DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Today, we are excited 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, along with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing function is its support knowing (RL) action, which was utilized to improve the model’s reactions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it’s equipped to break down complicated queries and factor through them in a detailed manner. This assisted thinking procedure enables the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry’s attention as a flexible text-generation model that can be incorporated into various workflows such as agents, rational reasoning and data analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most relevant professional “clusters.” This method enables the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize 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 thinking abilities of the main R1 model to more efficient 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, more efficient models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout 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, select Amazon SageMaker, and wiki.vst.hs-furtwangen.de verify you’re using 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 deploying. To ask for a limit boost, develop a limitation increase demand and connect to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and examine designs against essential security requirements. You can implement security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model responses 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 involves the following actions: First, the system receives 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 getting the design’s output, another guardrail check is used. 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 suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:

1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize 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 supplier and select the DeepSeek-R1 model.

The model detail page offers important details about the design’s abilities, pricing structure, and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for integration. The design supports various text generation tasks, consisting of content development, code generation, and concern answering, using its support finding out optimization and CoT thinking capabilities.
The page also consists of deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.

You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number 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 advised.
Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might want to examine these settings to align with your company’s security and compliance requirements.
7. Choose Deploy to start utilizing the model.

When the implementation is total, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can explore different triggers and adjust model criteria like temperature level and maximum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimum outcomes. For instance, content for inference.

This is an exceptional way to explore the design’s thinking and text generation abilities before integrating it into your applications. The play area provides instant feedback, assisting you comprehend how the design responds to different inputs and letting you fine-tune your triggers for optimal outcomes.

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

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design 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 actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a demand to produce text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in 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 data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s check out both techniques to assist you choose the method that best suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

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

4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows crucial details, consisting of:

– Model name
– Provider name
– Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design

5. Choose the design card to see the design details page.

The design details page includes the following details:

– The model name and provider details.
Deploy button to release the model.
About and Notebooks tabs with detailed details

The About tab includes important details, such as:

– Model description.
– License details.
– Technical specifications.
– Usage guidelines

Before you deploy the design, it’s suggested to examine the model details and license terms to verify compatibility with your usage case.

6. Choose Deploy to continue with release.

7. For Endpoint name, utilize the instantly generated name or create a custom-made one.
8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting suitable circumstances 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 enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.

The release procedure can take several minutes to finish.

When implementation is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the model using a SageMaker runtime client and integrate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To get begun 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 approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied 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 also use 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 shown in the following code:

Tidy up

To prevent unwanted charges, finish the steps in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
2. In the Managed implementations section, locate the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re erasing the appropriate deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want 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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 at AWS. He assists emerging generative AI companies build innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek enjoys hiking, enjoying movies, and trying different cuisines.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 Specialist Solutions Architect working on generative AI with the Third-Party Model Science group 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 building solutions that help consumers accelerate their AI journey and unlock organization value.