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, together with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement knowing (RL) action, which was used to improve the model's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate questions and reason through them in a detailed manner. This assisted reasoning process enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, logical reasoning and information interpretation jobs.

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

DeepSeek-R1 distilled models bring the thinking 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 sized, more efficient models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy 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, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit increase, develop a limitation increase request and reach out to your account team.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

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

The basic circulation includes the following steps: First, the system receives 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 model's output, another guardrail check is applied. 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 happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (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 brochure under Foundation models in the navigation pane. At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.

The model detail page offers essential details about the design's abilities, rates structure, and execution guidelines. You can discover detailed use instructions, including sample API calls and code bits for integration. The model supports different text generation tasks, consisting of content creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning abilities. The page likewise consists of release choices and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, select Deploy.

You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, go into a variety of instances (in between 1-100). 6. For Instance type, select your instance type. For performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and adjust model specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, material for inference.

This is an outstanding way to check out the model's reasoning and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your triggers for optimal results.

You can rapidly check the model in the play ground through the UI. However, to conjure up the released design 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 demonstrates how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a request 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 services that you can deploy with just a few 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 model through SageMaker JumpStart provides two convenient approaches: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the technique that best suits your needs.

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

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
  
  5. Choose the model card to see the model details page.
  
  The design details page consists of the following details:
  
  - The design name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you deploy the model, it's recommended to evaluate the design details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, utilize the immediately generated name or create a customized one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable circumstances types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and raovatonline.org making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The implementation procedure can take numerous minutes to complete.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require 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 releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To prevent unwanted charges, complete the actions in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
    
    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 deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 Getting going 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 build ingenious solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference efficiency of large language designs. In his downtime, 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 team 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 dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing services that help customers accelerate their AI journey and unlock company value.