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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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its reinforcement learning (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more effectively to user and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated questions and factor through them in a detailed way. This assisted reasoning process allows the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, sensible reasoning and information interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, it-viking.ch enabling effective inference by routing inquiries to the most pertinent expert "clusters." This technique permits the model to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and archmageriseswiki.com apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect 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 releasing. To ask for a limit boost, develop a limitation boost demand and reach out to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, 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 harmful content, and evaluate models against key security requirements. You can implement safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design reactions 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 develop the guardrail, see the GitHub repo.

The general flow involves the following actions: First, the system gets 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 inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 steps:

1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.

The model detail page supplies vital details about the design's abilities, prices structure, and execution guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different text generation jobs, including material creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking abilities. The page likewise consists of implementation alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, enter a number of instances (in between 1-100). 6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive user interface where you can try out various triggers and change design specifications like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for reasoning.

This is an exceptional method to check out the design's thinking and text generation abilities before integrating it into your applications. The play ground supplies immediate feedback, helping you understand how the design reacts to various inputs and letting you tweak your prompts for optimal outcomes.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning utilizing a released 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a request to produce text based on a user prompt.

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 release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the method that finest matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

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

The model internet browser shows available models, with details like the company name and design capabilities.

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

- Model name

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

8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of instances (default: 1). Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your deployment 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 configurations for accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
    
    The implementation procedure can take numerous minutes to complete.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates 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 note pad and range 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, complete the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
12. In the Managed deployments area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
15. Model name.
16. 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 business construct innovative services using AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and optimizing the inference performance of large language models. In his leisure time, Vivek delights in hiking, viewing movies, and trying various foods.
    
    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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about developing services that help customers accelerate their AI journey and unlock organization value.