GitLab

Today, we are excited to reveal 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 design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its support learning (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down intricate questions and reason through them in a detailed way. This guided thinking procedure enables the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as agents, logical thinking and data analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient inference by routing inquiries to the most pertinent professional "clusters." This method enables the design to focus on various problem domains while maintaining overall performance. DeepSeek-R1 requires 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 model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, 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 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 limitation increase, create a limit boost demand and connect to your account group.

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) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous material, and evaluate designs against crucial security requirements. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model responses released 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 circulation includes 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 design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. 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 took place at the input or output stage. The examples showcased in the following sections show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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, choose Model brochure under Foundation models in the navigation pane. At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.

The model detail page provides necessary details about the model's abilities, rates structure, and application guidelines. You can find detailed use directions, including sample API calls and code snippets for integration. The model supports various text generation tasks, consisting of material production, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning capabilities. The page likewise includes implementation choices and licensing details to help you get started with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, pick Deploy.

You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a number of instances (between 1-100). 6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may desire to examine these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and change design specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, material for reasoning.

This is an excellent way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.

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

Run reasoning using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a demand to create text based on 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 solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that best fits 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 triggered to develop a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

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

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. Each model card shows essential details, consisting of:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to see the model details page.
  
  The design details page includes the following details:
  
  - The design name and company details. Deploy button to release the design. About and systemcheck-wiki.de Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you deploy the design, it's suggested to examine the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, use the immediately generated name or produce a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of circumstances (default: 1). Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your deployment to change 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 accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
    
    The implementation process can take several minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing a SageMaker runtime client 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 need to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, finish the steps in this section 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 implementations.
12. In the Managed implementations area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to erase 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 design utilizing 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, SageMaker JumpStart pretrained designs, 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 services using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning efficiency of large language models. In his spare time, Vivek delights in hiking, enjoying films, and attempting various cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building solutions that help consumers accelerate their AI journey and unlock business value.