GitLab

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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to begin 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 large language design (LLM) developed by DeepSeek AI that uses reinforcement finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its support knowing (RL) action, which was used to fine-tune the model's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's equipped to break down complicated queries and factor through them in a detailed way. This assisted thinking process permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most relevant professional "clusters." This technique permits the model to specialize in various issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities 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 refers to a procedure of training smaller sized, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as a teacher model.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate models against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 releasing. To request a limitation increase, create a limit boost demand wavedream.wiki and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and examine designs against crucial . You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design actions released 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 produce the guardrail, see the GitHub repo.

The basic flow includes the following steps: 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 out to the model for reasoning. After receiving the design'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 suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show inference 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, complete the following actions:

1. On the Amazon Bedrock console, select Model brochure 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 does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.

The design detail page offers essential details about the model's capabilities, rates structure, and execution standards. You can discover detailed usage directions, including sample API calls and code snippets for combination. The model supports various text generation tasks, including content creation, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning capabilities. The page also includes implementation options and licensing details to assist you get begun with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, enter a variety of instances (in between 1-100). 6. For Instance type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to begin using the model.

When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in playground to access an interactive user interface where you can try out various prompts and adjust design criteria like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for reasoning.

This is an exceptional way to check out the model's reasoning and text generation abilities before integrating it into your applications. The playground offers instant feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your prompts for ideal results.

You can rapidly check the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a request to generate text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options 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 deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the method that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, choose Studio in the navigation pane. 2. First-time users will be triggered to develop 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 supplier name and design capabilities.

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if suitable), showing that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the design details page.
  
  The model details page includes the following details:
  
  - The model name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you release the model, it's advised to evaluate the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the automatically produced name or create a customized one.

8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: wiki.myamens.com 1). Selecting appropriate instance types and counts is important for cost and performance 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 configurations for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.
    
    The release procedure can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin 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 demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use 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 displayed in the following code:
    
    Tidy up
    
    To prevent unwanted charges, complete the actions 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 steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
12. In the Managed deployments section, locate the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the proper implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase 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 going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build innovative options using AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the inference performance of large language designs. In his spare time, Vivek delights in hiking, enjoying movies, 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 working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building options that help customers accelerate their AI journey and unlock company worth.