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 model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.

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 process from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) step, which was used to improve the model's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complex queries and reason through them in a detailed way. This assisted reasoning procedure enables the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, logical reasoning and information 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 specifications, enabling effective reasoning by routing queries to the most pertinent specialist "clusters." This approach allows the model to concentrate on different problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient 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 mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various 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, hb9lc.org you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. 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 limit boost request and connect to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful material, and examine models against essential safety criteria. You can implement safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design responses released 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 general flow 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 design for reasoning. After getting 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 happened at the input or output phase. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, demo.qkseo.in complete the following actions:

1. On the Amazon Bedrock console, pick Model catalog under Foundation models 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 pick the DeepSeek-R1 design.

The model detail page offers necessary details about the model's capabilities, rates structure, and application standards. You can find detailed use guidelines, including sample API calls and code bits for integration. The design supports various text generation tasks, consisting of content creation, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking abilities.
The page also consists of release alternatives 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 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 Number of circumstances, get in a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might desire to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.

When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust design parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an excellent method to explore the model's reasoning and text generation abilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the design responds to different inputs and letting you fine-tune your triggers for ideal results.

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 shows how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to generate 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 solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical approaches: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the method that finest matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:

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

The design browser displays available designs, with details like the company name and design capabilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows key details, consisting of:

- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, allowing 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 consists of the following details:

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

The About tab consists of crucial details, such as:

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

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

6. Choose Deploy to continue with deployment.

7. For Endpoint name, utilize the immediately produced name or develop a customized one.
8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of instances (default: 1).
Selecting appropriate circumstances types and counts is essential for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly recommend adhering 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 deployment is total, your endpoint status will change to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS permissions 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 supplied in the Github here. You can clone the notebook and range 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 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 tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
2. In the Managed implementations area, 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 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 desire 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and hb9lc.org 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 business develop innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek takes pleasure in hiking, enjoying movies, and trying different foods.

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 a Professional 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 passionate about developing services that help clients accelerate their AI journey and unlock business value.