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Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its support learning (RL) action, which was used to refine the model's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's equipped to break down complex questions and reason through them in a detailed manner. This directed thinking process permits the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, logical reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient inference by routing inquiries to the most pertinent specialist "clusters." This technique enables the model to focus on various issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and evaluate models against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation boost, produce a limitation boost request and connect to your account group.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging material, and evaluate designs against crucial security criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The general circulation includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.

The design detail page supplies essential details about the model's capabilities, rates structure, and execution standards. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation tasks, including content creation, higgledy-piggledy.xyz code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. The page also includes release options and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of instances, go into a variety of instances (in between 1-100). 6. For Instance type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and adjust model specifications like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, material for reasoning.

This is an excellent method to check out the model's thinking and text generation capabilities before integrating it into your applications. The play area provides instant feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimum outcomes.

You can rapidly check the design in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and . You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a demand to produce text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the technique that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane. 2. First-time users will be triggered to produce a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The model internet browser shows available designs, with details like the provider name and design abilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. Each design card reveals crucial details, consisting of:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to see the design details page.
  
  The model details page consists of the following details:
  
  - The design name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab includes important details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage standards
  
  Before you deploy the model, it's advised to examine the model details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, use the automatically created name or produce a custom one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is important for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the design.
    
    The release process can take a number of minutes to finish.
    
    When release is total, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
    
    Tidy up
    
    To avoid unwanted charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.
12. In the Managed deployments section, locate the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the appropriate deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build innovative options using AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language designs. In his leisure time, Vivek enjoys treking, viewing motion pictures, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building services that help customers accelerate their AI journey and unlock organization value.