import from moon-landing codebase

README.md

@@ -3,31 +3,89 @@ title: README
 emoji: 🐠
 colorFrom: pink
 colorTo: purple
-sdk: gradio
-app_file: app.py
+sdk: static
 pinned: false
 ---
 
-# Configuration
+<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>
 
-`title`: _string_  
-Display title for the Space
+<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>
 
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
+<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 Contrainers (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>