Today, we are thrilled 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 parameters to develop, experiment, and responsibly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing function is its support learning (RL) step, which was utilized to improve the model’s reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 employs a (CoT) approach, implying it’s geared up to break down intricate inquiries and factor through them in a detailed manner. This directed reasoning process allows the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry’s attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, rational reasoning and data analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most appropriate professional “clusters.” This approach enables the model to concentrate on different issue domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. 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 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and setiathome.berkeley.edu 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to simulate the behavior disgaeawiki.info and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and assess designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release 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 using 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 request a limit increase, develop a limit increase demand and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and assess designs against key security requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess 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 develop the guardrail, see the GitHub repo.

The basic circulation includes the following steps: 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 out to the design for reasoning. After getting the model’s output, another guardrail check is used. If the output passes this final check, it’s returned as the last 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 took place at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.

The design detail page supplies necessary details about the design’s abilities, rates structure, and implementation guidelines. You can discover detailed usage directions, including sample API calls and code bits for integration. The model supports different text generation jobs, including content development, code generation, and concern answering, using its support learning optimization and CoT thinking abilities. The page likewise includes implementation options 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 prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, enter a variety of circumstances (between 1-100). 6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire to review these settings to align with your organization’s security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the release is total, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive user interface where you can experiment with different triggers and change model specifications like temperature level and optimum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimal results. For instance, content for inference.

This is an exceptional way to explore the model’s reasoning and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak your prompts for optimal results.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 model 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 develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to generate text based upon 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 few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both techniques to help you choose the approach that best fits 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 develop a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design web browser shows available designs, with details like the supplier name and design capabilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card reveals essential details, including:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke 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 supplier details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details. - Technical specs.
• Usage standards
  
  Before you release the design, it’s suggested to review the design details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, surgiteams.com utilize the instantly generated name or produce a custom-made one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of instances (default: 1). Selecting appropriate instance types and counts is vital for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for pipewiki.org sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the model.
    
    The deployment procedure can take a number of minutes to finish.
    
    When implementation is total, your endpoint status will change to InService. At this point, the model is all set to accept inference requests through the endpoint. You can monitor higgledy-piggledy.xyz the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime customer and yewiki.org integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Tidy up
    
    To prevent undesirable charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
12. In the Managed deployments area, find the endpoint you wish 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 correct implementation: 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 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 release the DeepSeek-R1 design utilizing 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 models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 services using AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his complimentary time, Vivek takes pleasure in treking, viewing motion pictures, and trying various cuisines.
    
    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 Specialist Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about constructing options that assist customers accelerate their AI journey and unlock organization value.