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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement learning (RL) step, which was to fine-tune the design's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated queries and factor through them in a detailed manner. This assisted thinking procedure enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, rational thinking and information interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing queries to the most pertinent specialist "clusters." This method permits the model to focus on various problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher 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 place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're utilizing 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 releasing. To ask for a limit increase, produce a limitation increase demand and connect to your account group.

Because you will be releasing this model 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 permissions to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and assess designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic flow 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 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 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 happened at the input or output phase. The examples showcased in the following areas demonstrate inference utilizing 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, 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 use the InvokeModel API to invoke the model. It doesn't 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 supplies necessary details about the model's abilities, pricing structure, and implementation guidelines. You can find detailed use instructions, including sample API calls and code bits for combination. The model supports various text generation jobs, including content production, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning capabilities. The page likewise consists of implementation choices and licensing details to help you get started with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be prompted to set up 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 Variety of instances, enter a variety of circumstances (between 1-100). 6. For Instance type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment 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 interface where you can explore different prompts and change design criteria like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, material for reasoning.

This is an outstanding method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, helping you comprehend how the design responds to various inputs and letting you fine-tune your prompts for optimum results.

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

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends 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, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production utilizing either the UI or archmageriseswiki.com SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that best fits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:

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

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

4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. Each model card reveals key details, consisting of:

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the design details page.
  
  The design details page includes the following details:
  
  - The design name and service provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you release the design, it's advised to evaluate the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, utilize the automatically created name or develop a custom one.

8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1). Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the design.
    
    The implementation process can take a number of minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations 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 design is provided in the Github here. You can clone the note pad and range 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 likewise utilize 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 displayed in the following code:
    
    Clean up
    
    To avoid unwanted charges, complete the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
12. In the Managed implementations section, find the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're erasing 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 costs if you leave it running. Use the following code to erase 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, 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 business build innovative services utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his complimentary time, Vivek delights in treking, viewing films, and trying different 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 technology and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing options that assist customers accelerate their AI journey and unlock organization worth.