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  title: README
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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
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+ <p class="lg:col-span-3">
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+ Hugging Face is working with Amazon Web Services to make it easier than
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+ ever for startups and enterprises to <strong
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+ >train and deploy Hugging Face models in Amazon SageMaker</strong
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+ >.
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+ </p>
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+ <a
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+ href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"
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+ class="block overflow-hidden group"
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+ >
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+ <div
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+ 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"
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+ >
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+ <img
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+ alt=""
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+ src="/front/assets/promo/amazon_sagemaker_x_huggingface.png"
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+ class="w-40"
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+ />
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+ </div>
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+ <div class="underline">Read announcement blog post</div>
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+ </a>
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+ <a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden">
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+ <img
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+ alt=""
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+ src="/front/assets/promo/amazon_walkthrough_thumbnail.png"
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+ class="w-full h-40 object-cover mb-2 bg-gray-300 rounded-lg"
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+ />
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+ <div class="underline">Video Walkthrough with Philipp Schmid</div>
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+ </a>
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+ <a
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+ href="https://huggingface.co/docs/sagemaker"
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+ class="block overflow-hidden group"
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+ >
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+ <div
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+ class="w-full h-40 object-cover mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start"
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+ >
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+ <img
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+ alt=""
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+ src="/front/assets/promo/amazon_documentation.png"
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+ class="w-44 p-4"
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+ />
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+ </div>
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+ <div class="underline">Documentation: Hugging Face in SageMaker</div>
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+ </a>
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+ <div class="lg:col-span-3">
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+ <p class="mb-2">
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+ To train Hugging Face models in Amazon SageMaker, you can use the
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+ Hugging Face Deep Learning Containers (DLCs) and the Hugging Face
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+ support in the SageMaker Python SDK.
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+ </p>
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+ <p class="mb-2">
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+ The DLCs are fully integrated with the SageMaker distributed training
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+ libraries to train models more quickly using the latest generation of
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+ accelerated computing instances available on Amazon EC2. With the
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+ SageMaker Python SDK, you can start training with just a single line of
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+ code, enabling your teams to move from idea to production more quickly.
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+ </p>
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+ <p class="mb-2">
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+ To deploy Hugging Face models in Amazon SageMaker, you can use the
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+ Hugging Face Deep Learning Containers with the new Hugging Face
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+ Inference Toolkit.
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+ </p>
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+ <p class="mb-2">
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+ With the new Hugging Face Inference DLCs, deploy your trained models for
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+ inference with just one more line of code, or select any of the 10,000+
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+ models publicly available on the ๐Ÿค— Hub, and deploy them with Amazon
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+ SageMaker, to easily create production-ready endpoints that scale
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+ seamlessly, with built-in monitoring and enterprise-level security.
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+ </p>
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+ <p>
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+ More information: <a
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+ href="https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/"
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+ class="underline">AWS blog post</a
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+ >,
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+ <a
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+ href="https://discuss.huggingface.co/c/sagemaker/17"
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+ class="underline">Community Forum</a
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+ >
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+ </p>
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+ </div>
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+ </div>