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Sommelier: Curating DNN Models for the Masses

Authors:

Wenjun HuAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 1876 - 1890

https://doi.org/10.1145/3514221.3526173

Published: 11 June 2022 Publication History

Abstract

Deep learning model repositories are indispensable in machine learning ecosystems today to facilitate model reuse. However, existing model repositories provide a bare-bone interface for model retrieval. The onus is on the user to profile and select from potentially hundreds of choices, barely relieving an average user of the expertise required to design the model in the first place. In this paper, we present Sommelier, an indexing and query system above typical DNN model repositories to interface directly with inference serving or other use cases. Given a desirable accuracy target and resource budget for an inference task category, Sommelier automatically searches through the repository for the most suitable model, without requiring manual profiling from the user. Motivated by manual iterative model search processes and typical model design strategies that generate model variants or models with common segments, Sommelier organizes DNN models based on their semantic correlation, defined as the probability of models producing the same results. This is further combined with a resource index based on relative resource consumption. Sommelier is implemented as a standalone query engine that can interface with an existing repository such as TF-Hub. A case study of 163 models in TF-Hub highlights the extent of model correlation across different model series, suggesting the best candidate model can easily evade manual profiling. Extensive evaluation shows that Sommelier returns the ideal model for over 95% of the queries; When interfaced with an inference server, Sommelier can reduce the 90th percentile tail latency of inference tasks by a factor of 6 via automatic model switching, far more than typical scale-out system optimizations.

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Cited By

Ahmad SGuan HSitaraman RMencagli GDazzi PLowenthal DBadia R(2024)Loki: A System for Serving ML Inference Pipelines with Hardware and Accuracy ScalingProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658688(267-280)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658688
Ahmad SGuan HFriedman BWilliams TSitaraman RWoo TTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)Proteus: A High-Throughput Inference-Serving System with Accuracy ScalingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624849(318-334)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624849
Cao BSharma AO’Gorman LCoss MJain S(2024)A Lightweight Measure of Classification Difficulty from Application Dataset CharacteristicsPattern Recognition10.1007/978-3-031-78169-8_29(439-455)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-3-031-78169-8_29
Show More Cited By

Index Terms

Sommelier: Curating DNN Models for the Masses
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
    2. Query languages
      1. Query languages for non-relational engines

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

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Published: 11 June 2022

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Cited By

Ahmad SGuan HSitaraman RMencagli GDazzi PLowenthal DBadia R(2024)Loki: A System for Serving ML Inference Pipelines with Hardware and Accuracy ScalingProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658688(267-280)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658688
Ahmad SGuan HFriedman BWilliams TSitaraman RWoo TTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)Proteus: A High-Throughput Inference-Serving System with Accuracy ScalingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624849(318-334)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624849
Cao BSharma AO’Gorman LCoss MJain S(2024)A Lightweight Measure of Classification Difficulty from Application Dataset CharacteristicsPattern Recognition10.1007/978-3-031-78169-8_29(439-455)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-3-031-78169-8_29
Hu BHu WSha KBanerjee SChen J(2023)Middleware Support for Edge Data Analytics over Heterogeneous ScenariosProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3626613(171-184)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3583740.3626613
Wang YChen KTan HGuo KFedorova ANarayanan DDi Luna GQuerzoni L(2023)Tabi: An Efficient Multi-Level Inference System for Large Language ModelsProceedings of the Eighteenth European Conference on Computer Systems10.1145/3552326.3587438(233-248)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3552326.3587438
Li ZKant HHai RKatsifodimos ABrambilla MBozzon A(2023)Metadata Representations for Queryable Repositories of Machine Learning ModelsIEEE Access10.1109/ACCESS.2023.333064711(125616-125630)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3330647

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