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Today, we are excited 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that utilizes support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement learning (RL) action, which was utilized to improve the design's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's equipped to break down intricate questions and reason through them in a detailed manner. This guided reasoning procedure allows the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually captured 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 allows activation of 37 billion specifications, allowing efficient inference by routing questions to the most relevant expert "clusters." This method enables the model to concentrate on different problem domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective 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, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine models against essential security criteria. At the time of composing 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 usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, 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 use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, create a limitation increase request and reach out to your account team.

Because you will be releasing this model 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 directions, see Set up permissions to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and assess designs against key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The basic flow involves the following steps: 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 getting 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 showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 steps:

1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.

The design detail page supplies essential details about the model's abilities, pricing structure, and application guidelines. You can discover detailed use instructions, consisting of sample API calls and code bits for combination. The model supports numerous text generation tasks, including material creation, code generation, and 89u89.com question answering, utilizing its support finding out optimization and CoT thinking capabilities. The page likewise includes release options and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, get in a number of circumstances (between 1-100). 6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may desire to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and adjust design specifications like temperature level and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.

This is an outstanding method to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the model reacts to different inputs and letting you tweak your triggers for optimal outcomes.

You can rapidly evaluate the design in the play area through the UI. However, to invoke the released 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 demonstrates how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends out a request to generate text based on 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 services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that best suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 using 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, choose JumpStart in the navigation pane.

The model 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 model card shows crucial details, consisting of:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if relevant), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the design details page.
  
  The design details page includes the following details:
  
  - The model name and provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab includes important details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you deploy the design, it's suggested to evaluate the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, utilize the instantly generated name or create a customized one.

8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of instances (default: 1). Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The implementation procedure can take numerous minutes to finish.
    
    When release is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and integrate 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 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 shows how to deploy and utilize DeepSeek-R1 for . The code for deploying the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
    
    Clean up
    
    To prevent undesirable charges, complete the actions in this section 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 models in the navigation pane, pick Marketplace implementations.
12. In the Managed implementations section, find the endpoint you desire to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the appropriate release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released 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 checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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 assists emerging generative AI companies construct ingenious solutions utilizing AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his totally free time, Vivek delights in hiking, viewing movies, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 an Expert Solutions Architect dealing with 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 hub. She is passionate about developing solutions that help clients accelerate their AI journey and unlock service worth.