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

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 deploy DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement knowing (RL) action, which was used to fine-tune the design’s reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it’s geared up to break down complex queries and factor through them in a detailed way. This directed reasoning procedure enables the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market’s attention as a versatile text-generation model that can be incorporated into different workflows such as agents, logical reasoning and data analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient reasoning by routing queries to the most pertinent expert “clusters.” This technique permits the design to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 needs 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 release the design. 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 efficient architectures based upon 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 effective models to mimic the behavior and surgiteams.com reasoning patterns of the bigger DeepSeek-R1 model, utilizing 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 site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and examine models against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage 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 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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, develop a limit boost demand and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent damaging content, and assess models against essential security criteria. You can carry out safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model actions 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 produce the guardrail, see the GitHub repo.

The general flow includes 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 to the design for reasoning. After getting the design’s output, another guardrail check is used. If the output passes this final check, it’s returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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 composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.

The design detail page provides necessary details about the design’s abilities, prices structure, and application standards. You can find detailed use instructions, consisting of sample API calls and code bits for integration. The model supports different text generation jobs, including content creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT reasoning abilities.
The page likewise consists of release choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select 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 (between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of circumstances (in between 1-100).
6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to review these settings to line up with your company’s security and compliance requirements.
7. Choose Deploy to begin using the model.

When the deployment is complete, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can explore various triggers and adjust model criteria like temperature and optimum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimum results. For example, material for reasoning.

This is an outstanding method to explore the design’s thinking and text generation capabilities before integrating it into your applications. The play area provides instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for optimum results.

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

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a request to produce text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both methods to help you pick the approach that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

The model browser displays available designs, with details like the provider name and design abilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card reveals key details, including:

– Model name
– Provider name
– Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model

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

The model details page includes the following details:

– The design name and supplier details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details

The About tab consists of essential details, such as:

– Model description.
– License details.
– Technical requirements.
– Usage standards

Before you deploy the model, it’s recommended to evaluate the model details and license terms to confirm compatibility with your use case.

6. Choose Deploy to proceed with release.

7. For Endpoint name, utilize the immediately created name or produce a custom-made one.
8. For Instance type ¸ choose a circumstances type (default: higgledy-piggledy.xyz ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of circumstances (default: 1).
Selecting proper circumstances types and counts is important for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.

The release process can take a number of minutes to finish.

When implementation is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.

You can run additional requests against the predictor:

Implement guardrails and run reasoning with your predictor

Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed 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 deployment

If you released the design using Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
2. In the Managed releases area, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re erasing the right release: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model 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 release the DeepSeek-R1 design utilizing 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 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 Inference at AWS. He helps emerging generative AI companies build innovative options using AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek takes pleasure in treking, watching films, and attempting various cuisines.

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 Professional Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.

Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about developing solutions that assist clients accelerate their AI journey and unlock business worth.