Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen designs 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, in addition to the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support learning to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was utilized to improve the design's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated questions and factor through them in a detailed way. This assisted reasoning procedure permits the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, sensible reasoning and information interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most pertinent specialist "clusters." This method allows the model to concentrate on different issue domains while maintaining overall efficiency. 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 providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more effective architectures based upon 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, more efficient designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate designs against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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 deploying. To ask for a limitation boost, create a limitation increase request and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and evaluate models against crucial security requirements. You can implement safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic circulation 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 to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the last result. 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 occurred at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 catalog under Foundation models in the navigation pane. At the time of composing 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 supplier and pick the DeepSeek-R1 design.

The design detail page provides essential details about the design's abilities, prices structure, and implementation standards. You can find detailed use directions, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, consisting of content development, code generation, and question answering, using its support learning optimization and CoT reasoning capabilities. The page also includes deployment options and licensing details to help you start with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, select Deploy.

You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Number of circumstances, go into a variety of circumstances (in between 1-100). 6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might want to review these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change design parameters like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for inference.

This is an outstanding method to check out the model's thinking and text generation abilities before incorporating it into your applications. The playground offers instant feedback, helping you understand how the model responds to numerous inputs and letting you tweak your prompts for ideal outcomes.

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

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing 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 created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a request to produce text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the approach that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:

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

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

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the design details page.
  
  The design details page consists of 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 consists of essential details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the design, it's suggested to review the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the automatically created name or create a customized one.

8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.
    
    The deployment process can take several minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime client 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 set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and bytes-the-dust.com use DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional requests 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Tidy up
    
    To prevent unwanted charges, finish 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, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
12. In the Managed deployments section, find the endpoint you wish 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 correct deployment: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, forum.elaivizh.eu Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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 using AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his totally free time, Vivek takes pleasure in treking, viewing motion pictures, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building options that assist clients accelerate their AI journey and unlock company worth.