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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release 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 discovering to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its support learning (RL) step, which was utilized to refine the model's reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down complicated inquiries and factor through them in a detailed way. This guided thinking procedure enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, logical reasoning and information analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing inquiries to the most relevant specialist "clusters." This technique enables the model to specialize in various problem domains while maintaining overall performance. 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 instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more effective architectures based on popular open 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 mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate models against crucial security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect 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 use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit boost, create a limit boost demand and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and examine models against crucial safety criteria. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The basic circulation involves the following actions: First, the system receives an input for the design. 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 getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, select Model catalog 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 model.

The model detail page supplies necessary details about the model's abilities, prices structure, and execution guidelines. You can find detailed use directions, including sample API calls and code snippets for integration. The design supports various text generation jobs, including material production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. The page also includes release options and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a number of circumstances (between 1-100). 6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may want to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the design.

When the deployment 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 triggers and adjust model parameters like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, material for reasoning.

This is an exceptional method to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.

You can rapidly test the model in the playground 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 utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends out a demand to generate 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 solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the approach that finest matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to view the design details page.
  
  The design details page consists of the following details:
  
  - The model name and service provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab includes essential details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the design, it's recommended to examine the design details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, utilize the immediately produced name or produce a custom one.

8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of circumstances (default: 1). Selecting proper instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust 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 configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
    
    The implementation procedure can take a number of minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS approvals and pipewiki.org environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying 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 reasoning 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 using the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Clean up
    
    To prevent unwanted charges, complete 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 models in the navigation pane, choose Marketplace deployments.
12. In the Managed implementations area, locate the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, choose 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 model you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want 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 model 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 designs, wavedream.wiki SageMaker JumpStart pretrained models, 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 services utilizing AWS services and sped up compute. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning efficiency of big language designs. In his spare time, Vivek takes pleasure in hiking, enjoying movies, 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 Specialist 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 center. She is enthusiastic about constructing services that assist consumers accelerate their AI journey and unlock organization worth.