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Today, we are thrilled to reveal 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 model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly 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 comparable steps to release the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement learning to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) step, which was used to improve the design's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's geared up to break down intricate inquiries and factor through them in a detailed way. This assisted thinking process allows the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation model that can be integrated into various workflows such as agents, sensible reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient inference by routing queries to the most relevant professional "clusters." This technique allows the model to focus on various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking 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 describes a process of training smaller sized, more effective models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, using 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 advise releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and evaluate models against crucial safety 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 develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 instance in the AWS Region you are deploying. To ask for a limit increase, produce a limit increase demand and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging material, and assess designs against key safety criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design reactions released 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 create the guardrail, see the GitHub repo.

The general circulation includes 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 reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final result. 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 stage. The examples showcased in the following sections show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 actions:

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 use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.

The design detail page supplies necessary details about the design's abilities, prices structure, and application standards. You can usage guidelines, including sample API calls and setiathome.berkeley.edu code snippets for combination. The design supports various text generation jobs, including material development, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking capabilities. The page likewise consists of implementation alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of instances, get in a number of instances (between 1-100). 6. For example type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to evaluate 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 test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive interface where you can experiment with various triggers and change model specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for reasoning.

This is an excellent way to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground offers immediate feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for ideal results.

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

Run reasoning using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference 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 produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a request to produce text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy 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 utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the method that best matches your needs.

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 develop 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 design capabilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. Each model card shows essential details, including:

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
  
  5. Choose the design card to view the model details page.
  
  The design details page includes the following details:
  
  - The model name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you release 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 immediately created name or develop a customized one.

8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of instances (default: 1). Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust 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 highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.
    
    The release procedure can take numerous minutes to complete.
    
    When release is total, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime customer 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 need to install 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 reasoning programmatically. The code for deploying the model is provided 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 also 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 displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, complete the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
12. In the Managed deployments area, find 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 implementation: 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 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies develop ingenious solutions using AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his totally free time, Vivek delights in hiking, watching movies, and trying 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 Specialist Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building services that assist consumers accelerate their AI journey and unlock service value.