ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms 


Vol. 14,  No. 1, pp. 1-8, Jan.  2025
https://doi.org/10.3745/TKIPS.2025.14.1.1


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  Abstract

Recently deep learning has become widely used in many research fields and businesses. To improve the performance of deep learning, one of the critical challenges is to determine the optimal values of many parameters that can be adjusted. This paper focuses on the adjustable dataloader parameters that affect the overall training time and proposes an automated dataloader parameter tuning framework, called ADaPT, to determine the optimal values of the dataloader parameters. The proposed ADaPT uses the characteristic of the AVL tree and attempts to determine optimal dataloader parameters in a small amount of time to accelerate the data loading. The results show that the proposed method effectively accelerates the data loading speed compared to the default parameter values and values that are recommended by P yTorch and the speed is comparable to the optimal.

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[IEEE Style]

M. Ryu, X. Piao, J. Park, D. Synn, J. Kim, "ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms," The Transactions of the Korea Information Processing Society, vol. 14, no. 1, pp. 1-8, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.1.1.

[ACM Style]

MyungHoon Ryu, XinYu Piao, JooYoung Park, DoangJoo Synn, and Jong-Kook Kim. 2025. ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms. The Transactions of the Korea Information Processing Society, 14, 1, (2025), 1-8. DOI: https://doi.org/10.3745/TKIPS.2025.14.1.1.

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