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Today, we are excited 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 design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its reinforcement learning (RL) action, which was used to refine the model's actions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate inquiries and factor through them in a detailed way. This assisted thinking procedure permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, logical reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective inference by routing questions to the most pertinent professional "clusters." This method permits the model to concentrate on different problem domains while maintaining total performance. 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 deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design 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 effective designs to the habits and thinking patterns of the bigger 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 suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need 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 circumstances in the AWS Region you are deploying. To request a limit increase, create a limitation increase demand and reach out to your account group.

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) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful content, and evaluate designs against key security criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic flow includes 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 design for reasoning. After getting 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 stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections 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 models (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 brochure under Foundation designs 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 provider and choose the DeepSeek-R1 design.

The model detail page supplies essential details about the design's abilities, prices structure, and implementation guidelines. You can find detailed use instructions, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of material creation, code generation, and concern answering, using its support discovering optimization and CoT thinking capabilities. The page also consists of release choices and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the release 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 Number of circumstances, get in a number of instances (in between 1-100). 6. For example type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, higgledy-piggledy.xyz service function authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start using the model.

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 user interface where you can experiment with various triggers and adjust model criteria like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference.

This is an excellent way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the model responds to numerous inputs and letting you fine-tune your prompts for optimal outcomes.

You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference 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 develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a demand to create text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated 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 use case, with your information, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the approach that finest suits your requirements.

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 triggered to produce a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The model browser shows available designs, with details like the service provider name and model abilities.

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the model details page.
  
  The design details page consists of the following details:
  
  - The model name and service 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 specs.
• Usage guidelines
  
  Before you deploy the model, it's recommended to review the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, use the instantly created name or develop a custom-made one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of instances (default: 1). Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your release to adjust 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 configurations for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.
    
    The release procedure can take a number of minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get started 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 approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and range 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
12. In the Managed implementations section, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released will sustain costs 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 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 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 assists emerging generative AI companies develop ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of large language models. In his totally free time, Vivek delights in hiking, viewing movies, and trying different foods.
    
    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 technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect working on 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 hub. She is passionate about constructing solutions that assist clients accelerate their AI journey and unlock business value.