论文标题
数据加载参数调谐器：用于深度学习模型的自动化数据加载参数调谐器
Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models
论文作者
论文摘要
深度学习最近已成为最计算/数据密集型方法之一，并在许多研究领域和企业中广泛使用。深度学习的关键挑战之一是它具有许多可以调整的参数，并且可能需要确定最佳值，以更快地操作和高精度。本文的重点是数据加载程序的可调节参数。系统中的数据加载器主要分组数据并将其加载到主内存中，以便使用深度学习模型。我们介绍了一个称为DataLoader参数调谐器（DPT）的自动框架，该框架确定了数据加载程序所需的参数的最佳值。该框架通过网格搜索发现了数据加载子搜索次数（即工人）和预取系数的最佳值，以加速机器学习系统的数据传输。
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.
