings{hopaas:2023, author = "Barbetti, Matteo and Anderlini, Lucio", title = "{Hyperparameter Optimization as a Service on INFN Cloud}", booktitle = "{21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)", eprint = "2301.05522", archivePrefix = "arXiv", primaryClass = "cs.DC", month = "1", year = "2023" }

Credits

Poster project based on cpitclaudel/academic-poster-template. Poster webpage hosted by GitHub page.

Owner

  • Name: Matteo Barbetti
  • Login: mbarbetti
  • Kind: user
  • Location: Firenze, Italy
  • Company: University of Florence

PhD student in Smart Computing @ UniFi

Citation (CITATION.bib) 

@inproceedings{hopaas:2023,
    author = "Barbetti, Matteo and Anderlini, Lucio",
    title = "{Hyperparameter Optimization as a Service on INFN Cloud}",
    booktitle = "{21st International Workshop on Advanced Computing and 
                Analysis Techniques in Physics Research (ACAT 2022)",
    eprint = "2301.05522",
    archivePrefix = "arXiv",
    primaryClass = "cs.DC",
    month = "1",
    year = "2023"
}

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