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| title: README | |
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| <p class="lg:col-span-3"> | |
| Hugging Face is working with Amazon Web Services to make it easier than | |
| ever for startups and enterprises to <strong | |
| >train and deploy Hugging Face models in Amazon SageMaker</strong | |
| >. | |
| </p> | |
| <a | |
| href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face" | |
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| src="/front/assets/promo/amazon_sagemaker_x_huggingface.png" | |
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| </div> | |
| <div class="underline">Read announcement blog post</div> | |
| </a> | |
| <a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden"> | |
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| alt="" | |
| src="/front/assets/promo/amazon_walkthrough_thumbnail.png" | |
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| <div class="underline">Video Walkthrough with Philipp Schmid</div> | |
| </a> | |
| <a | |
| href="https://huggingface.co/docs/sagemaker" | |
| class="block overflow-hidden group" | |
| > | |
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| <div class="underline">Documentation: Hugging Face in SageMaker</div> | |
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| <p class="mb-2"> | |
| To train Hugging Face models in Amazon SageMaker, you can use the | |
| Hugging Face Deep Learning Containers (DLCs) and the Hugging Face | |
| support in the SageMaker Python SDK. | |
| </p> | |
| <p class="mb-2"> | |
| The DLCs are fully integrated with the SageMaker distributed training | |
| libraries to train models more quickly using the latest generation of | |
| accelerated computing instances available on Amazon EC2. With the | |
| SageMaker Python SDK, you can start training with just a single line of | |
| code, enabling your teams to move from idea to production more quickly. | |
| </p> | |
| <p class="mb-2"> | |
| To deploy Hugging Face models in Amazon SageMaker, you can use the | |
| Hugging Face Deep Learning Containers with the new Hugging Face | |
| Inference Toolkit. | |
| </p> | |
| <p class="mb-2"> | |
| With the new Hugging Face Inference DLCs, deploy your trained models for | |
| inference with just one more line of code, or select any of the 10,000+ | |
| models publicly available on the 🤗 Hub, and deploy them with Amazon | |
| SageMaker, to easily create production-ready endpoints that scale | |
| seamlessly, with built-in monitoring and enterprise-level security. | |
| </p> | |
| <p> | |
| More information: <a | |
| href="https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/" | |
| class="underline">AWS blog post</a | |
| >, | |
| <a | |
| href="https://discuss.huggingface.co/c/sagemaker/17" | |
| class="underline">Community Forum</a | |
| > | |
| </p> | |
| </div> | |
| </div> |