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Maggy: Scalable Asynchronous Parallel Hyperparameter Search
Logical Clocks AB, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-7236-4637
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-2748-8929
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-6779-7435
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2020 (English)In: Proceedings of the 1st Workshop on Distributed Machine Learning, Association for Computing Machinery , 2020, p. 28-33Conference paper, Published paper (Refereed)
Abstract [en]

Running extensive experiments is essential for building Machine Learning (ML) models. Such experiments usually require iterative execution of many trials with varying run times. In recent years, Apache Spark has become the de-facto standard for parallel data processing in the industry, in which iterative processes are implemented within the bulk-synchronous parallel (BSP) execution model. The BSP approach is also being used to parallelize ML trials in Spark. However, the BSP task synchronization barriers prevent asynchronous execution of trials, which leads to a reduced number of trials that can be run on a given computational budget. In this paper, we introduce Maggy, an open-source framework based on Spark, to execute ML trials asynchronously in parallel, with the ability to early stop poorly performing trials. In the experiments, we compare Maggy with the BSP execution of parallel trials in Spark and show that on random hyperparameter search on a convolutional neural network for the Fashion-MNIST dataset Maggy reduces the required time to execute a fixed number of trials by 33% to 58%, without any loss in the final model accuracy.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2020. p. 28-33
Keywords [en]
Scalable Hyperparameter Search, Machine Learning, Asynchronous Hyperparameter Optimization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-287209DOI: 10.1145/3426745.3431338ISI: 000709791500005Scopus ID: 2-s2.0-85097717704OAI: oai:DiVA.org:kth-287209DiVA, id: diva2:1506931
Conference
The 1st Workshop on Distributed Machine Learning (DistributedML'20)
Note

QC 20201207

Available from: 2020-12-04 Created: 2020-12-04 Last updated: 2023-03-06Bibliographically approved

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Publisher's full textScopushttps://doi.org/10.1145/3426745.3431338

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Sheikholeslami, SinaPayberah, Amir H.Vlassov, VladimirDowling, Jim

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Citation style
  • apa
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Output format
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