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Today, we are delighted to reveal 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, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly 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 comparable steps to deploy the distilled versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses support learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to improve the design's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down complicated queries and reason through them in a detailed manner. This directed thinking procedure permits the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical reasoning and data interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, enabling effective reasoning by routing queries to the most relevant specialist "clusters." This method permits the design to concentrate on different problem domains while maintaining overall effectiveness. 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 circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of smaller, more efficient designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across 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 confirm 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 deploying. To ask for a limit increase, create a limitation increase demand and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous content, and assess models against essential security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design responses released 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 circulation 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 model for inference. 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 happened at the input or output phase. The examples showcased in the following areas demonstrate inference 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, choose Model catalog under Foundation designs in the navigation pane. At the time of writing this post, you can use 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 service provider and pick the DeepSeek-R1 model.

The design detail page provides important details about the design's abilities, rates structure, and execution guidelines. You can discover detailed usage instructions, including sample API calls and code snippets for combination. The model supports different text generation tasks, consisting of material production, wiki.rolandradio.net code generation, and question answering, utilizing its support discovering optimization and CoT reasoning capabilities. The page also consists of deployment options and licensing details to assist you get begun with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, pick Deploy.

You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, enter a variety of circumstances (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 recommended. Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might want to examine these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the implementation is complete, you can test 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 various prompts and change model specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for inference.

This is an excellent method to check out the design's thinking and text generation abilities before integrating it into your applications. The playground provides immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.

You can rapidly test the model in the playground through the UI. However, to invoke the deployed design 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 carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce 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, hb9lc.org configures reasoning parameters, and sends out a request to create text based upon 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 solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the method that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:

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

The design internet browser shows available designs, with details like the supplier name and design abilities.

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to view the design details page.
  
  The design details page consists of the following details:
  
  - The design name and provider 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 specifications. - Usage guidelines
  
  Before you deploy the design, it's advised to examine the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, utilize the automatically generated name or produce a custom one.

8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate circumstances types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the model.
    
    The release procedure can take a number of minutes to complete.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 set up 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 use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
12. In the Managed implementations area, locate the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the appropriate release: 1. Endpoint name.
15. Model name.
16. Endpoint 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 wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored 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 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 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 companies develop innovative services utilizing AWS services and accelerated calculate. Currently, he is focused on establishing strategies for trademarketclassifieds.com fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek takes pleasure in treking, enjoying motion pictures, and attempting 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 a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that assist clients accelerate their AI journey and unlock service value.