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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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses support learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its reinforcement learning (RL) action, which was used to improve the design's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's equipped to break down complicated queries and factor through them in a detailed manner. This assisted reasoning procedure allows the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, logical thinking and information interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient inference by routing queries to the most pertinent specialist "clusters." This approach enables the design to specialize in various problem domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

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

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and examine models against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, 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 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 releasing. To request a limit boost, produce a limitation increase request and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and evaluate models against key security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model responses 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 develop the guardrail, see the GitHub repo.

The general flow 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 out to the model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing 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, pick Model catalog under Foundation models in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.

The design detail page provides essential details about the design's abilities, rates structure, and application standards. You can find detailed usage instructions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, including content development, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities. The page likewise consists of implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be triggered to set up the deployment 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 Variety of circumstances, enter a number of instances (in between 1-100). 6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and adjust design specifications like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for inference.

This is an outstanding method to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design responds to various inputs and letting you fine-tune your prompts for optimum results.

You can rapidly check the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using 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 develop the guardrail, forum.altaycoins.com see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a demand to produce text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 provides 2 practical methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the technique that best matches 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, pick JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to see the design details page.
  
  The design details page includes the following details:
  
  - The design name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab includes essential details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage guidelines
  
  Before you deploy the model, it's advised to examine the design 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 produced name or produce a custom one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of circumstances (default: 1). Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the design.
    
    The deployment procedure can take numerous minutes to complete.
    
    When implementation is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing 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 essential AWS permissions 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 releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise utilize 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 shown in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
12. In the Managed implementations area, find the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're erasing the proper release: 1. Endpoint name.
15. Model name.
16. 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 want 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 going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 develop innovative services utilizing AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his totally free time, Vivek enjoys treking, watching movies, and attempting different 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 tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing options that assist clients accelerate their AI journey and unlock company value.