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Today, we are thrilled to announce 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 design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses support discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement knowing (RL) step, which was used to refine the model's actions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate questions and factor through them in a detailed manner. This directed thinking procedure permits the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as agents, logical reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, allowing effective reasoning by routing inquiries to the most appropriate specialist "clusters." This technique permits the design to specialize in different problem domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective 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, more effective designs to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine designs against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, 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, pick 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 request a limitation boost, develop 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 correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and evaluate models against key safety criteria. You can carry out security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design actions 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 produce the guardrail, see the GitHub repo.

The general circulation includes the following actions: 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 design for inference. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last 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 show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.

The design detail page offers essential details about the design's abilities, pricing structure, and application guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports numerous text generation jobs, consisting of material production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities. The page also includes deployment options and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be prompted to set up 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 Number of circumstances, go into a number of instances (between 1-100). 6. For Instance type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might desire to examine these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust design specifications like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, material for inference.

This is an excellent 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 comprehend how the design reacts to various inputs and letting you fine-tune your triggers for optimum outcomes.

You can rapidly test the design in the playground through the UI. However, to invoke 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 using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a request to produce 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 services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that finest 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, select Studio in the navigation pane. 2. First-time users will be prompted to create a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model browser displays available models, with details like the provider name and model abilities.

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
  
  5. Choose the design card to view the design details page.
  
  The model details page consists of the following details:
  
  - The model name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage guidelines
  
  Before you deploy the design, it's recommended to evaluate the design details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the automatically generated name or develop a custom-made one.

8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of instances (default: 1). Selecting appropriate 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.
10. Review all setups for accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.
    
    The release procedure can take numerous minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing 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 necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, higgledy-piggledy.xyz you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, complete the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
12. In the Managed releases area, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed will sustain costs 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 checked out how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 services utilizing AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of large language models. In his downtime, Vivek takes pleasure in hiking, seeing films, and attempting various 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 Science and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing options that help consumers accelerate their AI journey and unlock organization value.