Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models 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, in addition to the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and responsibly 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 comparable steps to release the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement learning (RL) action, which was utilized to fine-tune the model's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated queries and factor through them in a detailed manner. This guided reasoning process allows the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, rational thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective inference by routing inquiries to the most pertinent expert "clusters." This approach enables the design to focus on various problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

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

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate models against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing 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, create a limit boost request and reach out to your account team.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and examine designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design responses 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 basic 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 inference. After receiving the model's output, another guardrail check is applied. 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 bytes-the-dust.com output stage. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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

The design detail page provides vital details about the design's abilities, prices structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, including content development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning abilities.
The page likewise includes release choices and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose 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, get in a variety of circumstances (in between 1-100).
6. For Instance type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.

When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can explore different prompts and change model criteria like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, <｜begin▁of▁sentence｜><｜User｜>material for inference<｜Assistant｜>.

This is an exceptional way to explore the model's thinking and text generation abilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.

You can quickly check 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 using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the technique that finest matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:

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

The design browser shows available models, with details like the provider name and design abilities.

4. Search for higgledy-piggledy.xyz DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card shows essential details, consisting of:

- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design

5. Choose the model card to view the model details page.

The design details page includes the following details:

- The model name and company details.
Deploy button to release the design.
About and Notebooks tabs with detailed details

The About tab includes crucial details, such as:

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

Before you deploy the design, it's recommended to review the model details and license terms to validate compatibility with your usage case.

6. Choose Deploy to proceed with implementation.

7. For Endpoint name, utilize the immediately created name or produce a custom one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of instances (default: 1).
Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.

The release procedure can take several minutes to finish.

When release is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate 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 need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals 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 model is provided in the Github here. You can clone the note pad and range 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:

Tidy up

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

Delete the Amazon Bedrock Marketplace implementation

If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
2. In the Managed deployments section, locate the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're deleting the appropriate release: 1. Endpoint name.
2. Model name.
3. 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 explored how you can access and release 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 assists emerging generative AI business build ingenious services utilizing AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his free time, Vivek takes pleasure in hiking, watching movies, and attempting 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 Science and Bioinformatics.

Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group 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 options that help clients accelerate their AI journey and unlock company worth.