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

Adriene Pardo edited this page 1 month ago

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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support learning (RL) action, which was used to improve the model's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down intricate questions and reason through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, rational reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing queries to the most relevant expert "clusters." This approach permits the model to concentrate on various problem domains while maintaining total 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 deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate designs against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just 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 deploy the DeepSeek-R1 design, you require 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, surgiteams.com and confirm you're using 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, higgledy-piggledy.xyz create a limit increase request and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and examine models against key security criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic circulation involves the following actions: 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 out to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. 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 oeclub.org specialized structure 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 composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.

The design detail page offers vital details about the design's abilities, prices structure, and execution standards. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The design supports various text generation tasks, including content production, code generation, and question answering, using its support finding out optimization and CoT reasoning abilities. The page also includes deployment options and licensing details to help you get started with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be prompted to configure the deployment 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 instances, go into a variety of instances (between 1-100). 6. For example type, pick 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 personal cloud (VPC) networking, service function permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can explore various prompts and change model parameters like temperature level and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for inference.

This is an exceptional method to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground provides immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimal outcomes.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design 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 create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to create 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 release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you choose the method that finest fits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

The design browser displays available designs, with details like the company name and setiathome.berkeley.edu model abilities.

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

- Model name

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

8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of instances (default: 1). Selecting proper instance types and counts is essential for expense and efficiency optimization. Monitor your deployment 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 model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The release process can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run extra 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Tidy up
    
    To avoid unwanted charges, finish the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you the model using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
12. In the Managed releases section, find the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're erasing the appropriate release: 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 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies build ingenious solutions using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of large language models. In his spare time, Vivek enjoys treking, seeing movies, and attempting various foods.
    
    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 Science 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 product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing solutions that help customers accelerate their AI journey and unlock service worth.