Today, we are delighted 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 versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly 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 comparable steps to deploy the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses support finding out to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement learning (RL) step, which was used to refine the design's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's geared up to break down complicated queries and factor through them in a detailed way. This directed reasoning process allows the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, rational reasoning and data analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient inference by routing inquiries to the most professional "clusters." This technique enables the model to concentrate on different issue domains while maintaining overall 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 deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities 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 describes a procedure of training smaller sized, more efficient designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing 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 place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and evaluate models against crucial security requirements. At the time of writing this blog, 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 use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 limitation boost, develop a limit increase request 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 Establish consents to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging content, and evaluate designs against key security criteria. You can implement security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The basic flow involves the following actions: First, the system receives 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 model for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the 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 took place at the input or output phase. The examples showcased in the following areas show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.

The design detail page provides necessary details about the model's capabilities, pricing structure, and implementation guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of material development, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning capabilities. The page likewise consists of release options and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, select Deploy.

You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of circumstances, enter a variety of instances (in between 1-100). 6. For Instance type, select your circumstances type. For ideal efficiency with DeepSeek-R1, forum.batman.gainedge.org a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive interface where you can try out different prompts and adjust model criteria like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, content for inference.

This is an outstanding method to check out the design's thinking and text generation abilities before integrating it into your applications. The playground provides instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimal results.

You can rapidly check the design in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model 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 produce the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a request to create text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the approach that best suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the model card to view the model details page.
  
  The model details page consists of 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 consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage guidelines
  
  Before you deploy the model, it's recommended to examine the model details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, use the immediately created name or produce a custom-made one.

8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1). Selecting proper circumstances types and counts is important for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. 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 release the design.
    
    The deployment process can take numerous minutes to finish.
    
    When implementation is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up 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 using 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 inference programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, complete the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
12. In the Managed implementations section, locate the endpoint you wish 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 proper deployment: 1. Endpoint name.
15. Model name.
16. 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 want 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 model 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 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 construct ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of big language designs. In his spare time, Vivek delights in treking, watching movies, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science 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 partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing options that assist customers accelerate their AI journey and unlock company worth.