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Today, we are delighted 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, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying function is its reinforcement learning (RL) step, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated questions and reason through them in a detailed manner. This directed thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation design that can be integrated into various workflows such as agents, sensible reasoning and data analysis 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, making it possible for effective inference by routing questions to the most pertinent expert "clusters." This approach allows the design to focus on different issue domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based on popular open designs 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 imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess models against crucial 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 develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 deploying. To request a limitation boost, create a limit boost 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) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous content, and assess models against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design reactions 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 includes 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 out to the design for reasoning. After receiving the model'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 occurred at the input or output phase. The examples showcased in the following sections 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total 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 choose the DeepSeek-R1 model.

The design detail page provides essential details about the model's capabilities, prices structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports various text generation jobs, including content production, code generation, and question answering, utilizing its support learning optimization and CoT thinking abilities. The page also includes deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To begin 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, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of instances, get in a number of instances (between 1-100). 6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may 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 check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can explore different triggers and change model specifications like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, material for inference.

This is an exceptional method to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground provides instant feedback, assisting you understand how the model reacts to different inputs and letting you tweak your prompts for optimum results.

You can quickly check the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up 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) center with FMs, integrated algorithms, and prebuilt ML services that you can release with simply 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 convenient methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the method that finest matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions 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 create a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The design web browser displays available models, with details like the service provider name and model abilities.

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if applicable), indicating that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the model 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 essential details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage guidelines
  
  Before you release the design, it's recommended to examine the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, utilize the automatically generated name or create a customized one.

8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of circumstances (default: 1). Selecting suitable circumstances types and counts is essential for cost 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 configurations for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The deployment procedure can take several minutes to complete.
    
    When release is total, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize 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:
    
    Tidy up
    
    To avoid unwanted charges, complete the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
12. In the Managed implementations section, find the endpoint you want to delete.
13. Select the endpoint, and mediawiki.hcah.in on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're erasing the proper deployment: 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 checked out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 ingenious options using AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his downtime, Vivek takes pleasure in hiking, enjoying movies, 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 Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing solutions that assist customers accelerate their AI journey and unlock organization worth.