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 versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support knowing (RL) action, which was used to improve the model's reactions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex questions and reason through them in a detailed manner. This directed reasoning process allows the model to produce more accurate, transparent, and detailed answers. This design 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 actually captured the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, logical thinking and information interpretation jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling effective reasoning by routing queries to the most appropriate professional "clusters." This approach allows the model to specialize in different issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 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 features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model 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 mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine models against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across 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, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 releasing. To request a limitation increase, develop a limit boost demand and reach out to your account team.

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) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful material, and examine designs against key security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing 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 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 to the design for inference. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, pick Model brochure 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 model detail page provides vital details about the model's capabilities, rates structure, and implementation standards. You can find detailed usage instructions, including sample API calls and code snippets for integration. The design supports various text generation tasks, engel-und-waisen.de consisting of content production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities.
The page also consists of implementation alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, go into a number of circumstances (between 1-100).
6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the model.

When the implementation is total, you can check 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 triggers and adjust design specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area provides instant feedback, helping you understand how the model reacts to various inputs and letting you tweak your prompts for optimum outcomes.

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

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a request to create 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 designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the approach that best suits 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, select Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

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

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

- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model 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 see the model details page.

The model 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 consists of essential details, such as:

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

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

6. Choose Deploy to continue with release.

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

The deployment procedure can take numerous minutes to finish.

When implementation is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.

You can run additional demands against the predictor:

Implement guardrails and run reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:

Tidy up

To prevent unwanted charges, complete the actions in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments.
2. In the Managed deployments area, locate the endpoint you want 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 appropriate implementation: 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 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 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 designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business develop innovative options utilizing AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his downtime, Vivek enjoys hiking, viewing films, and attempting different 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 Professional 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 constructing services that assist clients accelerate their AI journey and unlock business worth.