GitLab

Today, we are thrilled 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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to construct, 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 similar steps to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its support learning (RL) action, which was utilized to fine-tune the model's actions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's geared up to break down intricate questions and reason through them in a detailed manner. This guided thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, rational thinking and data analysis tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing effective reasoning by routing questions to the most appropriate professional "clusters." This technique enables the model to concentrate on various problem domains while maintaining general effectiveness. 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 release the model. ml.p5e.48 xlarge comes with 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 effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and assess models against essential safety 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 develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, 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 confirm you're using 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 ask for a limitation increase, create a limit boost request and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and assess models against key safety requirements. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design reactions deployed 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 create the guardrail, see the GitHub repo.

The general flow 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 model for inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened 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 demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 doesn't support Converse APIs and other tooling. 2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.

The design detail page supplies important details about the design's capabilities, pricing structure, and application guidelines. You can discover detailed use instructions, including sample API calls and code bits for integration. The design supports numerous text generation jobs, including content development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. The page likewise includes release options and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of circumstances, enter a variety of circumstances (between 1-100). 6. For Instance type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the model.

When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. 8. Choose Open in playground to access an interactive user interface where you can try out different triggers and adjust design criteria like temperature level and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, content for reasoning.

This is an outstanding method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area offers immediate feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your triggers for ideal outcomes.

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

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to produce text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services 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 data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach 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, select Studio in the navigation pane. 2. First-time users will be triggered to produce a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
  
  5. Choose the model card to see the model details page.
  
  The design details page includes the following details:
  
  - The design name and service provider details. Deploy button to deploy the model. 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 design, it's recommended to examine the model details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, utilize the instantly created name or produce a custom-made one.

8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting proper circumstances types and counts is crucial for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the model.
    
    The deployment process can take numerous minutes to complete.
    
    When implementation is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime customer and integrate 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 set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, higgledy-piggledy.xyz select Marketplace implementations.
12. In the Managed implementations section, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 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 develop ingenious services using AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his totally free time, Vivek enjoys hiking, viewing movies, and attempting different cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that help consumers accelerate their AI journey and unlock service worth.