Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker

Training large summarization models like BART or T5 is inherently distributed work. Relying on SageMaker for this isn't just convenience; it's about managing the distributed complexity that normally kills projects. The real headache isn't the training code; it's coordinating the distributed GPUs and managing the data partitioning across multiple instances.

If you don't set up the SageMaker pipeline correctly, you end up with a mess of poorly synchronized training jobs. It's an MLOps problem disguised as a training problem. The benefit is getting the heavy lifting off your local machine and onto managed infrastructure, which saves us headaches during deployment and scaling.