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Meister, Moritz Johannes (2019). Maggy: open-source asynchronous distributed hyperparameter optimization based on Apache Spark. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).
Title: | Maggy: open-source asynchronous distributed hyperparameter optimization based on Apache Spark |
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Item Type: | Thesis (Master thesis) |
Masters title: | Data Science |
Date: | July 2019 |
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Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Lenguajes y Sistemas Informáticos e Ingeniería del Software |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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For the past two years, Hopsworks, an open-source machine learning platform, has used Apache Spark to distribute hyperparameter optimization tasks in machine learning. Hopsworks provides some basic optimizers (grid-search, random-search, differential evolution) to propose combinations of hyperparameters (trials) that are run synchronously in parallel. However, many such trials perform poorly, and waste a lot of hardware accelerator cycles on trials that could be stopped early, freeing up resources for other trials. In this thesis, the work on Maggy is presented, an open-source asynchronous and fault-tolerant hyperparameter optimization framework built on Spark. Maggy transparently schedules and manages hyperparameter trials, enabling state-of-the-art asynchronous optimization algorithms, thereby increasing resource utilization and increasing the number of trials that can be performed in a given period of time up to 30% on a fixed amount of resources. Early stopping is found to perform best when the model is sensitive, in terms of generalization performance, to the hyperparameter configurations.
Item ID: | 56977 |
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DC Identifier: | http://oa.upm.es/56977/ |
OAI Identifier: | oai:oa.upm.es:56977 |
Deposited by: | Biblioteca Facultad de Informatica |
Deposited on: | 21 Oct 2019 14:22 |
Last Modified: | 21 Oct 2019 14:22 |