As artificial intelligence advances, training large-scale neural networks, including large language models, has become increasingly critical. The growing size and complexity of these models not only elevate the costs and energy requirements associated with training but also highlight the necessity for effective hardware utilization. In response to these challenges, researchers and engineers are exploring distributed decentralized training strategies. In this blog post, we will examine various methods of distributed training, such as data-parallel training and gossip-based averaging, to illustrate how these approaches can optimize model training efficiency while addressing the rising demands of the field.
Data-Parallelism, the All-Reduce Operation and Synchronicity
Data-parallel training is a technique that involves dividing mini-batches of data across multiple devices (workers). This method not only enables several workers to compute gradients simultaneously, thereby improving training speed, but also allows…