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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI ideas 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 deploy the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement discovering to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its support learning (RL) action, which was utilized to improve the model's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down intricate questions and reason through them in a detailed way. This assisted reasoning process enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing 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 numerous workflows such as representatives, sensible thinking and information interpretation tasks.

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, allowing effective inference by routing questions to the most relevant professional "clusters." This approach enables the design to concentrate on various issue 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 use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities 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 describes a process of training smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, 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 model, we suggest releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and evaluate designs against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, larsaluarna.se open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 deploying. To request a limitation boost, produce a limitation boost request and connect to your account group.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging material, and assess designs against essential safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model reactions 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 circulation includes the following steps: 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 final outcome. 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 phase. The examples showcased in the following areas demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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

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

You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, wiki.vst.hs-furtwangen.de go into an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of circumstances, get in a variety of instances (between 1-100). 6. For example type, select 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 facilities settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to line up with your company'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 area to access an interactive user interface where you can experiment with various prompts and adjust model parameters like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for reasoning.

This is an excellent way to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play ground supplies immediate feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for optimum results.

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

Run reasoning using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released 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 produce the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to generate text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options 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 information, and release them into production using 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 check out both techniques to assist you pick the technique that finest matches 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, choose Studio in the navigation pane. 2. First-time users will be prompted to produce a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The design browser shows available models, with details like the supplier name and design capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card reveals key details, including:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if relevant), showing that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
  
  5. Choose the design card to see the model details page.
  
  The model details page includes the following details:
  
  - The design name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you deploy the model, it's advised to evaluate the model details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to proceed with implementation.
  
  7. For Endpoint name, use the instantly generated name or create a custom one.

8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of circumstances (default: 1). Selecting proper instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. to deploy the model.
    
    The deployment procedure can take numerous minutes to complete.
    
    When deployment is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can conjure up the design 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 require to install the SageMaker Python SDK and fishtanklive.wiki make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, finish the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    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, pick Marketplace releases.
12. In the Managed deployments area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses 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 explored how you can access and deploy 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, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, larsaluarna.se 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 helps emerging generative AI companies construct ingenious services using AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his spare time, Vivek delights in treking, seeing movies, and attempting various foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 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 hub. She is enthusiastic about building solutions that help consumers accelerate their AI journey and unlock organization value.