The development of deep learning models involves two key stages: architecture generation and training. Traditionally, creating neural architectures involves a manual process requiring extensive adjustments and deep expertise. Recently, however, there has been a significant shift with the advent of automated methods for architecture generation, such as Neural Architecture Search (NAS). Efficient training of Deep Learning architectures forms an important component of automating the architecture generation process, which has led to considerable improvements in model development.