DeepSpeed Data Efficiency

DeepSpeed Data Efficiency focuses on optimizing the training and inference of large language models (LLMs) and other deep learning models by improving how data is used and processed. Current research emphasizes techniques like efficient data sampling, optimized data routing (including strategies for handling long sequences and Mixture-of-Experts architectures), and innovative parallel processing methods to reduce memory usage and improve training speed. These advancements are crucial for making large-scale model training more feasible and cost-effective, accelerating progress in scientific fields like structural biology and enabling broader deployment of LLMs in various applications.