使用 🤗 Transformers 與 Amazon SageMaker 進行分散式訓練：以 BART/T5 摘要生成模型為例
Original: Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker
This technical guide, published by Hugging Face in 2021, details how to use Amazon SageMaker's managed infrastructure and distributed…
這是一篇 Hugging Face 官方教學，指導開發者如何使用 Amazon SageMaker 的分散式訓練功能來微調大型 Seq2Seq 模型（如 BART 和 T5）。文章詳細說明了如何將 Hugging Face 的 Seq2SeqTrainer 與 SageMaker Data Parallelism 庫結合，以解決單一 GPU 記憶體不足或訓練過慢的問題。讀者將學會如何準備訓練腳本、配置 SageMaker Estimator，並在 AWS 的多 GPU 實例上啟動高效的分散式訓練任務。
This technical guide, published by Hugging Face in 2021, details how to use Amazon SageMaker's managed infrastructure and distributed training capabilities to accelerate the fine-tuning of large sequence-to-sequence (Seq2Seq) models such as BART and T5 for text summarization tasks.
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