TY  - JOUR
T1  - ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms
AU  - Ryu, MyungHoon 
AU  - Piao, XinYu 
AU  - Park, JooYoung 
AU  - Synn, DoangJoo 
AU  - Kim, Jong-Kook 
JO  - The Transactions of the Korea Information Processing Society
PY  - 2025
DA  - 2025/1/30
DO  - https://doi.org/10.3745/TKIPS.2025.14.1.1
KW  - Deep Learning
KW  - AVL-Tree Search Algorithm
KW  - data loader
KW  - Parameter Tuning
AB  - 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.