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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release 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 support discovering to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement knowing (RL) action, which was utilized to refine the model's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's geared up to break down intricate inquiries and factor through them in a detailed manner. This guided reasoning procedure enables the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and data analysis jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient inference by routing queries to the most relevant specialist "clusters." This approach permits the design to specialize in different problem domains while maintaining overall effectiveness. 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 release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, higgledy-piggledy.xyz 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing 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 suggest deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate models against crucial security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety 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 confirm you're utilizing 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 releasing. To ask for a limit increase, create a limit boost request and connect to your account group.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and assess models against crucial security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions released 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 develop the guardrail, see the GitHub repo.

The basic circulation involves the following steps: 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 out to the model for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final result. 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 happened at the input or output phase. The examples showcased in the following areas demonstrate inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, pick Model brochure 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 supplier and select the DeepSeek-R1 model.

The model detail page supplies necessary details about the design's capabilities, prices structure, and implementation guidelines. You can discover detailed use guidelines, including sample API calls and code bits for combination. The model supports different text generation jobs, including content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. The page likewise consists of implementation options and licensing details to help you start with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, go into a variety of instances (between 1-100). 6. For example type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive interface where you can explore various prompts and change 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 instance, material for inference.

This is an excellent method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for optimum outcomes.

You can quickly test the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a request to produce 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 solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the method that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 using 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, pick JumpStart in the navigation pane.

The model web browser displays available designs, with details like the provider name and design abilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card shows essential details, consisting of:

- Model name

• Provider name
• Task classification (for example, 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 model
  
  5. Choose the model card to view the design details page.
  
  The design details page includes the following details:
  
  - The model name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you release the design, it's recommended to examine the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the automatically created name or develop a customized one.

8. For Instance type ¸ choose an instance 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 important for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the model.
    
    The deployment process can take several minutes to complete.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the design is ready to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using 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 essential AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and run 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce 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 steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released 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 implementations.
12. In the Managed implementations section, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the proper deployment: 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 . 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 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 begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies develop ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference performance of large language models. In his spare time, Vivek delights in treking, watching movies, and attempting different cuisines.
    
    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 Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing services that assist consumers accelerate their AI journey and unlock organization value.