1 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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Today, we are thrilled 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes support discovering to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement knowing (RL) step, which was used to refine the model's actions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complex queries and factor through them in a detailed way. This assisted thinking process allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, logical thinking and data interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient inference by routing queries to the most relevant expert "clusters." This approach allows the design to specialize in various problem domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and assess designs against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need 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 verify you're using 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 limit boost, produce a limit increase request and connect to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent damaging material, and assess designs against crucial safety criteria. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design actions released 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 gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the check, it's sent out to the model for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.

The design detail page supplies necessary details about the model's abilities, rates structure, and application guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The model supports numerous text generation jobs, including content production, code generation, and concern answering, utilizing its support learning optimization and CoT thinking abilities. The page likewise includes release alternatives and licensing details to help you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose Deploy.

You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, enter a number of circumstances (between 1-100). 6. For Instance type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and change design parameters like temperature level and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For instance, material for inference.

This is an exceptional method to explore the model's thinking and text generation abilities before integrating it into your applications. The play area supplies instant feedback, assisting you understand how the model responds to various inputs and letting you fine-tune your prompts for optimal outcomes.

You can rapidly evaluate the design in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to generate text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the method that finest suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions 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 develop a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

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

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

- Model name

  • Provider name
  • Task category (for example, Text Generation). Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, enabling you to use 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 company details. Deploy button to deploy the model. About and Notebooks tabs with detailed details

    The About tab consists of essential details, such as:

    - Model description.
  • License details.
  • Technical requirements.
  • Usage standards

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

    6. Choose Deploy to proceed with release.

    7. For Endpoint name, utilize the automatically produced name or create a custom one.
  1. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
  2. For Initial circumstances count, go into the number of instances (default: 1). Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
  3. Review all setups for accuracy. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
  4. Choose Deploy to release the model.

    The implementation procedure can take several minutes to finish.

    When implementation 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 an eye on the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.

    Deploy DeepSeek-R1 using the SageMaker Python SDK

    To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for yewiki.org reasoning programmatically. The code for deploying the model is provided 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 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 utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:

    Tidy up

    To avoid undesirable charges, complete the actions in this area to clean up your resources.

    Delete the Amazon Bedrock Marketplace release

    If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:

    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
  5. In the Managed releases area, locate the endpoint you want to erase.
  6. Select the endpoint, and on the Actions menu, choose Delete.
  7. Verify the endpoint details to make certain you're erasing the right 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 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 deploy 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 ingenious solutions using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his leisure time, Vivek delights in hiking, seeing movies, and attempting various 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 technology and Bioinformatics.

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

    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing solutions that assist consumers accelerate their AI journey and unlock company worth.