Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen models 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 varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) action, which was utilized to refine the model's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated inquiries and factor through them in a detailed way. This directed reasoning process allows the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, rational thinking and information interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient reasoning by routing queries to the most pertinent expert "clusters." This technique enables the design to specialize in various problem domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 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 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.

You can deploy DeepSeek-R1 model 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 use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving 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 circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 deploying. To request a limit increase, create a limit increase request and connect 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 utilize Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and evaluate designs against crucial safety requirements. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The general circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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 utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.

The design detail page provides important details about the design's abilities, prices structure, and implementation standards. You can find detailed usage guidelines, including sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. The page also consists of deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be triggered 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 Variety of instances, enter a variety of instances (in between 1-100). 6. For Instance type, choose your instance type. For ideal 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 function authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive user interface where you can explore different prompts and change design specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for inference.

This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your prompts for ideal outcomes.

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

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing 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, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and wiki.myamens.com sends out a request to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in 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 use case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the technique 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, pick Studio in the navigation pane. 2. First-time users will be triggered to develop a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model web browser shows available designs, with details like the company name and design abilities.

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
  
  5. Choose the design card to see the model details page.
  
  The design details page consists of the following details:
  
  - The design name and provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you deploy the model, it's suggested to examine the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the instantly produced name or produce a custom one.

8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1). Selecting proper instance types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.
    
    The release process can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime client and integrate 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 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 provided in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
12. In the Managed releases section, locate the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the proper deployment: 1. Endpoint name.
15. Model name.
16. status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase 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 deploy the DeepSeek-R1 model 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 models, 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 construct ingenious options using AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek delights in treking, watching films, and trying different cuisines.
    
    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 Science 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 item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about developing services that help clients accelerate their AI journey and unlock organization value.