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Today, we are thrilled 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly 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 designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support knowing (RL) action, which was used to refine the model's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's geared up to break down complex queries and factor through them in a detailed manner. This assisted reasoning process permits the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and information analysis tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient reasoning by routing questions to the most relevant professional "clusters." This method permits the design to specialize in various problem domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 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 process of training smaller, more effective designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use 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 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing 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 releasing. To ask for a limit increase, develop a limitation increase demand and connect to your account team.

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 use Amazon Bedrock Guardrails. For instructions, see Set up consents to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, prevent damaging content, and evaluate models against essential safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic circulation includes the following actions: First, the system receives 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 design's output, another 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 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 using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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 brochure under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not 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 supplies necessary details about the design's capabilities, prices structure, and execution guidelines. You can find detailed use guidelines, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of material development, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning capabilities. The page also includes deployment alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be prompted to configure the release 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 number of instances (between 1-100). 6. For example type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might wish to evaluate 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 evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and adjust design specifications like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for inference.

This is an exceptional method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal results.

You can quickly test the model in the play area 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 carry out reasoning using a deployed DeepSeek-R1 design 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 produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a request to create text based on 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 options 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 release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the approach that best suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions 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 displays available designs, with details like the supplier name and design capabilities.

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if suitable), engel-und-waisen.de showing that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the model card to see the model details page.
  
  The design details page includes the following details:
  
  - The design name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab includes important details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage standards
  
  Before you deploy the design, it's recommended to review the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, utilize the automatically produced name or produce a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate instance types and counts is essential for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning 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 adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.
    
    The implementation procedure can take several minutes to complete.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run extra demands 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To prevent unwanted charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
12. In the Managed implementations area, find the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the proper 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 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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 develop innovative solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning efficiency of big language models. In his downtime, Vivek delights in hiking, enjoying movies, and 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 technology and Bioinformatics.
    
    Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building solutions that assist customers accelerate their AI journey and unlock organization value.