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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| Accès en ligne: | https://arxiv.org/abs/2301.05522 |
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| _version_ | 1866917762964652032 |
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| author | Barbetti, Matteo Anderlini, Lucio |
| author_facet | Barbetti, Matteo Anderlini, Lucio |
| contents | 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, 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 REST APIs can be used to access a dedicated service based on INFN Cloud to monitor and coordinate multiple training instances, with gradient-less optimization techniques, via simple HTTP requests. The service, called Hopaas (Hyperparameter OPtimization As A Service), is made of a web interface and sets of APIs implemented with a FastAPI backend 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 frontend is also made available for quick prototyping. We present applications to hyperparameter optimization campaigns performed by combining private, INFN Cloud, and CINECA resources. Such multi-node multi-site optimization studies have given a significant boost to the development of a set of parameterizations for the ultra-fast simulation of the LHCb experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_05522 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hyperparameter Optimization as a Service on INFN Cloud Barbetti, Matteo Anderlini, Lucio Distributed, Parallel, and Cluster Computing Machine Learning Data Analysis, Statistics and Probability 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, 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 REST APIs can be used to access a dedicated service based on INFN Cloud to monitor and coordinate multiple training instances, with gradient-less optimization techniques, via simple HTTP requests. The service, called Hopaas (Hyperparameter OPtimization As A Service), is made of a web interface and sets of APIs implemented with a FastAPI backend 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 frontend is also made available for quick prototyping. We present applications to hyperparameter optimization campaigns performed by combining private, INFN Cloud, and CINECA resources. Such multi-node multi-site optimization studies have given a significant boost to the development of a set of parameterizations for the ultra-fast simulation of the LHCb experiment. |
| title | Hyperparameter Optimization as a Service on INFN Cloud |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2301.05522 |