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---
title: README
emoji: 🐠
colorFrom: pink
colorTo: purple
sdk: static
pinned: false
---

<div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
	<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"
		class="block overflow-hidden group"
	>
		<div
			class="w-full h-40 object-cover mb-2 bg-indigo-100 rounded-lg flex items-center justify-center dark:bg-gray-900 dark:group-hover:bg-gray-850"
		>
			<img
				alt=""
				src="/front/assets/promo/amazon_sagemaker_x_huggingface.png"
				class="w-40"
			/>
		</div>
		<div class="underline">Read announcement blog post</div>
	</a>
	<a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden">
		<img
			alt=""
			src="/front/assets/promo/amazon_walkthrough_thumbnail.png"
			class="w-full h-40 object-cover mb-2 bg-gray-300 rounded-lg"
		/>
		<div class="underline">Video Walkthrough with Philipp Schmid</div>
	</a>
	<a
		href="https://huggingface.co/docs/sagemaker"
		class="block overflow-hidden group"
	>
		<div
			class="w-full h-40 object-cover mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start"
		>
			<img
				alt=""
				src="/front/assets/promo/amazon_documentation.png"
				class="w-44 p-4"
			/>
		</div>
		<div class="underline">Documentation: Hugging Face in SageMaker</div>
	</a>
	<div class="lg:col-span-3">
		<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>