Spaces:
Running
Running
File size: 3,114 Bytes
294261e 49e981f 294261e 41f047d 49e981f 41f047d 49e981f 41f047d 49e981f 41f047d 49e981f 41f047d 49e981f 41f047d ed4d320 41f047d 49e981f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
---
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> |