GitLab

Today, we are excited 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 variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses support finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) action, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's equipped to break down intricate inquiries and reason through them in a detailed manner. This assisted thinking procedure enables the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, rational reasoning and data 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 specifications, making it possible for effective inference by routing questions to the most relevant professional "clusters." This method permits the design to focus on various problem domains while maintaining overall performance. 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 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 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 describes a procedure of training smaller sized, more efficient models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and assess designs against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, 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, pick Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, produce a limitation boost demand and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and assess designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model responses deployed 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 create the guardrail, see the GitHub repo.

The general circulation involves the following actions: 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 design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete 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 use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.

The model detail page supplies essential details about the model's abilities, rates structure, and implementation guidelines. You can discover detailed usage directions, including sample API calls and code bits for integration. The model supports different text generation tasks, including material creation, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning capabilities. The page likewise consists of implementation alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose 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, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, go into a number of instances (in between 1-100). 6. For example type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might desire to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start using the design.

When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in play ground to access an interactive interface where you can try out various prompts and adjust design parameters like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for inference.

This is an excellent way to check out the design's thinking and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, helping you understand how the design responds to numerous inputs and letting you fine-tune your prompts for ideal results.

You can quickly evaluate the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out inference utilizing a released DeepSeek-R1 model 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 execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.

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

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing 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, choose JumpStart in the navigation pane.

The design browser shows available models, with details like the service provider name and design capabilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each model card shows key details, consisting of:

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
  
  5. Choose the model card to see the design details page.
  
  The design details page consists of the following details:
  
  - The model name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage guidelines
  
  Before you release the design, it's recommended to review the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, use the instantly produced name or develop a customized one.

8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number 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 reasoning is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the design.
    
    The implementation procedure can take several minutes to complete.
    
    When release is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can invoke 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 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied 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 also 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 displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, finish the steps in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
12. In the Managed releases area, locate 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 erasing the proper implementation: 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 erase the endpoint if you want to stop sustaining charges. For wiki.snooze-hotelsoftware.de more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we explored 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 started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 companies build ingenious services using AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek takes pleasure in hiking, enjoying motion pictures, and trying various cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing options that assist clients accelerate their AI journey and unlock service worth.