Optimizing Distributed Training in Deep Learning

This chapter explores the challenges and strategies involved in transitioning to distributed training for deep learning using PyTorch, particularly in the context of autonomous driving. It discusses the optimization of data loaders, integration of Horovod, and the significant improvements in semantic segmentation efficiency achieved through distributed methods. The conversation also highlights the logistical and algorithmic considerations when scaling GPU usage, alongside the management of machine learning experiments.