When training deep neural networks, it is crucial that the data loading process is efficient enough to prevent the system from becoming I/O constrained. Although built-in data loaders might not be instantaneously fast, their speed is typically sufficient because processing images with a deep network (such as forward and backward propagation) takes considerably longer. Thus, the system is primarily bottlenecked by computation rather than data input/output.