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

Today, we are delighted 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 design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI ideas 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 actions to deploy the distilled versions of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement finding out to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support learning (RL) action, which was used to refine the design’s actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it’s geared up to break down complex queries and factor through them in a detailed manner. This assisted thinking process enables the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry’s attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, sensible thinking and information analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for inference by routing questions to the most relevant specialist “clusters.” This method permits the design to concentrate on different issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient 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 effective models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher 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, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess designs against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, 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, choose Amazon SageMaker, and validate you’re utilizing 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 request a limit boost, develop a limit increase request and reach out to your account group.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and assess designs against essential security criteria. You can carry out safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The general flow involves the following steps: 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 inference. After receiving the model’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 indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate inference 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, complete 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 utilize the InvokeModel API to conjure up the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.

The design detail page provides necessary details about the model’s abilities, rates structure, and implementation guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation jobs, including content development, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning abilities.
The page likewise includes deployment choices and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.

You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, go into a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might 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 release is total, you can test DeepSeek-R1’s capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can try out different triggers and change design parameters like temperature level and optimum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimal results. For instance, material for reasoning.

This is an outstanding method to check out the model’s thinking and text generation capabilities before integrating it into your applications. The playground provides immediate feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your triggers for ideal outcomes.

You can rapidly evaluate the design in the play area through the UI. However, to conjure up the released design 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 demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a demand to generate 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 just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s explore both methods to assist you choose the technique that best fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 utilizing 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 design internet browser displays available designs, with details like the provider name and model capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals key details, including:

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

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 design.
About and Notebooks tabs with detailed details

The About tab includes important details, such as:

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

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

6. Choose Deploy to continue with release.

7. For Endpoint name, use the instantly produced name or develop a custom one.
8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of circumstances (default: 1).
Selecting proper instance types and counts is important for cost and efficiency optimization. Monitor your release 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 configurations for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.

The deployment procedure can take several minutes to complete.

When release is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the model 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 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:

Tidy up

To avoid undesirable charges, finish the actions in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace deployment

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

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases.
2. In the Managed deployments section, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re erasing the correct release: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model you released will sustain costs 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, mediawiki.hcah.in Amazon Bedrock Marketplace, and Getting started 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 focused on developing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek takes pleasure in hiking, seeing motion pictures, and trying various foods.

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 technology and Bioinformatics.

Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is enthusiastic about constructing options that help clients accelerate their AI journey and unlock business value.