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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses support learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) action, which was used to improve the model's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's geared up to break down complex questions and factor through them in a detailed manner. This assisted reasoning procedure allows the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, sensible reasoning and information interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most appropriate professional "clusters." This method enables the design to concentrate on different issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, develop a limitation increase request and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and examine designs against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic circulation includes the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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

The model detail page offers essential details about the model's abilities, rates structure, and implementation guidelines. You can find detailed usage instructions, consisting of sample API calls and code snippets for integration. The model supports various text generation tasks, including content production, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page also consists of deployment alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, go into a variety of instances (between 1-100). 6. For example type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and yewiki.org file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the model.

When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play ground to access an interactive interface where you can explore different triggers and change design criteria like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For example, content for reasoning.

This is an exceptional way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the design reacts to numerous inputs and letting you fine-tune your prompts for ideal results.

You can quickly evaluate the model in the play area through the UI. However, to invoke the deployed 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 demonstrates how to carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_ customer, sets up reasoning specifications, and sends a request to create text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

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

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest suits your requirements.

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 prompted to produce a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model browser displays available designs, with details like the company name and design capabilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. Each model card shows essential details, including:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the model details page.
  
  The model details page consists of the following details:
  
  - The model name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage guidelines
  
  Before you release the design, it's recommended to review the design details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, utilize the immediately produced name or produce a custom-made one.

8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of instances (default: 1). Selecting appropriate instance types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The release procedure can take numerous minutes to finish.
    
    When release is total, your endpoint status will change to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use 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:
    
    Clean up
    
    To prevent unwanted charges, complete the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
12. In the Managed deployments section, find the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're deleting the correct release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his leisure time, Vivek delights in hiking, viewing movies, and attempting various cuisines.
    
    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 Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and pipewiki.org tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing services that help consumers accelerate their AI journey and unlock service value.