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An Intermediate Representation for Optimizing Machine Learning Pipelines
- Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domain-specific language for collections and matrices. Lara's inter-mediate representation (IR) reflects on the complete program, i.e., UDFs, control flow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domain-specific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of ourMachine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domain-specific language for collections and matrices. Lara's inter-mediate representation (IR) reflects on the complete program, i.e., UDFs, control flow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domain-specific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of our proposed optimizations on dense and sparse data, which achieve speedups of up to an order of magnitude.…
Author details: | Andreas KunftORCiDGND, Asterios Katsifodimos, Sebastian SchelterGND, Sebastian Bress, Tilmann RablORCiDGND, Volker MarklGND |
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DOI: | https://doi.org/10.14778/3342263.3342633 |
ISSN: | 2150-8097 |
Title of parent work (English): | Proceedings of the VLDB Endowment |
Publisher: | Association for Computing Machinery |
Place of publishing: | New York |
Publication type: | Article |
Language: | English |
Date of first publication: | 2019/07/01 |
Publication year: | 2019 |
Release date: | 2021/01/11 |
Volume: | 12 |
Issue: | 11 |
Number of pages: | 15 |
First page: | 1553 |
Last Page: | 1567 |
Funding institution: | EU project E2Data [780245]; German Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF) [01IS18025A, 01IS18037A]; Moore-Sloan Data Science Environment at New York University |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |