Today, we are thrilled 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 model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support discovering to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, rational thinking and data analysis jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing effective inference by routing questions to the most pertinent expert "clusters." This approach allows the model to focus on various issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon 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 efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.

You can deploy 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, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're using 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 request a limitation increase, create a limitation increase request and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and examine designs against essential security requirements. You can execute security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The basic flow includes the following actions: First, the system gets an input for archmageriseswiki.com the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent 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 last result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 steps:

1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. At the time of writing 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 provider and pick the DeepSeek-R1 model.

The model detail page offers necessary details about the model's capabilities, pricing structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code bits for combination. The model supports various text generation tasks, including material production, code generation, and question answering, using its support finding out optimization and CoT thinking capabilities. The page also includes release alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, get in a number of circumstances (between 1-100). 6. For example type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may desire to review these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive user interface where you can explore various triggers and change design specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for reasoning.

This is an excellent method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, helping you comprehend how the model reacts to different inputs and letting you tweak your prompts for optimal outcomes.

You can quickly check 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 inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a demand to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

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

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the method that finest fits 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, pick Studio in the navigation pane. 2. First-time users will be triggered to develop a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

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

4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. Each design card shows crucial details, including:

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to see the design details page.
  
  The design details page consists of the following details:
  
  - The design name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you release the design, it's recommended to review the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, utilize the instantly produced name or develop a customized one.

8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of instances (default: 1). Selecting suitable circumstances types and counts is crucial for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.
    
    The implementation procedure can take a number of minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can invoke the design utilizing 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 necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra demands 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 utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the design using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
12. In the Managed deployments section, find the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete 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 start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 helps emerging generative AI companies develop innovative solutions using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his spare time, Vivek delights in hiking, watching movies, and attempting different cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science 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 collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing options that assist clients accelerate their AI journey and unlock service value.