Today, we are excited 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, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI ideas 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 deploy the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support finding out to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its support knowing (RL) action, which was used to fine-tune the design's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complex queries and factor 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 create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient reasoning by routing questions to the most pertinent professional “clusters.” This method enables the design to concentrate on various issue domains while maintaining general effectiveness. 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 instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure 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 design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate models against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 ask for a limit boost, create a limit increase request and reach out 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) consents to use Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent harmful content, and evaluate designs against essential security criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design responses released 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 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 reasoning. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the final 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 happened at the input or output stage. The examples showcased in the following sections demonstrate 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, total the following steps:

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

The model detail page provides vital details about the design's abilities, prices structure, and implementation guidelines. You can find detailed use directions, consisting of sample API calls and code bits for integration. The design supports numerous text generation tasks, consisting of content creation, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking capabilities. The page likewise consists of release options and licensing details to assist you get started with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, pick 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, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, enter a variety of instances (between 1-100). 6. For Instance type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the design.

When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive interface where you can explore various prompts and adjust model specifications like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for inference.

This is an excellent method to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.

You can quickly evaluate the design in the play ground 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 shows how to carry out inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to produce 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 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 provides 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the approach that best fits your requirements.

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 produce a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the model details page.
  
  The model details page consists of the following details:
  
  - The design name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab includes essential details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the design, it's suggested to review the design details and license terms to verify compatibility with your use case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, utilize the immediately generated name or produce a custom one.

1. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
2. For Initial instance count, enter the number of circumstances (default: raovatonline.org 1). Selecting appropriate instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
3. Review all setups for precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
4. Choose Deploy to release the design.
   
   The release process can take numerous minutes to complete.
   
   When deployment is complete, your endpoint status will change to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
   
   Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
   
   To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model 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 reasoning 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 utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
   
   Tidy up
   
   To avoid undesirable charges, finish the steps in this area to tidy up your resources.
   
   Delete the Amazon Bedrock Marketplace implementation
   
   If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
   
   1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
5. In the Managed deployments section, find the endpoint you want to erase.
6. Select the endpoint, and on the Actions menu, pick Delete.
7. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name.
8. Model name.
9. 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 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 develop innovative solutions using AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of big language designs. In his downtime, Vivek delights in hiking, viewing movies, and trying different foods.
   
   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 a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
   
   Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing solutions that assist consumers accelerate their AI journey and unlock company worth.