DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Today, we are thrilled to reveal 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, in addition to the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.

In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses support finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its support learning (RL) action, which was utilized to refine the model’s reactions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it’s equipped to break down intricate queries and reason through them in a detailed way. This assisted thinking process enables the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry’s attention as a versatile text-generation model that can be integrated into various workflows such as agents, rational reasoning and information analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, making it possible for effective reasoning by routing questions to the most relevant specialist “clusters.” This technique enables the model to focus on different issue domains while maintaining general effectiveness. 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 instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, 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 advise deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, it-viking.ch and evaluate models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. 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 use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit boost, develop a limit boost request and connect to your account team.

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) permissions to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and evaluate designs against essential safety requirements. You can execute security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model reactions released 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 develop the guardrail, see the GitHub repo.

The general 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 design for reasoning. After getting the design’s output, another guardrail check is applied. If the output passes this last check, it’s returned as the 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 happened at the input or output phase. The examples showcased in the following sections demonstrate reasoning 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 steps:

1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.

The model detail page offers important details about the model’s capabilities, prices structure, and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for integration. The design supports different text generation tasks, consisting of material development, code generation, and concern answering, bytes-the-dust.com using its support discovering optimization and CoT thinking capabilities.
The page likewise consists of release options and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.

You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, enter a variety of circumstances (between 1-100).
6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may desire to evaluate these settings to align with your organization’s security and compliance requirements.
7. Choose Deploy to begin using the model.

When the implementation is total, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can experiment with various triggers and adjust design specifications like temperature and optimum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat template for ideal outcomes. For example, content for reasoning.

This is an excellent way to explore the model’s reasoning and text generation abilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your prompts for ideal results.

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

Run inference using guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to produce text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both methods to assist you choose the technique that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be triggered 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 company name and design capabilities.

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

– Model name
– Provider name
– Task category (for instance, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design

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

The model details page includes the following details:

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

The About tab includes important details, such as:

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

Before you deploy the design, it’s recommended to examine the model details and license terms to confirm compatibility with your usage case.

6. to proceed with deployment.

7. For Endpoint name, larsaluarna.se use the immediately created name or create a customized one.
8. For engel-und-waisen.de example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of circumstances (default: 1).
Selecting proper instance types and counts is vital for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.

The implementation procedure can take a number of minutes to finish.

When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime customer and integrate 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 necessary AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied 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 inference with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise 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 shown in the following code:

Clean up

To prevent undesirable charges, complete the steps in this section to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the design using 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 releases area, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re deleting the appropriate deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 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 companies construct ingenious services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek takes pleasure in hiking, viewing movies, and trying various foods.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 working on generative AI with the Third-Party Model Science group at AWS.

Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about building options that help clients accelerate their AI journey and unlock service worth.