add section in how to

pagezyhf · pagezyhf · commit 70a3db32e153 · 2025-04-24T14:10:32.000+02:00
diff --git a/docs/sagemaker/_toctree.yml b/docs/sagemaker/_toctree.yml
@@ -21,8 +21,14 @@
 - sections:
     - local: how-to/index
       title: Introduction
-    - local: how-to/get-started-sagemaker-sdk
-      title: Get started with the SageMaker SDK
+    - local: how-to/sagemaker-sdk-quickstart
+      title: SageMaker SDK Quickstart
+    - local: how-to/jumpstart-quickstart
+      title: Jumpstart Quickstart
+    - local: how-to/bedrock-quickstart
+      title: Bedrock Quickstart
+    - local: how-to/compute-services-quickstart
+      title: EC2, ECS and EKS Quickstart
     - local: how-to/deploy-sagemaker-sdk
       title: Deploy models with SageMaker SDK
     - local: how-to/training-sagemaker-sdk
diff --git a/docs/sagemaker/deploy.md b/docs/sagemaker/deploy.md
@@ -6,32 +6,30 @@ Deploying Hugging Face models on AWS is streamlined through various services, ea
 
 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.
 
-To get started, check out this tutorial.
+[Sagemaker SDK Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/sagemaker-sdk-quickstart)
 
 ## With Sagemaker Jumpstart
 
 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)
 
-To get started, check out this tutorial.
+[Sagemaker Jumpstart Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/jumpstart-quickstart)
 
 ## With AWS Bedrock
 
 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.
 
-To get started, check out this [blogpost](https://huggingface.co/blog/bedrock-marketplace?).
+[AWS Bedrock Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/bedrock-quickstart)
 
 ## With Hugging Face Inference Endpoints
 
 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.
 
-[Get started with Hugging Face Inference Endpoints](https://huggingface.co/docs/inference-endpoints/main/en/index).
+[Hugging Face Inference Endpoints Quickstart](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint).
 
 ## With ECS, EKS, and EC2
 
 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.
 
 AWS Elastic Container Service (ECS), Elastic Kubernetes Service (EKS), and Elastic Compute Cloud (EC2) allow you to leverage DLCs directly.
 
