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Today, we are thrilled 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 release 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 responsibly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement finding out to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its support learning (RL) step, which was used to fine-tune the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate questions and reason through them in a detailed manner. This directed thinking process allows the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible thinking and data interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, enabling effective inference by routing queries to the most pertinent expert "clusters." This method enables the design to focus on different problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and examine models against key security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate 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 deploying. To request a limit increase, produce a limit boost request and reach out to your account group.

Because you will be releasing 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 authorizations to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and assess designs against essential safety requirements. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The basic flow 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 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. At the time of writing this post, you can use the InvokeModel API to conjure up 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 design detail page offers important details about the model's abilities, rates structure, and implementation standards. You can discover detailed use instructions, including sample API calls and code snippets for integration. The model supports various text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its optimization and CoT reasoning abilities. The page also includes implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be triggered 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 (in between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a number of circumstances (in between 1-100). 6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the design.

When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive interface where you can try out various triggers and change design parameters like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for inference.

This is an exceptional method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies instant feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your triggers for optimum results.

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

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 design 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 create 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 criteria, and sends out 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, built-in algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the technique that finest matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, select 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 provider name and model capabilities.

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to see the design details page.
  
  The model details page includes the following details:
  
  - The design name and supplier details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage guidelines
  
  Before you deploy the model, it's suggested to review the design details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, utilize the immediately generated name or develop a custom one.

8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1). Selecting proper instance types and counts is vital for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.
    
    The release procedure can take numerous minutes to complete.
    
    When release is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run extra demands 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
12. In the Managed implementations section, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the proper implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed 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 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 models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 construct ingenious options utilizing AWS services and sped up calculate. Currently, larsaluarna.se he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his leisure time, Vivek delights in treking, seeing motion pictures, and trying different cuisines.
    
    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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about building options that help customers accelerate their AI journey and unlock service value.