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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria 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 similar steps to release the distilled variations of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses reinforcement finding out to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its support learning (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's equipped to break down complicated inquiries and factor through them in a detailed way. This guided thinking procedure enables the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, rational reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing queries to the most appropriate expert "clusters." This approach enables the model to concentrate on various problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models 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 describes a procedure of training smaller, more efficient models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and assess models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing 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 examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing 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 releasing. To request a limitation boost, create a limitation increase demand and reach out to your account team.

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) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and assess models against key safety criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and yewiki.org model responses deployed 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 flow includes 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 model for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning using 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, total the following steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs 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 provider and pick the DeepSeek-R1 model.

The model detail page supplies necessary details about the design's abilities, prices structure, and implementation guidelines. You can discover detailed usage directions, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, including material development, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning abilities. The page also includes deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, pick Deploy.

You will be triggered to configure the release 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 Number of circumstances, go into a variety of instances (between 1-100). 6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances 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, setiathome.berkeley.edu service role approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and change model specifications like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, material for reasoning.

This is an excellent method to check out the design's thinking and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the design reacts to different inputs and letting you tweak your prompts for ideal outcomes.

You can rapidly check the model in the play ground through the UI. However, to conjure up the released 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 inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a demand to produce text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub 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 usage case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the technique that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, pick Studio in the navigation pane. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, pick 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 model card. Each model card reveals key details, including:

- Model name

  • Provider name
  • Task category (for instance, Text Generation). Bedrock Ready badge (if appropriate), 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 see the model details page.

    The model details page includes 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 includes essential details, such as:

    - Model description.
  • License details.
  • Technical specifications.
  • Usage guidelines

    Before you deploy the model, it's advised to evaluate the model details and license terms to confirm compatibility with your usage case.

    6. Choose Deploy to proceed with implementation.

    7. For Endpoint name, utilize the automatically created name or produce a custom-made one.
  1. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
  2. For Initial circumstances count, get in the number of instances (default: 1). Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your implementation 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.
  3. Review all setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
  4. Choose Deploy to deploy the design.

    The deployment process can take several minutes to complete.

    When release is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.

    Deploy DeepSeek-R1 using the SageMaker Python SDK

    To get going with DeepSeek-R1 utilizing 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 shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.

    You can run additional 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 create a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:

    Clean up

    To prevent unwanted charges, complete the steps in this section to clean up your resources.

    Delete the Amazon Bedrock Marketplace implementation

    If you released the design using Amazon Bedrock Marketplace, complete the following steps:

    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
  5. In the Managed deployments area, find the endpoint you wish to delete.
  6. Select the endpoint, and on the Actions menu, choose Delete.
  7. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name.
  8. Model name.
  9. Endpoint status

    Delete the SageMaker JumpStart predictor

    The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 assists emerging generative AI business develop ingenious options using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language designs. In his downtime, Vivek delights in hiking, seeing motion pictures, and attempting various foods.

    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 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 tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing solutions that help consumers accelerate their AI journey and unlock company value.