  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.
