18 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Adeline Redman edited this page 1 month ago

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 model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses reinforcement discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying function is its support knowing (RL) step, which was utilized to fine-tune the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex questions and factor through them in a detailed manner. This directed reasoning procedure allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, sensible reasoning and data interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing queries to the most pertinent expert "clusters." This approach permits the model to focus on various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective 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, more efficient designs to imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as a teacher design.

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 utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate designs against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy 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, select Amazon SageMaker, and validate 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 deploying. To request a limitation increase, create a limitation increase request and connect to your account group.

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 guidelines, see Establish consents to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and examine models against key security criteria. You can execute safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The general flow includes 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 to the model for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last outcome. 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 stage. The examples showcased in the following areas 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.

The model detail page supplies essential details about the design's abilities, prices structure, and application guidelines. You can discover detailed use directions, including sample API calls and code snippets for combination. The model supports different text generation jobs, consisting of content creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking capabilities. The page likewise consists of deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a number of circumstances (in between 1-100). 6. For example type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may want to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive user interface where you can experiment with various triggers and change design criteria like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for reasoning.

This is an exceptional way to check out the model's thinking and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for ideal results.

You can rapidly test the design in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning using guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a request to produce text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in 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 usage case, with your information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the approach that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if relevant), suggesting that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to see the design details page.
  
  The model details page includes the following details:
  
  - The design name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications. - Usage guidelines
  
  Before you release the model, it's recommended to review the design details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the immediately created name or produce a customized one.

8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of circumstances (default: 1). Selecting proper instance types and counts is essential 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 setups for accuracy. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.
    
    The release process can take numerous minutes to finish.
    
    When release is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands 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 implementation is total, you can invoke the design using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference 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 requests against the predictor:
    
    Implement guardrails and run reasoning 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 implement it as displayed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, complete the actions in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
12. In the Managed deployments area, find the 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 erasing 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 Inference at AWS. He assists emerging generative AI companies construct innovative solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning efficiency of large language models. In his totally free time, Vivek enjoys hiking, enjoying films, and it-viking.ch trying different cuisines.
    
    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 Science and Bioinformatics.
    
    Jonathan Evans is an Expert 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 passionate about building solutions that assist customers accelerate their AI journey and unlock organization value.