Today, we are excited 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, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, 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 similar actions to release the distilled variations of the models too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate inquiries and factor through them in a detailed way. This guided reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, rational reasoning and data interpretation tasks.

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 efficient reasoning by routing questions to the most pertinent professional "clusters." This approach permits the design to concentrate on different issue domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 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 effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, 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 recommend deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate models against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across 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, select 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 ask for a limitation increase, create a limit boost request and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous material, and evaluate models against essential safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use 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 develop the guardrail, see the GitHub repo.

The basic flow involves the following actions: 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 to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. At the time of composing 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 company and pick the DeepSeek-R1 design.

The design detail page provides vital details about the design's capabilities, pricing structure, and execution guidelines. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, including material creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT reasoning capabilities. The page also includes implementation choices and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, go into a variety of instances (between 1-100). 6. For example type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may wish to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive interface where you can try out various prompts and change model parameters like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for inference.

This is an exceptional method to explore the design's thinking and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimal results.

You can rapidly check the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a request to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just 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 offers 2 hassle-free methods: utilizing the instinctive 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 fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 using 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, select JumpStart in the navigation pane.

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

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. Each model card reveals key details, including:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to see the model details page.
  
  The design details page consists of the following details:
  
  - The model name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab includes important details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the model, it's suggested to review the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the immediately produced name or develop a customized one.

8. For archmageriseswiki.com Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1). Selecting appropriate circumstances types and counts is essential for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.
    
    The implementation procedure can take several minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show pertinent 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design 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 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 undesirable charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
12. In the Managed deployments area, locate the endpoint you want 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 appropriate 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 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 at AWS. He assists emerging generative AI companies construct ingenious solutions using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek takes pleasure in treking, enjoying films, and attempting 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 Science and Bioinformatics.
    
    Jonathan Evans is an Expert 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 constructing options that assist consumers accelerate their AI journey and unlock service worth.