HuggingFace Transformers on AWS SageMaker enables distributed BART summarization training (facebook/bart-large-cnn)

AI Impact Summary

The guide documents distributed fine-tuning of a Seq2Seq model (facebook/bart-large-cnn) for summarization using HuggingFace Transformers DLCs and Amazon SageMaker, enabling multi-GPU training via SageMaker Data Parallelism. It relies on the HuggingFace estimator with code-based entry_point run_summarization.py, trains on Samsum, uses per-device batch size 4 with a 16-GPU setup, and publishes the resulting model to huggingface.co. Operationally, it requires an IAM role and S3 artifacts, and carries substantial compute costs; teams should plan data access, artifact storage, and model deployment workflow.