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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion to construct, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses support discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its reinforcement knowing (RL) action, which was used to refine the model's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down complex questions and reason through them in a detailed way. This guided thinking process enables the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, logical reasoning and data interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, allowing effective inference by routing questions to the most appropriate expert "clusters." This method permits the design to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more efficient architectures based upon 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, more effective models to simulate the behavior and reasoning 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 suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and examine designs against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 increase, develop a limitation boost request and reach out to your account group.

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) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and assess designs against key security requirements. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design responses 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 produce the guardrail, see the GitHub repo.

The basic circulation includes the following steps: First, the system receives 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 design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show reasoning utilizing 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, total the following actions:

1. On the Amazon Bedrock console, choose 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 model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for it-viking.ch DeepSeek as a company and choose the DeepSeek-R1 model.

The model detail page offers essential details about the model's abilities, rates structure, and application standards. You can find detailed use directions, including sample API calls and code bits for combination. The model supports various text generation jobs, consisting of material creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking abilities. The page likewise consists of implementation choices 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 prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, get in a number of instances (between 1-100). 6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive user interface where you can explore various prompts and change model criteria like temperature level and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, content for reasoning.

This is an excellent way to explore the model's thinking and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, assisting you comprehend how the model reacts to numerous inputs and letting you tweak your prompts for optimum outcomes.

You can rapidly test the design in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using 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 actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a request to produce text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest fits your requirements.

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 prompted to produce a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model web browser displays available designs, with details like the service provider name and model capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card shows crucial details, including:

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to see the design details page.
  
  The model details page includes the following details:
  
  - The model name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage guidelines
  
  Before you release the design, it's recommended to evaluate the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the automatically created name or develop a custom-made one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of circumstances (default: 1). Selecting proper instance types and counts is crucial for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.
    
    The deployment process can take a number of minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and range 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 utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
12. In the Managed releases section, locate the endpoint you want 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 proper release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design 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 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 Getting going 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 ingenious solutions utilizing AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek delights in 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 area 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 working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing options that help consumers accelerate their AI journey and unlock company value.