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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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its support learning (RL) step, which was used to refine the design's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down complicated queries and factor through them in a detailed way. This directed thinking process allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, sensible thinking and information interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most relevant specialist "clusters." This method permits the model to concentrate on various issue domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use 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 thinking 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 of training smaller sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate models against essential security criteria. 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 produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and hb9lc.org standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. 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 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 increase, develop a limit boost demand and connect to your account team.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent hazardous content, and evaluate designs against essential safety requirements. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic flow involves 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 receiving the model'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 suggesting the nature of the intervention and whether it happened 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 gives 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 actions:

1. On the Amazon Bedrock console, select Model catalog under Foundation models 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 model detail page offers essential details about the design's capabilities, prices structure, and application standards. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports different text generation jobs, including content development, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities. The page also includes release alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, choose Deploy.

You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, go into a number of circumstances (in between 1-100). 6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may desire to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. 8. Choose Open in play ground to access an interactive interface where you can explore different prompts and change model parameters like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.

This is an excellent way to explore the model's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, helping you comprehend how the model responds to different inputs and letting you tweak your triggers for ideal outcomes.

You can rapidly test the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using 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 create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a demand to create text based upon a user timely.

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 designs to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the method that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

The model web browser displays available designs, with details like the supplier name and model abilities.

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, enabling you to use 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 model name and service provider details. Deploy button to release 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 guidelines
  
  Before you deploy the model, it's suggested to evaluate the model details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the instantly created name or create a custom-made one.

8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. 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.
11. Choose Deploy to release the design.
    
    The release procedure can take several minutes to finish.
    
    When implementation is complete, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for 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 extra requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
12. In the Managed implementations area, find the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're deleting the proper implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released 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 deploy the DeepSeek-R1 design using 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 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 business build innovative solutions utilizing 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 leisure time, Vivek delights in hiking, seeing movies, and attempting different foods.
    
    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 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 product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about constructing solutions that help clients accelerate their AI journey and unlock company value.