Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs 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, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative AI concepts on AWS.

In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) step, which was used to improve the design's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down complex queries and reason through them in a detailed way. This directed reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, sensible reasoning and information analysis tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, enabling effective reasoning by routing queries to the most appropriate specialist "clusters." This technique permits the model to specialize in different problem domains while maintaining overall effectiveness. 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 circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, create a limit increase request and connect to your account group.

Because you will be deploying this model 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 authorizations to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and assess designs against key safety criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using 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 guardrail check, it's sent out to the design for inference. After receiving the model's output, another guardrail check is used. 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 occurred 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 offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, hb9lc.org complete the following steps:

1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.

The model detail page supplies vital details about the model's abilities, prices structure, and application standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation jobs, including material creation, code generation, and concern answering, using its reinforcement learning optimization and capabilities. The page also includes release choices and licensing details to assist you start with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, pick Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of circumstances, enter a variety of instances (in between 1-100). 6. For Instance type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start using the design.

When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and change model specifications like temperature and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for inference.

This is an excellent way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you understand how the model responds to various inputs and letting you fine-tune your prompts for optimal results.

You can rapidly evaluate the design in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing 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 implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out a demand to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the approach that best 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 prompted to create a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task category (for instance, 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 conjure up the model
  
  5. Choose the model card to view the design details page.
  
  The model details page consists of the following details:
  
  - The model name and provider 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 requirements.
• Usage standards
  
  Before you deploy the model, it's suggested to examine the model details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the instantly created name or create a customized one.

8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of circumstances (default: 1). Selecting appropriate instance types and counts is essential for cost and efficiency optimization. Monitor your deployment 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.
10. Review all setups for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.
    
    The deployment process can take numerous minutes to finish.
    
    When deployment is complete, your endpoint status will alter to InService. At this point, the design is all set to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the model utilizing 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 needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or wiki.dulovic.tech the API, wiki.dulovic.tech and execute it as displayed in the following code:
    
    Clean up
    
    To avoid undesirable charges, complete the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
12. In the Managed deployments area, find the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
15. Model name.
16. 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 erase 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 genbecle.com release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe 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 assists emerging generative AI business build innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of big language models. In his downtime, Vivek enjoys treking, viewing movies, and trying 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 technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect working on 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 center. She is passionate about developing solutions that help clients accelerate their AI journey and unlock company value.