Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen models 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 variations varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) action, which was utilized to refine the model's responses beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down complex inquiries and reason through them in a detailed manner. This directed reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, logical thinking and data interpretation jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, allowing effective inference by routing queries to the most relevant professional "clusters." This approach permits the model to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs 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 release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and wiki.snooze-hotelsoftware.de 70B). Distillation describes a procedure of training smaller, more effective models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and evaluate designs against key security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and kousokuwiki.org standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 increase, develop a limitation increase demand 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) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging material, and evaluate designs against key safety requirements. You can execute safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic circulation 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 model for inference. 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened 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 offers 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 catalog under Foundation designs in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.

The design detail page supplies important details about the model's capabilities, rates structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration. The model supports various text generation jobs, consisting of material production, code generation, and pediascape.science concern answering, utilizing its support finding out optimization and CoT thinking abilities. The page also includes release alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, enter a variety of instances (between 1-100). 6. For Instance type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin using the model.

When the release is total, you can check DeepSeek-R1's abilities 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 design criteria like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.

This is an exceptional way to check out the design's reasoning and text generation capabilities before integrating it into your . The playground provides instant feedback, assisting you understand how the model responds to various inputs and letting you tweak your prompts for optimal outcomes.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a request to generate text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

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

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the method that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:

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

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

4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. Each model card reveals key details, consisting of:

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the model 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 crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the design, it's recommended to review the design details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, utilize the automatically generated name or wiki.myamens.com produce a customized one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of circumstances (default: 1). Selecting appropriate circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The deployment procedure can take a number of minutes to complete.
    
    When release is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and wiki.snooze-hotelsoftware.de make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run additional requests 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed 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 deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
12. In the Managed deployments area, 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 deleting the right deployment: 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 delete the endpoint if you desire 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 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 Inference at AWS. He assists emerging generative AI business build innovative options using AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his free time, Vivek enjoys treking, enjoying motion pictures, and attempting different cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology 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 tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that assist customers accelerate their AI journey and unlock organization value.