Today, we are thrilled 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, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative AI concepts 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 steps to deploy the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses support finding out to improve 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 improve the model’s reactions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it’s geared up to break down complicated questions and factor through them in a detailed way. This directed thinking process enables the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry’s attention as a text-generation model that can be integrated into numerous workflows such as representatives, sensible thinking and information analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by routing inquiries to the most relevant professional “clusters.” This technique allows the design to focus on various issue 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 utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against key security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and kigalilife.co.rw standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, 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 use. 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, develop a limitation boost demand and reach out to your account team.

Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Set up consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous content, and assess designs against essential security criteria. You can carry out safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate 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 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 applied. If the output passes this final check, it’s returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
   
   The model detail page supplies important details about the design’s abilities, rates structure, and application standards. You can discover detailed use directions, including sample API calls and code bits for integration. The design supports various text generation tasks, consisting of content production, code generation, and question answering, wavedream.wiki using its reinforcement discovering optimization and CoT thinking abilities. The page likewise includes deployment choices and licensing details to assist you start with DeepSeek-R1 in your applications.
2. To start utilizing DeepSeek-R1, pick Deploy.
   
   You will be triggered to set up the deployment details for wavedream.wiki DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
4. For Number of circumstances, get in a number of circumstances (in between 1-100).
5. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your company’s security and compliance requirements.
6. Choose Deploy to begin using the model.
   
   When the deployment is total, you can evaluate DeepSeek-R1’s abilities straight in the Amazon Bedrock play area.
7. Choose Open in play area to access an interactive interface where you can experiment with different triggers and change model criteria like temperature level and optimum length. When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for optimal results. For example, material for reasoning.
   
   This is an excellent way to explore the design’s thinking and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your prompts for optimal results.
   
   You can quickly check the model in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example demonstrates how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a demand to produce text based on a user prompt.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both techniques to help you pick the technique that best matches your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be prompted to produce a domain.
9. On the SageMaker Studio console, select JumpStart in the navigation pane.
   
   The model web browser displays available designs, with details like the company name and model capabilities.
   
   4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each model card reveals essential details, including:
   
   - Model name - Provider name
10. Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
    
    5. Choose the design card to view the model details page.
    
    The design details page consists of the following details:
    
    - The design name and provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
    
    The About tab includes important details, such as:
    
    - Model description.
11. License details.
12. Technical specs.
13. Usage guidelines
    
    Before you deploy the model, it’s suggested to evaluate the design details and license terms to validate compatibility with your usage case.
    
    6. Choose Deploy to proceed with deployment.
    
    7. For Endpoint name, utilize the instantly created name or develop a custom-made one.
14. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
15. For Initial circumstances count, get in the number of instances (default: 1). Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
16. Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
17. Choose Deploy to release the design.
    
    The release process can take a number of minutes to complete.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 range 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 also utilize 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 shown in the following code:
    
    Tidy up
    
    To avoid unwanted charges, finish the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments.
18. In the Managed releases area, find the endpoint you wish to erase.
19. Select the endpoint, and on the Actions menu, choose Delete.
20. Verify the endpoint details to make certain you’re erasing the correct release: 1. Endpoint name.
21. Model name.
22. 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 model using 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 assists emerging generative AI business develop innovative options using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference performance of big 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 item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about developing solutions that assist clients accelerate their AI journey and unlock company worth.