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  • Alisha Rea
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Created Feb 27, 2025 by Alisha Rea@alisharea05160Maintainer

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


Today, we are delighted to announce 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 variations 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 begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's actions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's geared up to break down complicated inquiries and factor through them in a detailed manner. This assisted reasoning process enables the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and information analysis jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most relevant expert "clusters." This technique allows the model to specialize in different issue domains while maintaining total effectiveness. 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 deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon 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 efficient models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation increase, develop a limitation boost demand and connect to your account group.

Because you will be releasing 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 instructions, see Establish approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging content, and examine models against essential security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design actions 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 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 design's output, another guardrail check is applied. 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 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 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 actions:

1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can utilize 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 service provider and pick the DeepSeek-R1 model.

The model detail page provides essential details about the model's capabilities, rates structure, and application guidelines. You can find detailed use directions, including sample API calls and code bits for integration. The design supports different text generation tasks, including content production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities. The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, trademarketclassifieds.com choose Deploy.

You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, wiki.lafabriquedelalogistique.fr go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of instances, get in a variety of instances (between 1-100). 6. For Instance type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For hb9lc.org a lot of utilize cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in playground to access an interactive user interface where you can experiment with different prompts and adjust model criteria like temperature level and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, material for reasoning.

This is an excellent way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, yewiki.org assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimum 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 need to get the endpoint ARN.

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a demand to generate text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor bytes-the-dust.com pre-trained designs 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 two convenient methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the technique 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, choose Studio in the navigation pane. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model web browser shows available designs, with details like the company name and design abilities.

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

- Model name

  • Provider name
  • Task classification (for example, Text Generation). Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design

    5. Choose the model card to view the model details page.

    The model details page includes the following details:

    - The design name and company details. Deploy button to deploy 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 standards

    Before you release the design, it's advised to evaluate the design details and license terms to confirm compatibility with your use case.

    6. Choose Deploy to continue with implementation.

    7. For Endpoint name, utilize the automatically generated name or create a custom one.
  1. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
  2. For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting suitable instance types and counts is essential for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
  3. Review all configurations for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
  4. Choose Deploy to deploy the model.

    The implementation process 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 requests through the endpoint. You can keep track of 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 utilizing a SageMaker runtime client and integrate it with your applications.

    Deploy DeepSeek-R1 using the SageMaker Python SDK

    To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference 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 additional requests against the predictor:

    Implement guardrails and wiki.dulovic.tech 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:

    Clean up

    To avoid unwanted charges, complete the steps 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, select Marketplace releases.
  5. In the Managed implementations area, locate the endpoint you desire to delete.
  6. Select the endpoint, and on the Actions menu, pick Delete.
  7. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
  8. Model name.
  9. 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 model 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 designs, 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 business develop innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference performance of big language designs. In his complimentary time, Vivek delights in treking, watching movies, and attempting different cuisines.

    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and wavedream.wiki Bioinformatics.

    Jonathan Evans is an Expert Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.

    Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing services that assist customers accelerate their AI journey and unlock business worth.
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