Distributed Training: Errors to Avoid

In this era of large language models (LLMs), monolithic foundation models, and increasingly enormous datasets, distributed training is a must, as both data and model weights very rarely fit on a single machine. However, distributed training in ML is complex and error-prone, with many hidden pitfalls that can cause huge issues in the model training…

data datasets distributed error errors foundation language language models large language models llms machine ml model development mlops training