Today, we are delighted 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, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) step, which was used to fine-tune the design’s reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it’s geared up to break down complex inquiries and reason through them in a detailed way. This directed reasoning process allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually captured the industry’s attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, sensible reasoning and data analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, allowing effective inference by routing queries to the most appropriate professional “clusters.” This technique allows the model to specialize in different problem domains while maintaining total efficiency. 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 release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and archmageriseswiki.com 70B). Distillation refers to a process of training smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess designs against essential security 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 develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 releasing. To ask for a limitation boost, produce a limitation increase demand and reach out to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging content, and assess designs against key safety requirements. You can carry out safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions 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 general flow 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 out to the model for inference. After receiving the design’s output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate inference using 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, choose Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other tooling.

1. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.
   
   The model detail page supplies vital details about the model’s abilities, rates structure, and execution guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports different text generation tasks, consisting of content creation, code generation, and concern answering, using its support finding out optimization and CoT thinking capabilities. The page also includes release alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications.
2. To start utilizing DeepSeek-R1, choose Deploy.
   
   You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
4. For Variety of circumstances, enter a number of instances (between 1-100).
5. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might desire to review these settings to align with your company’s security and compliance requirements.
6. Choose Deploy to start using the design.
   
   When the release is complete, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
7. Choose Open in play area to access an interactive user interface where you can try out various triggers and adjust model criteria like temperature level 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 inference.
   
   This is an excellent method to explore the model’s reasoning and text generation abilities before integrating it into your applications. The playground provides instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for optimum outcomes.
   
   You can quickly test the design in the playground through the UI. However, to invoke the deployed 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 shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using 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, configures reasoning criteria, and sends 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 solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both methods to help you choose the technique 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, pick Studio in the navigation pane.
8. First-time users will be triggered to produce a domain.
9. On the SageMaker Studio console, choose JumpStart in the navigation pane.
   
   The design web browser displays available designs, with details like the company name and model abilities.
   
   4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card reveals key details, wiki.snooze-hotelsoftware.de consisting of:
   
   - Model name
10. Provider name
11. Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), suggesting 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 model name and service provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
    
    The About tab includes essential details, such as:
    
    - Model description.
12. License details.
13. Technical specs.
14. Usage standards
    
    Before you deploy the design, it’s recommended to review the model details and license terms to validate compatibility with your use case.
    
    6. Choose Deploy to continue with deployment.
    
    7. For Endpoint name, utilize the automatically produced name or produce a custom-made one.
15. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial instance count, get in the number of instances (default: 1). Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your release 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.
17. Review all configurations for ratemywifey.com accuracy. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
18. Choose Deploy to release the model.
    
    The deployment process can take a number of minutes to finish.
    
    When deployment is total, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
19. In the Managed implementations area, find the endpoint you want to delete.
20. Select the endpoint, and on the Actions menu, choose Delete.
21. Verify the endpoint details to make certain you’re deleting the right release: 1. Endpoint name.
22. Model name.
23. 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 delete 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 begun with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business construct ingenious solutions using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his free time, Vivek takes pleasure in treking, viewing motion pictures, and attempting various 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 Science and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about developing options that help consumers accelerate their AI journey and unlock company worth.