sagemaker/pr_1706/en/getting-started/index.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Hugging Face on AWS&quot;,&quot;local&quot;:&quot;hugging-face-on-aws&quot;,&quot;sections&quot;:[],&quot;depth&quot;: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="{&quot;title&quot;:&quot;Hugging Face on AWS&quot;,&quot;local&quot;:&quot;hugging-face-on-aws&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="hugging-face-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="#hugging-face-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>Hugging Face on AWS</span></h1> <p data-svelte-h="svelte-110m2k6"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sagemaker/cover.png" alt="cover"></p> <p data-svelte-h="svelte-qw7acg">Hugging Face partners with Amazon Web Services (AWS) to democratize artificial intelligence (AI), enabling developers to seamlessly build, train, and deploy state-of-the-art machine learning models using AWS’s robust cloud infrastructure. ​</p> <p data-svelte-h="svelte-b4ncu4">This collaboration aims to offer developers access to an everyday growing catalog of pre-trained models and dataset from the Hugging Face Hub, using Hugging Face open-source libraries across a broad spectrum of AWS services and hardware platforms.</p> <p data-svelte-h="svelte-1skuoom">We build new experiences for developers to seamlessly train and deploy Hugging Face models whether they use AWS AI platforms such as Amazon SageMaker AI and AWS Bedrock, or AWS Compute services such as Elastic Container Service (ECS), Elastic Kubernetes Service (EKS), and virtual servers on Amazon Elastic Compute Cloud (EC2).</p> <p data-svelte-h="svelte-1rw6c52">We develop new tools to simplify the adoption of custom AI accelerators like AWS Inferentia and AWS Trainium, designed to enhance the performance and cost-efficiency of machine learning workloads.</p> <p data-svelte-h="svelte-wmv839">By combining Hugging Face’s open-source models and libraries with AWS’s scalable and secure cloud services, developers can more easily and affordably incorporate advanced AI capabilities into their applications.</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/index.md" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</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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