1 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

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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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, 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 designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement learning to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's equipped to break down complex queries and reason through them in a detailed way. This directed thinking procedure enables the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, rational thinking and information interpretation jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, allowing effective inference by routing questions to the most relevant specialist "clusters." This approach permits the design to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities 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 sized, more effective designs to mimic the behavior and reasoning 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 recommend deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess models against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and gratisafhalen.be Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect 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 usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit increase, produce a limit 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) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and evaluate designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design actions deployed 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 create the guardrail, see the GitHub repo.

The general flow 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 to the model for inference. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. 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 stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation 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 use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.

The model detail page offers vital details about the design's capabilities, pricing structure, engel-und-waisen.de and implementation guidelines. You can discover detailed usage directions, including sample API calls and code bits for integration. The design supports different text generation jobs, including content development, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking abilities. The page likewise consists of release options and licensing details to assist you get begun with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, get in a variety of circumstances (between 1-100). 6. For example type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, 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 align with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can explore various triggers and change model parameters like temperature level and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, material for inference.

This is an excellent method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the design responds to different inputs and letting you fine-tune your prompts for optimum outcomes.

You can quickly test the model in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out inference 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 develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends a request to produce text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of 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 uses 2 hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you choose the technique that best suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

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

- Model name

• Provider name
• Task classification (for instance, Text Generation). Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the model details page.
  
  The design details page includes the following details:
  
  - The design name and supplier 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 release the design, it's advised to review the design details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the automatically produced name or develop a custom one.

8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of circumstances (default: 1). Selecting appropriate instance types and counts is important for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low .
10. Review all setups for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.
    
    The implementation procedure can take numerous minutes to finish.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the design 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 need to install the SageMaker Python SDK and make certain you have the needed AWS approvals 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 releasing the model is provided in the Github here. You can clone the note pad and range 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and garagesale.es execute it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
12. In the Managed implementations section, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing 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 wish 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Getting going with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business build ingenious services using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language models. In his spare time, Vivek delights in treking, viewing movies, and trying different foods.
    
    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 Professional Solutions Architect dealing with 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 solutions that help clients accelerate their AI journey and unlock service worth.