GitLab

Today, we are delighted 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, together with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar 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 discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement learning (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated questions and reason through them in a detailed way. This guided thinking procedure permits the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, rational thinking and data interpretation tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient inference by routing queries to the most relevant expert "clusters." This approach allows the model to focus on various issue domains while maintaining general efficiency. 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 circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more efficient architectures based on 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 sized, more efficient models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing 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 examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, create a limitation increase request and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and evaluate models against key security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic circulation involves the following steps: First, the system gets 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 model for inference. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final 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 took place at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure 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 doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.

The model detail page offers necessary details about the design's capabilities, rates structure, and implementation standards. You can find detailed use directions, consisting of sample API calls and code bits for combination. The model supports numerous text generation tasks, including content creation, code generation, and question answering, using its reinforcement finding out optimization and CoT thinking abilities. The page also includes release choices and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, select Deploy.

You will be prompted to configure the deployment 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 Variety of instances, go into a number of circumstances (between 1-100). 6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play ground to access an interactive interface where you can explore different prompts and adjust design specifications like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, content for reasoning.

This is an outstanding method to explore the model's thinking and text generation abilities before incorporating it into your applications. The playground provides immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for optimal outcomes.

You can rapidly evaluate the model in the playground through the UI. However, to invoke the deployed 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 demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference 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, built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose 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, choose 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 browser displays available designs, with details like the company name and design abilities.

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

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the design details page.
  
  The design details page includes the following details:
  
  - The design name and company details. Deploy button to release the model. About and demo.qkseo.in 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 validate compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the immediately created name or produce a custom one.

8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1). Selecting proper instance types and counts is important for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the design.
    
    The release procedure can take a number of minutes to complete.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime client 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 set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    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 create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Tidy up
    
    To avoid undesirable charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
    
    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 wish to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you deployed will sustain expenses 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 explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 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 companies build innovative options using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his downtime, Vivek delights in treking, viewing movies, and attempting different cuisines.
    
    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 group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing solutions that help consumers accelerate their AI journey and unlock organization value.