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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Deploy models on AWS","local":"deploy-models-on-aws","sections":[{"title":"With Sagemaker SDK","local":"with-sagemaker-sdk","sections":[],"depth":2},{"title":"With Sagemaker Jumpstart","local":"with-sagemaker-jumpstart","sections":[],"depth":2},{"title":"With AWS Bedrock","local":"with-aws-bedrock","sections":[],"depth":2},{"title":"With Hugging Face Inference Endpoints","local":"with-hugging-face-inference-endpoints","sections":[],"depth":2},{"title":"With ECS, EKS, and EC2","local":"with-ecs-eks-and-ec2","sections":[],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/sagemaker/pr_1706/en/_app/immutable/chunks/index.186c2d73.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Deploy models on AWS","local":"deploy-models-on-aws","sections":[{"title":"With Sagemaker SDK","local":"with-sagemaker-sdk","sections":[],"depth":2},{"title":"With Sagemaker Jumpstart","local":"with-sagemaker-jumpstart","sections":[],"depth":2},{"title":"With AWS Bedrock","local":"with-aws-bedrock","sections":[],"depth":2},{"title":"With Hugging Face Inference Endpoints","local":"with-hugging-face-inference-endpoints","sections":[],"depth":2},{"title":"With ECS, EKS, and EC2","local":"with-ecs-eks-and-ec2","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="deploy-models-on-aws" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deploy-models-on-aws"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Deploy models on AWS</span></h1> <p data-svelte-h="svelte-1ipfta6">Deploying Hugging Face models on AWS is streamlined through various services, each suited for different deployment scenarios. Here’s how you can deploy your models using AWS and Hugging Face offerings.</p> <h2 class="relative group"><a id="with-sagemaker-sdk" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#with-sagemaker-sdk"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>With Sagemaker SDK</span></h2> <p data-svelte-h="svelte-yyy36l">Amazon SageMaker is a fully managed AWS service for building, training, and deploying machine learning models at scale. The SageMaker SDK simplifies interacting with SageMaker programmatically. Amazon SageMaker SDK provides a seamless integration specifically designed for Hugging Face models, simplifying the deployment process of managed endpoints. With this integration, you can quickly deploy pre-trained Hugging Face models or your own fine-tuned models directly into SageMaker-managed endpoints, significantly reducing setup complexity and time to production.</p> <p data-svelte-h="svelte-rvzxnc">To get started, check out this tutorial.</p> <h2 class="relative group"><a id="with-sagemaker-jumpstart" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#with-sagemaker-jumpstart"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>With Sagemaker Jumpstart</span></h2> <p data-svelte-h="svelte-1a138w1">Amazon SageMaker JumpStart is a curated model catalog from which you can deploy a model with just a few clicks. We maintain a Hugging Face section in the catalog that will let you self-host the most famous open models in your VPC with performant default configurations, powered under the hood by Hugging Face Deep Learning Catalogs (DLCs). (#todo link to DLC intro)</p> <p data-svelte-h="svelte-rvzxnc">To get started, check out this tutorial.</p> <h2 class="relative group"><a id="with-aws-bedrock" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#with-aws-bedrock"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>With AWS Bedrock</span></h2> <p data-svelte-h="svelte-1ugr84r">Amazon Bedrock enables developers to easily build and scale generative AI applications through a single API. With Bedrock Marketplace, you can now combine the ease of use of SageMaker JumpStart with the fully managed infrastructure of Amazon Bedrock, including compatibility with high-level APIs such as Agents, Knowledge Bases, Guardrails and Model Evaluations.</p> <p data-svelte-h="svelte-z5y5oz">To get started, check out this <a href="https://huggingface.co/blog/bedrock-marketplace?" rel="nofollow">blogpost</a>.</p> <h2 class="relative group"><a id="with-hugging-face-inference-endpoints" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#with-hugging-face-inference-endpoints"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>With Hugging Face Inference Endpoints</span></h2> <p data-svelte-h="svelte-d7r1p7">Hugging Face Inference Endpoints allow you to deploy models hosted directly by Hugging Face, fully managed and optimized for performance. It’s ideal for quick deployment and scalable inference workloads.</p> <p data-svelte-h="svelte-v81v84"><a href="https://huggingface.co/docs/inference-endpoints/main/en/index" rel="nofollow">Get started with Hugging Face Inference Endpoints</a>.</p> <h2 class="relative group"><a id="with-ecs-eks-and-ec2" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#with-ecs-eks-and-ec2"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>With ECS, EKS, and EC2</span></h2> <p data-svelte-h="svelte-7lqney">Hugging Face provides Inference Deep Learning Containers (DLCs) to AWS users, optimized environments preconfigured with Hugging Face libraries for inference, natively integrated in SageMaker SDK and JumpStart. However, the HF DLCs can also be used across other AWS services like ECS, EKS, and EC2.</p> <p data-svelte-h="svelte-1bymtxh">AWS Elastic Container Service (ECS), Elastic Kubernetes Service (EKS), and Elastic Compute Cloud (EC2) allow you to leverage DLCs directly.</p> <p data-svelte-h="svelte-1q2f2eu">Get started with HF DLCs on EC2.
	Get started with HF DLCs on ECS.
	Get started with HF DLCs on EKS.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/hub-docs/blob/main/docs/sagemaker/getting-started/deploy.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p>

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