Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

TL;DR

This paper introduces Dataloader Parameter Tuner (DPT), an automated framework that optimizes dataloader parameters like subprocesses and prefetch factor to enhance data loading efficiency in deep learning models.

Contribution

The paper presents a novel automated framework that uses grid search to find optimal dataloader parameters, improving data transfer speed in deep learning systems.

Findings

Optimizes dataloader subprocesses and prefetch factor.

Accelerates data transfer in deep learning workflows.

Automates parameter tuning process.

Abstract

Deep learning has recently become one of the most compute/data-intensive methods and is widely used in many research areas and businesses. One of the critical challenges of deep learning is that it has many parameters that can be adjusted, and the optimal value may need to be determined for faster operation and high accuracy. The focus of this paper is the adjustable parameters of the dataloader. The dataloader in a system mainly groups the data appropriately and loads it to the main memory for the deep learning model to use. We introduce an automated framework called Dataloader Parameter Tuner (DPT) that determines the optimal value for the parameters required for the dataloader. This framework discovers the optimal values for the number of dataloader's subprocesses (i.e., worker) and prefetch factor through grid search to accelerate the data transfer for machine learning systems.