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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 design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly 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 actions to release the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its support learning (RL) step, which was utilized to fine-tune the design's actions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate inquiries and reason through them in a detailed manner. This directed thinking procedure allows the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, rational reasoning and information interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, making it possible for effective reasoning by routing inquiries to the most pertinent professional "clusters." This method enables the model to focus on various problem domains while maintaining general effectiveness. 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 instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities 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 process of training smaller, more effective models to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate models against essential safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing 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 deploying. To request a limitation increase, develop a limitation boost request and connect to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful content, and examine models against essential security requirements. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The general flow involves the following steps: First, the system receives 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 reasoning. After getting the model'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 took place at the input or output phase. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total 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 model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.

The model detail page offers essential details about the design's abilities, pricing structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code bits for integration. The model supports numerous text generation tasks, including content creation, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning capabilities. The page also consists of implementation alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, enter a variety of instances (between 1-100). 6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, disgaeawiki.info for production releases, you may desire to evaluate 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 total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust design parameters like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, content for inference.

This is an excellent method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimum results.

You can quickly test the model in the play area through the UI. However, to conjure up the released model 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 demonstrates how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a demand to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient techniques: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the approach that finest fits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to view the design details page.
  
  The model details page consists of the following details:
  
  - The design name and provider details. Deploy button to deploy 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 standards
  
  Before you deploy the design, it's suggested to review the model details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the immediately generated name or produce a custom-made one.

8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of instances (default: 1). Selecting appropriate circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the design.
    
    The deployment process can take several minutes to finish.
    
    When release is total, your endpoint status will change to InService. At this point, the design is prepared 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 release is total, you can conjure up the design using a SageMaker runtime customer 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 install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Clean up
    
    To prevent unwanted charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations.
12. In the Managed implementations section, locate the endpoint you desire to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're deleting the proper deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design 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 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 designs, SageMaker JumpStart pretrained designs, 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 business develop ingenious services utilizing AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of big language designs. In his leisure time, Vivek delights in hiking, enjoying movies, and attempting various 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 product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing options that help clients accelerate their AI journey and unlock service worth.