Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and properly 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 comparable steps to release the distilled versions of the models too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its support learning (RL) action, which was utilized to improve the model's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated questions and factor through them in a detailed way. This assisted reasoning process permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as agents, rational reasoning and information 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 criteria, allowing effective reasoning by routing inquiries to the most pertinent specialist "clusters." This method permits the model to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 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 effective 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, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess models against key security requirements. 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 develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, 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, choose 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 increase, create a limit increase request and connect to your account group.

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) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and assess designs against crucial security criteria. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general 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 model's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 actions:

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 model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.

The design detail page offers essential details about the model's abilities, pricing structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports numerous text generation jobs, consisting of material development, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities. The page also includes deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, get in a variety of instances (in between 1-100). 6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you might wish to examine 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 check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust design criteria like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.

This is an exceptional method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the design responds to various inputs and letting you tweak your triggers for optimum outcomes.

You can quickly check the model in the playground 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 using guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a request to generate text based upon 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 options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the model card to view the model details page.
  
  The model details page includes 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 specs.
• Usage guidelines
  
  Before you release the model, it's recommended to examine the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, use the automatically produced name or produce a custom-made one.

8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of instances (default: 1). Selecting proper circumstances types and counts is vital for expense and efficiency optimization. Monitor your release to adjust 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 accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The implementation procedure can take numerous minutes to complete.
    
    When implementation is total, your endpoint status will change to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can up the design utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 utilizing 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 demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and range 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Clean up
    
    To avoid unwanted charges, finish the actions in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
12. In the Managed releases area, locate the endpoint you desire 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 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 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 get begun. 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 Getting begun with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for systemcheck-wiki.de Inference at AWS. He helps emerging generative AI business develop innovative services using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his spare time, Vivek takes pleasure in treking, enjoying movies, archmageriseswiki.com and trying 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 technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing solutions that assist consumers accelerate their AI journey and unlock service worth.