-Get started with HF DLCs on EC2. 
-Get started with HF DLCs on ECS.
-Get started with HF DLCs on EKS.
+[EC2, ECS and EKS Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/compute-services-quickstart)
diff --git a/docs/sagemaker/examples/index.md b/docs/sagemaker/examples/index.md
@@ -1 +1,19 @@
-# Examples
+# Examples
+
+Take a look at our examples about using Hugging Face models on AWS.
+
+## Sagemaker SDK
+
+- 
+
+## Jumpstart
+
+- 
+
+## Bedrock
+
+- 
+
+## EC2, ECS and EKS
+
+- 
diff --git a/docs/sagemaker/how-to/bedrock-quickstart.md b/docs/sagemaker/how-to/bedrock-quickstart.md
@@ -0,0 +1,103 @@
+# Use Hugging Face models with Amazon Bedrock
+
+We are excited to announce that popular open models from Hugging Face are now available on Amazon Bedrock in the new Bedrock Marketplace! AWS customers can now deploy [83 open models](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/model-catalog) with Bedrock Marketplace to build their Generative AI applications.
+
+Under the hood, Bedrock Marketplace model endpoints are managed by Amazon Sagemaker Jumpstart. 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.
+
+When registering your Sagemaker Jumpstart endpoints in Amazon Bedrock, you only pay for the Sagemaker compute resources and regular Amazon Bedrock APIs prices are applicable.
+
+In this blog we will show you how to deploy [Gemma 2 27B Instruct](https://huggingface.co/google/gemma-2-27b-it) and use the model with Amazon Bedrock APIs. Learn how to:
+
+1. Deploy Google Gemma 2 27B Instruct
+2. Send requests using the Amazon Bedrock APIs
+3. Clean Up
+
+## Deploy Google Gemma 2 27B Instruct
+
+There are two ways to deploy an open model to be used with Amazon Bedrock:
+
+1. You can deploy your open model from the Bedrock Model Catalog.
+2. You can deploy your open model with Amazon Jumpstart and register it with Bedrock.
+
+Both ways are similar, so we will guide you through the Bedrock Model catalog.
+
+To get started, in the Amazon Bedrock console, make sure you are in one of the 14 regions where the Bedrock Marketplace is available. Then, you choose [“Model catalog”](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/model-catalog) in the “Foundation models” section of the navigation pane. Here, you can search for both serverless models and models available in Amazon Bedrock Marketplace. You filter results by “Hugging Face” provider and you can browse through the 83 open models available.
+
+For example, let’s search and select Google Gemma 2 27B Instruct.
+
+![model-catalog.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/bedrock-marketplace/model-catalog.png)
+
+Choosing the model opens the model detail page where you can see more information from the model provider such as highlights about the model, and usage including sample API calls.
+
+On the top right, let’s click on Deploy.
+
+![model-card.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/bedrock-marketplace/model-card.png)
+
+It brings you to the deployment page where you can select the endpoint name, the instance configuration and advanced settings related to networking configuration and service role used to perform the deployment in Sagemaker. Let’s use the default advanced settings and the recommended instance type.
+
+You are also required to accept the End User License Agreement of the model provider.
+
+On the bottom right, let’s click on Deploy.
+
+![model-deploy.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/bedrock-marketplace/model-deploy.png)
+
+We just launched the deployment of  GoogleGemma 2 27B Instruct model on a ml.g5.48xlarge instance, hosted in your Amazon Sagemaker tenancy, compatible with Amazon Bedrock APIs!
+
+The endpoint deployment can take several minutes. It will appear in the “Marketplace deployments” page, which you can find in the “Foundation models” section of the navigation pane.
+
+## Use the model with Amazon Bedrock APIs
+
+You can quickly test the model in the Playground through the UI.  However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
+
+From the list of managed deployments, choose your model deployment to copy its endpoint ARN.
+
+![model-arn.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/bedrock-marketplace/model-arn.png)
+
+You can query your endpoint using the AWS SDK in your preferred language or with the AWS CLI.
+
+Here is an example using Bedrock Converse API through the AWS SDK for Python (boto3):
+
+```python
+import boto3
+
+bedrock_runtime = boto3.client("bedrock-runtime")
+
+# Add your bedrock endpoint arn here.
+endpoint_arn = "arn:aws:sagemaker:<AWS::REGION>:<AWS::AccountId>:endpoint/<Endpoint_Name>"
+
+# Base inference parameters to use.
+inference_config = {
+	"maxTokens": 256,
+	"temperature": 0.1,
+	"topP": 0.999,
+}
+
+# Additional inference parameters to use.
+additional_model_fields = {"parameters": {"repetition_penalty": 0.9, "top_k": 250, "do_sample": True}}
+response = bedrock_runtime.converse(
+	modelId=endpoint_arn,
+	messages=[
+		{
+			"role": "user",
+			"content": [
+				{
+					"text": "What is Amazon doing in the field of generative AI?",
+				},
+			]
+		},
+		],
+	inferenceConfig=inference_config,
+	additionalModelRequestFields=additional_model_fields,
+)
+print(response["output"]["message"]["content"][0]["text"])
+```
+
+```python
+"Amazon is making significant strides in the field of generative AI, applying it across various products and services. Here's a breakdown of their key initiatives:\n\n**1. Amazon Bedrock:**\n\n* This is their **fully managed service** that allows developers to build and scale generative AI applications using models from Amazon and other leading AI companies. \n* It offers access to foundational models like **Amazon Titan**, a family of large language models (LLMs) for text generation, and models from Cohere"
+```
+
+That’s it! If you want to go further, have a look at the [Bedrock documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html).
+
+## Clean up
+
+Don’t forget to delete your endpoint at the end of your experiment to stop incurring costs! At the top right of the page where you grab the endpoint ARN, you can delete your endpoint by clicking on “Delete”.
diff --git a/docs/sagemaker/how-to/compute-services-quickstart.md b/docs/sagemaker/how-to/compute-services-quickstart.md
@@ -0,0 +1,3 @@
+# EC2, ECS and EKS Quickstart
+
+This page is under construction, bear with us!
diff --git a/docs/sagemaker/how-to/index.md b/docs/sagemaker/how-to/index.md
@@ -1,7 +1,19 @@
 # How to
 
+Take a look at our tutorials about using Hugging Face models on AWS.
+
 ## Sagemaker SDK
 
+- [Sagemaker SDK Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/sagemaker-sdk-quickstart)
+
 ## Jumpstart
 
-## Bedrock
+- [Sagemaker Jumpstart Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/jumpstart-quickstart)
+
+## Bedrock
+
+- [AWS Bedrock Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/bedrock-quickstart)
+
+## EC2, ECS and EKS
+
+- [EC2, ECS and EKS Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/compute-services-quickstart)
diff --git a/docs/sagemaker/how-to/jumpstart-quickstart.md b/docs/sagemaker/how-to/jumpstart-quickstart.md
@@ -0,0 +1,3 @@
+# Jumpstart Quickstart
+
+This page is under construction, bear with us!
diff --git a/docs/sagemaker/how-to/sagemaker-sdk-quickstart.md b/docs/sagemaker/how-to/sagemaker-sdk-quickstart.md
diff --git a/docs/sagemaker/train.md b/docs/sagemaker/train.md
@@ -6,14 +6,12 @@ Training Hugging Face models on AWS is streamlined through various services. Her
 
 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 training job management. With this integration, you can quickly create your own fine-tuned models, significantly reducing setup complexity and time to production.
 
-To get started, check out this example.
+[Sagemaker SDK Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/sagemaker-sdk-quickstart)
 
 ## With ECS, EKS, and EC2
 
 Hugging Face provides Training Deep Learning Containers (DLCs) to AWS users, optimized environments preconfigured with Hugging Face libraries for training, natively integrated in SageMaker SDK. However, the HF DLCs can also be used across other AWS services like ECS, EKS, and EC2.
 
 AWS Elastic Container Service (ECS), Elastic Kubernetes Service (EKS), and Elastic Compute Cloud (EC2) allow you to leverage DLCs directly.
 
-Get started with HF DLCs on EC2
-Get started with HF DLCs on ECS
-Get started with HF DLCs on EKS
+[EC2, ECS and EKS Quickstart](https://huggingface.co/docs/sagemaker/main/en/how-to/compute-services-quickstart)

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