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 model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI concepts on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement learning (RL) step, which was used to fine-tune the design’s responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it’s geared up to break down intricate questions and reason through them in a detailed manner. This directed thinking procedure permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the industry’s attention as a flexible text-generation model that can be integrated into different workflows such as representatives, rational reasoning and data interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by routing queries to the most pertinent professional “clusters.” This technique allows the model to specialize in various problem domains while maintaining total effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and examine designs against essential security requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you’re using 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 boost, create a limit boost demand and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging content, and examine designs against crucial safety requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions released 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 create the guardrail, see the GitHub repo.

The general circulation involves the following steps: 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 reasoning. After getting the design’s output, another guardrail check is used. If the output passes this final check, it’s returned as the outcome. 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 took place 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 offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:

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 use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.
   
   The model detail page supplies vital details about the model’s capabilities, rates structure, and implementation guidelines. You can discover detailed usage instructions, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, consisting of content creation, hb9lc.org code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page also includes implementation alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications.
2. To begin utilizing DeepSeek-R1, choose Deploy.
   
   You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
4. For Variety of circumstances, go into a number of instances (between 1-100).
5. For Instance type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role consents, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to line up with your company’s security and compliance requirements.
6. Choose Deploy to start utilizing the model.
   
   When the implementation is total, you can evaluate DeepSeek-R1’s capabilities straight in the Amazon Bedrock play area.
7. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust design specifications 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, content for reasoning.
   
   This is an exceptional method to check out the design’s reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimal results.
   
   You can quickly test the model in the playground through the UI. However, to conjure up the deployed model 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 shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out 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 options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s explore both methods to help you choose the method that finest matches your needs.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
   
   1. On the SageMaker console, it-viking.ch pick Studio in the navigation pane.
8. First-time users will be triggered to produce a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The design internet browser shows available models, with details like the company name and model capabilities.
   
   4. Look for larsaluarna.se DeepSeek-R1 to see the DeepSeek-R1 model card. Each model card reveals essential details, consisting of:
   
   - Model name
10. Provider name
11. Task classification (for instance, Text Generation). Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
    
    5. Choose the model card to see the model details page.
    
    The model details page includes the following details:
    
    - The design name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
    
    The About tab consists of essential details, such as:
    
    - Model description.
12. License details.
13. Technical requirements.
14. Usage standards
    
    Before you deploy the design, it’s suggested to evaluate the design details and license terms to verify compatibility with your usage case.
    
    6. Choose Deploy to continue with deployment.
    
    7. For Endpoint name, use the immediately created name or develop a custom one.
15. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
16. For Initial circumstances count, wiki-tb-service.com get in the variety of circumstances (default: 1). Selecting appropriate instance types and counts is crucial for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
17. Review all setups for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
18. Choose Deploy to release the model.
    
    The release procedure can take a number of minutes to finish.
    
    When deployment 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 monitor the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin 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 authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference 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 extra demands against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize 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 displayed in the following code:
    
    Clean 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, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
19. In the Managed releases area, find the endpoint you want to erase.
20. Select the endpoint, and on the Actions menu, pick Delete.
21. Verify the endpoint details to make certain you’re erasing the correct implementation: 1. Endpoint name.
22. Model name.
23. 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 want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored 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 begin. For more details, it-viking.ch describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 helps emerging generative AI companies develop ingenious services using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference performance of big language models. In his spare time, Vivek enjoys treking, watching motion pictures, and trying 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 technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic 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.