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 design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to develop, 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 variations of the models too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement discovering to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually improving both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's geared up to break down intricate questions and reason through them in a detailed manner. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, sensible thinking and data interpretation jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, enabling effective inference by routing questions to the most appropriate specialist "clusters." This approach enables the design to concentrate on different issue domains while maintaining overall efficiency. DeepSeek-R1 needs at least 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, wiki.asexuality.org using it as a teacher model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog, wiki.dulovic.tech we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate designs 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 only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are releasing. To request a limitation boost, develop a limitation boost request and reach out 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) consents to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous content, and assess designs against essential safety criteria. You can execute security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model responses deployed 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 create the guardrail, see the GitHub repo.

The basic circulation includes the following actions: 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 out to the model for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate 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, 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 use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and wiki.snooze-hotelsoftware.de select the DeepSeek-R1 model.

The model detail page supplies necessary details about the design's abilities, rates structure, and application standards. You can discover detailed use guidelines, including sample API calls and code snippets for integration. The model supports numerous text generation tasks, consisting of content development, code generation, and concern answering, using its support discovering optimization and CoT reasoning abilities.
The page likewise consists of deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.

You will be prompted 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 circumstances, get in a variety of circumstances (between 1-100).
6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.

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

This is an excellent way to check out the design's thinking and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.

You can quickly test the model in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning using 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 execute guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a request to create text based upon 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 options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you choose the method 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 prompted to produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model web browser shows available designs, with details like the service provider name and model abilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals essential details, consisting of:

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

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

The design details page consists of the following details:

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

The About tab includes important details, such as:

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

Before you release the design, it's suggested to review the model details and license terms to verify compatibility with your use case.

6. Choose Deploy to continue with release.

7. For Endpoint name, utilize the immediately produced name or create a customized one.
8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of circumstances (default: 1).
Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust 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 setups for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the design.

The release process can take several minutes to complete.

When implementation is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, it-viking.ch which will show relevant metrics and status details. When the implementation is total, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.

You can run extra demands against the predictor:

Implement guardrails and run inference 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 implement it as displayed in the following code:

Tidy up

To prevent undesirable charges, complete the actions in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you deployed the design using Amazon Bedrock Marketplace, total the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
2. In the Managed implementations section, find 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 proper 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 delete 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 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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 innovative options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of big language models. In his spare time, Vivek enjoys treking, viewing motion pictures, and trying various cuisines.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology 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 strategic collaborations 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.