'Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker'
philschmid
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In case you missed it: on March 25th [we announced a collaboration with Amazon SageMaker](https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face) to make it easier to create State-of-the-Art Machine Learning models, and ship cutting-edge NLP features faster.  

Together with the SageMaker team, we built 🤗  Transformers optimized [Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) to accelerate training of Transformers-based models. Thanks AWS friends!🤗 🚀 

With the new HuggingFace estimator in the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/), you can start training with a single line of code. 

![thumbnail](assets/19_sagemaker_distributed_training_seq2seq/thumbnail.png)

The [announcement blog post](https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face) provides all the information you need to know about the integration, including a "Getting Started" example and links to documentation, examples, and features.

listed again here:

- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)

If you're not familiar with Amazon SageMaker: *"Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models." [[REF](https://aws.amazon.com/sagemaker/faqs/)]*