Training large-scale models with distributed training

As ML algorithms grow more complex and the volumes of available training data expand exponentially, model training times have become a major bottleneck. Single-device training on massive datasets or gigantic models like large language models is increasingly impractical given memory, time, and latency constraints. For example, state-of-the-art language models have rapidly scaled from millions of parameters a decade ago to hundreds of billions today. The following graph illustrates how language models have evolved in recent years:

Figure 10.1: The growth of language models

To overcome computational challenges, distributed training techniques have become critical to accelerate model development by parallelizing computation across clusters of GPUs or TPUs in the cloud. By sharding data and models across devices and nodes, distributed training enables the scaling out of computation to train modern massive models and data...