This blog dives into the challenges and innovations in distributed training for deep learning models. We explore how GPU and TPU accelerators are crucial for managing large neural networks and detail the use of gradient descent in model optimization. Key discussions include the strategies of data parallel and distributed data parallel, with insights into implementations like Horovod’s ring-allreduce and PyTorch’s process-based approach. This overview provides a solid foundation for understanding how modern computational resources can be leveraged to enhance deep learning training efficiency.