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Today, we are delighted 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 develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses reinforcement learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating function is its support learning (RL) action, which was utilized to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated questions and factor through them in a detailed manner. This assisted thinking process permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most relevant professional "clusters." This approach permits the model to concentrate on various issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances 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 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 mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine designs against crucial safety criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, 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, pick Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation boost, create a limit boost demand and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and assess models against crucial security criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The general flow involves the following actions: First, the system gets 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 receiving the design's output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it occurred 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 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, 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 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 supplier and select the DeepSeek-R1 model.

The model detail page supplies important details about the design's capabilities, rates structure, and application guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of material development, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page likewise consists of release alternatives and licensing details to help you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose Deploy.

You will be triggered to configure the implementation 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 instances, go into a number of instances (between 1-100). 6. For example type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative 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 releases, you may wish to evaluate these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the implementation is total, you can check 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 adjust model parameters like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for reasoning.

This is an excellent way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for ideal results.

You can quickly evaluate the design 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 utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning using a deployed 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to produce 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 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 utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the that best suits your needs.

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 develop a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
  
  5. Choose the model card to see the design details page.
  
  The model 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 includes important details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you deploy the model, it's advised to evaluate the design details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, utilize the immediately created name or produce a custom-made one.

8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting appropriate instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.
    
    The release procedure can take numerous minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To start 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 approvals 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 deploying the model is supplied in the Github here. You can clone the notebook and run 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, complete the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the design using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
12. In the Managed deployments area, find the endpoint you want 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 right 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 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, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 disgaeawiki.info Inference at AWS. He assists emerging generative AI companies build innovative services using AWS services and sped up compute. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of big language designs. In his totally free time, Vivek delights in hiking, watching movies, and attempting various foods.
    
    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 dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that assist clients accelerate their AI journey and unlock organization worth.