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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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to begin 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) established by DeepSeek AI that uses reinforcement discovering to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its support knowing (RL) step, which was utilized to fine-tune the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complex questions and factor 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 generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, logical thinking and information interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient inference by routing queries to the most appropriate professional "clusters." This method enables the model to focus on various problem domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 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 upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using 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 recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and assess designs against essential security requirements. 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 create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 instance in the AWS Region you are releasing. To request a limit boost, produce 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) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and assess designs against essential security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model 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 create the guardrail, see the GitHub repo.

The general flow includes the following actions: 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 to the design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. 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 steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.

The design detail page provides vital details about the design's abilities, rates structure, and application standards. You can discover detailed usage guidelines, including sample API calls and pipewiki.org code bits for combination. The design supports numerous text generation tasks, consisting of material production, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. The page also consists of release choices and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To start using 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, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of instances, enter a number of circumstances (in between 1-100). 6. For Instance 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 innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may desire to review these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the model.

When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in playground to access an interactive user interface where you can experiment with different prompts and change design parameters like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.

This is an exceptional way to explore the model's reasoning and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you understand how the design reacts to various inputs and letting you fine-tune your triggers for optimal results.

You can quickly test the model in the playground through the UI. However, to invoke the released model 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 shows how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a request to generate text based on a user prompt.

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 few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical approaches: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the approach that finest suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, select Studio in the navigation pane. 2. First-time users will be triggered to produce a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The design web browser shows available models, with details like the company name and design abilities.

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to see the model details page.
  
  The model details page consists of the following details:
  
  - The model name and service provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage guidelines
  
  Before you release the design, it's suggested to review the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, utilize the instantly generated name or produce a custom-made one.

8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of instances (default: 1). Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The release procedure can take several minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use 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 displayed in the following code:
    
    Clean up
    
    To avoid unwanted charges, complete the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
12. In the Managed deployments section, locate the endpoint you desire 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 appropriate deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe 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 assists emerging generative AI business build ingenious services using AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his downtime, Vivek delights in hiking, seeing 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 technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building options that help clients accelerate their AI journey and unlock business value.