Training Throughput

Training throughput in deep learning focuses on maximizing the speed and efficiency of model training, primarily aiming to reduce training time and costs. Current research emphasizes optimizing data loading and transfer, mitigating hardware failures (especially in large-scale distributed training using pipeline parallelism), and improving the efficiency of model architectures (like transformers and GNNs) through techniques such as quantization and memory optimization. These advancements are crucial for making deep learning more accessible and cost-effective, enabling faster development and deployment of sophisticated models across various applications.