# Hyperparameter Optimization as a Service on INFN Cloud

arXiv: https://arxiv.org/abs/2301.05522 · HTML: https://mlsanity.com/paper/2301.05522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)

## Built on by (most cited first, 1 of 1)

- [Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss](https://mlsanity.com/paper/2303.11428/) (2023)

## Abstract (arXiv)

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.