Today, we are thrilled 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 model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy 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 learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its support learning (RL) step, which was used to refine the design's responses beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's equipped to break down complicated questions and reason through them in a detailed way. This assisted reasoning procedure permits the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, sensible reasoning and data interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, enabling effective reasoning by routing inquiries to the most pertinent specialist "clusters." This technique enables the model to focus on various problem domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open designs 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 designs to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and examine models against crucial safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you need access to an ml.p5e instance. 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 deploying. To ask for a limitation increase, produce a limitation increase request 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) permissions to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and evaluate models against crucial security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing 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 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 out to the model for inference. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. 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 occurred at the input or output phase. The examples showcased in the following sections show reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (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 catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke 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 supplies essential details about the design's capabilities, prices structure, and application guidelines. You can find detailed usage directions, including sample API calls and code bits for integration. The design supports different text generation tasks, consisting of material production, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities.
The page likewise includes deployment options and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose 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, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a variety of instances (between 1-100).
6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.

When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive user interface where you can try out different prompts and change design parameters like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, <｜begin▁of▁sentence｜><｜User｜>content for inference<｜Assistant｜>.

This is an exceptional way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your triggers for ideal results.

You can quickly test the design 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing 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 created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a demand to create 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 services 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 information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 using 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, choose JumpStart in the navigation pane.

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

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

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

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

The design details page includes the following details:

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

The About tab consists of crucial details, such as:

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

Before you release the model, it's suggested to examine the model details and license terms to confirm compatibility with your usage case.

6. Choose Deploy to proceed with deployment.

7. For Endpoint name, use the instantly created name or develop a custom one.
8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of circumstances (default: 1).
Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.

The implementation procedure can take numerous minutes to finish.

When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and run 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 develop a guardrail using 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 tidy up your resources.

Delete the Amazon Bedrock Marketplace release

If you released the model using Amazon Bedrock Marketplace, total the following steps:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
2. In the Managed implementations section, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, select 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 model you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe 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 assists emerging generative AI companies build ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his leisure time, Vivek delights in hiking, viewing films, systemcheck-wiki.de and attempting different 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 an Expert Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about developing options that help customers accelerate their AI journey and unlock business value.