Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen designs 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, together with the distilled variations ranging from 1.5 to 70 billion parameters 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 comparable actions to deploy the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement finding out to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's responses beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's geared up to break down intricate questions and factor through them in a detailed way. This assisted reasoning process enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be incorporated into different workflows such as agents, logical reasoning and information analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for efficient inference by routing queries to the most relevant expert "clusters." This method allows the model to focus on various problem domains while maintaining general effectiveness. 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 circumstances to release the model. ml.p5e.48 xlarge includes 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 effective architectures based on popular open models 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 simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher design.

You can release 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, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and evaluate models against key security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing 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 deploying. To request a limitation increase, develop a limit boost request and connect to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous material, and assess models against key security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design actions 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 develop the guardrail, see the GitHub repo.

The general 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 to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final 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 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 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 use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.

The design detail page offers vital details about the design's abilities, rates structure, trademarketclassifieds.com and execution guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code bits for integration. The design supports various text generation jobs, consisting of material development, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities. The page also includes release choices and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be triggered to set up the release details for DeepSeek-R1. The design 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, go into a variety of circumstances (between 1-100). 6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive user interface where you can try out various triggers and adjust model criteria like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for reasoning.

This is an excellent way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play ground provides instant feedback, helping you understand how the design reacts to numerous inputs and letting you tweak your prompts for ideal results.

You can quickly evaluate the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a request to produce 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 models 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 hassle-free approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the approach that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

The design web browser shows available models, with details like the service provider name and design capabilities.

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 instance, Text Generation). Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the design details page.
  
  The design details page includes the following details:
  
  - The model 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 specs.
• Usage standards
  
  Before you release the model, it's suggested to evaluate the design 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 created name or create a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of circumstances (default: 1). Selecting proper circumstances types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.
    
    The deployment procedure can take numerous minutes to complete.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS permissions 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 deploying the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use 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 unwanted charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
12. In the Managed deployments area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the appropriate release: 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 delete 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 release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop ingenious services using AWS services and accelerated compute. Currently, pipewiki.org he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language designs. In his downtime, Vivek enjoys hiking, viewing films, and trying various 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 Science and kousokuwiki.org Bioinformatics.
    
    Jonathan Evans is a Specialist 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 hub. She is passionate about building solutions that assist consumers accelerate their AI journey and unlock service value.