Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs 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, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes support learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement knowing (RL) step, which was used to improve the design's responses beyond the standard pre-training and fine-tuning procedure. By RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's geared up to break down complex inquiries and factor through them in a detailed way. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing effective reasoning by routing inquiries to the most appropriate specialist "clusters." This method permits the design to specialize in various problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize 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 reasoning capabilities of the main R1 design to more efficient architectures based on 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, more efficient designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, create a limitation boost demand and reach out to your account group.

Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and assess models against essential security criteria. You can carry out security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model reactions deployed 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 develop the guardrail, see the GitHub repo.

The general flow 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 inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred 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, select Model brochure under Foundation models 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 supplier and pick the DeepSeek-R1 model.

The design detail page provides essential details about the model's abilities, rates structure, and execution guidelines. You can discover detailed usage instructions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, including material development, code generation, and concern answering, using its support learning optimization and CoT thinking capabilities. The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose 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 (between 1-50 alphanumeric characters). 5. For Number of circumstances, get in a variety of circumstances (in between 1-100). 6. For example type, select your circumstances type. For optimal efficiency 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 encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play ground to access an interactive interface where you can try out various triggers and adjust model criteria like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for reasoning.

This is an excellent method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies instant feedback, assisting you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for optimum results.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

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

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 release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the approach that best suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

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

The design internet browser displays available models, with details like the supplier name and design capabilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card shows crucial details, consisting of:

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to view the model details page.
  
  The design details page consists of the following details:
  
  - The model name and company 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 specifications.
• Usage guidelines
  
  Before you deploy the model, it's suggested to evaluate the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, use the automatically produced name or create a customized one.

8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting proper circumstances types and counts is crucial 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 configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
    
    The release procedure can take several minutes to finish.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations 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 model 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 inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, engel-und-waisen.de and execute it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the model utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments.
12. In the Managed implementations area, find the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the proper release: 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 wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored how you can access and deploy the DeepSeek-R1 design utilizing 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 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 business construct ingenious services utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his spare time, Vivek takes pleasure in treking, viewing films, and trying various 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 center. She is enthusiastic about building services that assist clients accelerate their AI journey and unlock organization value.