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, in addition to the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down complex inquiries and reason through them in a detailed manner. This assisted reasoning process allows the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, sensible reasoning and data analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, enabling effective reasoning by routing inquiries to the most appropriate expert "clusters." This approach allows the model to specialize in various problem domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 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 comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess designs against key security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security 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 circumstances in the AWS Region you are deploying. To ask for a limitation boost, produce a limitation boost request and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and assess designs against crucial security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general circulation involves the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.

The model detail page provides important details about the model's abilities, rates structure, and execution standards. You can find detailed use directions, including sample API calls and code snippets for combination. The model supports different text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning capabilities. The page likewise includes deployment choices and licensing details to help you start with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Number of circumstances, go into a number of instances (in between 1-100). 6. For example type, select your circumstances type. For optimal efficiency 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 personal cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might want to review these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive interface where you can explore various prompts and change model parameters like temperature and maximum length. When using 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 exceptional method to explore the model's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the model responds to various inputs and letting you tweak your triggers for optimum outcomes.

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

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a request to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated 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 usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:

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

The design internet browser displays available designs, with details like the provider name and kousokuwiki.org design abilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card reveals key details, including:

- Model name

• Provider name
• Task category (for instance, Text Generation). badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to see the model details page.
  
  The model details page includes the following details:
  
  - The design name and provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage standards
  
  Before you release the design, it's recommended to examine the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, utilize the instantly produced name or create a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1). Selecting proper circumstances types and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.
    
    The deployment process can take a number of minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
12. In the Managed deployments section, find the endpoint you desire to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek enjoys hiking, viewing motion pictures, and trying various foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing solutions that assist customers accelerate their AI journey and unlock business value.