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

Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI‚s first-generation frontier design, wiki.lafabriquedelalogistique.fr DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its support learning (RL) step, which was used to improve the design’s reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it’s geared up to break down complex inquiries and factor through them in a detailed manner. This guided thinking procedure enables the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market’s attention as a flexible text-generation model that can be integrated into various workflows such as representatives, rational thinking and information analysis tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing inquiries to the most pertinent specialist „clusters.“ This approach enables the design to specialize in different problem domains while maintaining overall performance. DeepSeek-R1 needs 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 release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities 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 describes a process 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 model.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check 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 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, produce a limitation boost demand and reach out to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and assess designs against crucial safety requirements. You can carry out safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate 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 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 to the design for inference. After receiving 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 actions:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the to conjure up 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 model.

The design detail page provides vital details about the design’s capabilities, pricing structure, and execution standards. You can discover detailed usage instructions, including sample API calls and code snippets for integration. The design supports numerous text generation tasks, consisting of content development, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking abilities.
The page likewise consists of release options 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 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 Variety of circumstances, enter a number of circumstances (in between 1-100).
6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may want to examine these settings to line up with your organization’s security and compliance requirements.
7. Choose Deploy to begin using the design.

When the deployment is total, you can check DeepSeek-R1’s capabilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can try out 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 design template for optimum results. For instance, material for reasoning.

This is an excellent way to explore the model’s thinking and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for ideal outcomes.

You can rapidly check the design in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning using guardrails with the released 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 produce 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 carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends a request to produce text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just a couple of 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 two practical techniques: using 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 best suits 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, choose 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 model internet browser shows available designs, with details like the service provider name and model capabilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows crucial details, including:

– Model name
– Provider name
– Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design

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

The design details page includes the following details:

– The model name and service provider details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details

The About tab consists of important details, such as:

– Model description.
– License details.
– Technical specifications.
– Usage guidelines

Before you deploy the design, it’s suggested to evaluate the design details and license terms to confirm compatibility with your use case.

6. Choose Deploy to continue with release.

7. For Endpoint name, use the instantly produced name or create 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 circumstances (default: 1).
Selecting appropriate circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to adjust 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 setups for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the design.

The release process can take numerous minutes to finish.

When deployment is complete, your endpoint status will change to InService. At this point, the design is ready to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied 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 inference with your SageMaker JumpStart predictor

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

Tidy up

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

Delete the Amazon Bedrock Marketplace release

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

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
2. In the Managed implementations section, locate the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re deleting the right deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model you deployed will sustain costs 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 explored 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 helps emerging generative AI companies develop ingenious options using AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of big language designs. In his downtime, Vivek delights in treking, viewing movies, 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 a Specialist 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 options that assist clients accelerate their AI journey and unlock service worth.