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

Arden Brubaker edited this page 2 months ago

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 design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly 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 steps to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support finding out to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its reinforcement learning (RL) action, which was used to fine-tune the model's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's equipped to break down complex inquiries and factor through them in a detailed manner. This directed thinking procedure allows the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, logical reasoning and data analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient inference by routing questions to the most pertinent professional "clusters." This method permits the model to focus on different problem domains while maintaining total efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model 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, more efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate 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 limitation boost, produce a limit increase demand and reach out to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful content, and examine models against crucial safety criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine 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 produce the guardrail, see the GitHub repo.

The general flow includes 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 to the model for reasoning. After receiving the model's output, another guardrail check is used. 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 showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show reasoning using 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 steps:

1. On the Amazon Bedrock console, wiki.whenparked.com pick Model catalog under Foundation designs in the navigation pane. At the time of writing this post, you can utilize 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 service provider and select the DeepSeek-R1 model.

The design detail page offers necessary details about the model's capabilities, prices structure, and execution guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, including material creation, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning abilities. The page also includes implementation alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be triggered to set up the release 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 Variety of circumstances, enter a variety of circumstances (between 1-100). 6. For Instance type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in playground to access an interactive interface where you can try out different triggers and change model parameters like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, content for inference.

This is an exceptional method to explore the design's thinking and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, assisting you understand how the design responds to different inputs and letting you tweak your prompts for optimum outcomes.

You can rapidly check the design in the playground through the UI. However, to conjure up the deployed 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 perform reasoning utilizing 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, links.gtanet.com.br see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a demand to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the approach that best fits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 using 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, select JumpStart in the navigation pane.

The design web browser shows available designs, with details like the provider name and design abilities.

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), showing that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
  
  5. Choose the design card to view the model details page.
  
  The design details page consists of the following details:
  
  - The design name and company details. Deploy button to deploy 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 guidelines
  
  Before you release the model, it's recommended to evaluate the model details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, utilize the instantly created name or develop a custom-made one.

8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of circumstances (default: 1). Selecting proper circumstances types and counts is vital for cost and efficiency 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 setups for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.
    
    The deployment procedure can take a number of minutes to complete.
    
    When implementation is total, your endpoint status will change to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show relevant 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 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to release 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 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 using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the steps in this section to tidy 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, select Marketplace deployments.
12. In the Managed implementations area, locate the endpoint you desire 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 costs 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 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 models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business construct innovative solutions using and accelerated compute. Currently, he is concentrated on developing methods for trademarketclassifieds.com fine-tuning and enhancing the reasoning performance of big language models. In his spare time, Vivek delights in hiking, watching motion pictures, and trying 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 Science and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science group 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 constructing solutions that assist consumers accelerate their AI journey and unlock organization worth.