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, along with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its reinforcement knowing (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and oeclub.org tweak procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down complex queries and factor through them in a detailed way. This directed reasoning process allows the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a model that can be integrated into numerous workflows such as representatives, rational thinking and data interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient reasoning by routing questions to the most appropriate expert "clusters." This approach enables the model to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to mimic 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 advise deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess models against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using 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 deploying. To ask for a limit boost, develop a limitation boost request and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and examine models against key safety criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 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 design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final outcome. 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 took place at the input or output stage. 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 structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. 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 offers essential details about the model's abilities, rates structure, and implementation standards. You can find detailed use directions, consisting of sample API calls and code snippets for combination. The design supports various text generation jobs, including material production, code generation, and question answering, using its reinforcement learning optimization and CoT thinking abilities. The page also consists of deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be triggered to configure the release 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 Number of instances, enter a variety of instances (between 1-100). 6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may desire to review these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust model criteria like temperature level 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 inference.

This is an exceptional method to check out the design's thinking and text generation abilities before integrating it into your applications. The playground supplies instant feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for optimum results.

You can quickly check the model in the play ground through the UI. However, to conjure up 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 shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a request to produce text based upon a user prompt.

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 just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the technique that best suits 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, choose Studio in the navigation pane. 2. First-time users will be prompted to create a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model internet browser displays available models, with details like the service provider name and design capabilities.

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, permitting 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 consists of the following details:
  
  - The design name and service provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage guidelines
  
  Before you deploy the model, it's recommended to review the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, utilize the instantly generated name or develop a custom one.

8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of circumstances (default: 1). Selecting suitable 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 inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The deployment procedure can take a number of minutes to complete.
    
    When deployment is total, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin 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 approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run additional demands 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, finish the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations.
12. In the Managed deployments section, locate the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed will sustain expenses 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 design 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 designs, 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 ingenious options using AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his spare time, Vivek delights in hiking, seeing films, and attempting 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 technology and Bioinformatics.
    
    Jonathan Evans is a Professional 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 hub. She is passionate about building options that help consumers accelerate their AI journey and unlock service worth.