Today, we are excited 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its support knowing (RL) action, which was used to refine the model's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's equipped to break down complicated questions and reason through them in a detailed manner. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, rational thinking and data interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing effective inference by routing inquiries to the most pertinent specialist "clusters." This technique allows the model to specialize in various issue domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, wiki.whenparked.com 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and evaluate models against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. 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 boost, create a limitation 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 instructions, see Establish authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and assess designs against crucial security criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions deployed 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 produce the guardrail, see the GitHub repo.

The general circulation involves the following actions: First, the system gets 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 inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is stepped in 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 sections show inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. At the time of composing this post, you can utilize 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 provider and select the DeepSeek-R1 model.

The design detail page offers vital details about the design's capabilities, prices structure, and execution standards. You can discover detailed usage directions, including sample API calls and code bits for combination. The design supports different text generation jobs, consisting of content development, code generation, and concern answering, using its support learning optimization and CoT reasoning capabilities. The page also consists of release choices and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, select Deploy.

You will be prompted to set up the deployment 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, enter a variety of instances (in between 1-100). 6. For example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive interface where you can try out various prompts and change design parameters like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, material for reasoning.

This is an excellent method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The playground provides immediate feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.

You can rapidly check the design in the play area through the UI. However, to invoke the deployed model 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 perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a demand to produce text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

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

Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the technique that best suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release 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, select JumpStart in the navigation pane.

The model web 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 essential details, including:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the model card to view the model details page.
  
  The model details page consists of 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 includes important details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you release the model, it's advised to examine the design details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the automatically created name or produce a customized one.

8. For ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting proper circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation 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 configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the design.
    
    The implementation procedure can take a number of minutes to complete.
    
    When deployment is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can conjure up the design using a SageMaker runtime customer and integrate 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 require to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing 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 utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
12. In the Managed deployments section, find 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 deleting the right implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to erase 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe 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 innovative services utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning performance of big language models. In his leisure time, Vivek delights in treking, enjoying movies, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 an Expert Solutions Architect working on 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 center. She is passionate about constructing options that assist consumers accelerate their AI journey and unlock company worth.