Training Pipeline

Training pipelines for machine learning models, particularly large language models and neural networks, are being actively optimized to improve efficiency and performance. Current research focuses on mitigating bottlenecks like data loading, asynchronous parallelism (e.g., using 1F1B schedules and weight prediction), and efficient resource utilization across heterogeneous hardware (GPUs, CPUs, SSDs). These advancements aim to reduce training time and costs, enabling the development and deployment of larger, more accurate models across various applications, from natural language processing to computer vision and robotics.