Today, we are excited 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 model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support learning (RL) action, which was utilized to fine-tune the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate inquiries and factor through them in a detailed way. This directed thinking process permits the model to produce more precise, transparent, and detailed responses. This model fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be integrated into different workflows such as agents, rational reasoning and information interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling effective reasoning by routing inquiries to the most relevant expert "clusters." This method allows the design to specialize in different problem domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 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, more efficient designs to simulate the behavior and reasoning patterns of the larger 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 design, we advise deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine designs against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using 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 ask for a limit increase, create a limit boost request and connect to your account group.

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) approvals to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful material, and examine models against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model reactions deployed 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 produce the guardrail, see the GitHub repo.

The general circulation involves 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 to the design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show inference using 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, complete the following actions:

1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to invoke the model. 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 supplies vital details about the design's capabilities, rates structure, and forum.altaycoins.com application standards. You can find detailed use directions, including sample API calls and code snippets for integration. The model supports numerous text generation tasks, including material production, code generation, and question answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. The page likewise includes release alternatives and licensing details to help you start with DeepSeek-R1 in your applications. 3. To start utilizing 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, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of circumstances, go into a variety of instances (in between 1-100). 6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive interface where you can try out different triggers and change model criteria like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, material for reasoning.

This is an outstanding method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimal outcomes.

You can quickly test the design in the play area through the UI. However, to conjure up the deployed 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 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 create the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up 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 options that you can deploy with just a couple of 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 SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the technique that finest suits 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 prompted to develop a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if suitable), 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 see the model details page.
  
  The design details page includes the following details:
  
  - The model name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you release the model, it's advised to review the model details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the immediately produced name or create a customized one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of instances (default: 1). Selecting appropriate circumstances types and counts is important for expense and performance optimization. Monitor your deployment to adjust 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 setups for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.
    
    The implementation process can take numerous minutes to finish.
    
    When deployment is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
12. In the Managed deployments 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 deleting the proper release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed will sustain expenses 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 deploy the DeepSeek-R1 model using 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 Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business build innovative solutions using AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek delights in treking, viewing movies, and trying different 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 an Expert Solutions Architect working on 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 building services that help customers accelerate their AI journey and unlock organization value.