Today, we are thrilled to announce 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 design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly 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 actions to deploy the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its support learning (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complex questions and reason through them in a detailed manner. This guided thinking process permits the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a versatile text-generation design that can be incorporated into different workflows such as agents, logical thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, making it possible for effective reasoning by routing questions to the most relevant professional “clusters.” This technique permits the model to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires 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 features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check 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 circumstances in the AWS Region you are deploying. To ask for a limit increase, develop a limit boost request and connect to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and assess models against key safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate 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 create the guardrail, see the GitHub repo.

The general flow 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 model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or 89u89.com output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections 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, total the following actions:

1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. At the time of composing 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 DeepSeek as a provider and pick the DeepSeek-R1 model.

The design detail page provides vital details about the model's abilities, rates structure, and implementation guidelines. You can find detailed use guidelines, consisting of calls and code bits for combination. The model supports different text generation jobs, consisting of content creation, code generation, and question answering, using its support learning optimization and CoT thinking abilities. The page likewise includes implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, pick Deploy.

You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a number of circumstances (in between 1-100). 6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For a lot 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 design.

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

This is an excellent method to check out the design's thinking and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak your prompts for ideal outcomes.

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

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a demand to create text based upon 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 just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the technique that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:

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

The design internet browser displays available models, with details like the supplier name and design capabilities.

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to view the model details page.
  
  The model details page consists of the following details:
  
  - The design name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you deploy the design, it's recommended to examine the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the immediately generated name or create a customized one.

1. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
2. For Initial instance count, go into the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
3. Review all setups for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
4. Choose Deploy to release the model.
   
   The release process can take several minutes to finish.
   
   When release is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
   
   Deploy DeepSeek-R1 using 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 essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
   
   You can run additional demands 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
   
   Tidy up
   
   To avoid undesirable charges, complete the actions in this area to tidy up your resources.
   
   Delete the Amazon Bedrock Marketplace release
   
   If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
   
   1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
5. In the Managed implementations section, find the endpoint you want to delete.
6. Select the endpoint, and on the Actions menu, pick Delete.
7. Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. Endpoint name.
8. Model name.
9. 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 wish 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 begin. For more details, refer to 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 assists emerging generative AI business build ingenious services using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his downtime, Vivek takes pleasure in treking, seeing motion pictures, and attempting different 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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
   
   Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing services that assist consumers accelerate their AI journey and unlock business value.