Today, we are delighted to reveal 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, bytes-the-dust.com DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) step, which was used to improve the model's actions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's geared up to break down complicated questions and reason through them in a detailed way. This assisted thinking process allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as agents, sensible thinking and data analysis tasks.

DeepSeek-R1 uses 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 efficient reasoning by routing inquiries to the most pertinent professional "clusters." This method permits the design to focus on various issue domains while maintaining total efficiency. 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 model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based upon 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 efficient designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher 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 utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and examine designs against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, develop a limitation increase request and reach out 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) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and evaluate designs against key safety criteria. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general circulation involves the following steps: First, the system gets 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 model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. 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 sections demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 designs in the navigation pane. At the time of composing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.

The design detail page supplies necessary details about the design's abilities, prices structure, and execution guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The model supports numerous text generation tasks, including content creation, code generation, and concern answering, using its support learning optimization and CoT reasoning abilities. The page likewise consists of implementation choices and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, pick Deploy.

You will be triggered to set up 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 circumstances (between 1-100). 6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and change design parameters like temperature level and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, material for inference.

This is an outstanding way to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The playground provides immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimal results.

You can quickly test the model 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 inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends 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, integrated algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the approach that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

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

The design internet browser displays available designs, with details like the service provider name and design abilities.

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the model card to view the design details page.
  
  The model details page includes the following details:
  
  - The model name and service provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details. requirements.
• Usage guidelines
  
  Before you deploy the model, it's recommended to evaluate the model details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, utilize the instantly generated name or produce a customized one.

8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of circumstances (default: 1). Selecting suitable instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation 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 accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the model.
    
    The deployment procedure can take several minutes to complete.
    
    When implementation is total, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra requests 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
    
    Tidy up
    
    To avoid unwanted charges, complete the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the design using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
12. In the Managed deployments section, locate the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're erasing the proper release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released 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 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 begin. 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 Beginning 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 develop ingenious options using AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek takes pleasure in hiking, watching motion pictures, and trying various foods.
    
    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 team at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing services that assist consumers accelerate their AI journey and unlock business value.