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 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, together with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to get started 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 model (LLM) developed by DeepSeek AI that uses reinforcement learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) step, which was utilized to improve the design’s responses beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it’s equipped to break down complex inquiries and reason through them in a detailed way. This directed reasoning process allows the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually captured the market’s attention as a versatile text-generation model that can be integrated into various workflows such as agents, logical thinking and information analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most appropriate expert “clusters.” This technique permits the model to concentrate on various 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 use 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 thinking abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security 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 verify you’re utilizing 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 releasing. To request a limit boost, develop a limit increase demand and connect to your account team.

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) consents to utilize Amazon Bedrock Guardrails. For instructions, it-viking.ch see Set up authorizations to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging material, and examine designs against crucial security requirements. You can implement safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model responses deployed 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 produce the guardrail, see the GitHub repo.

The basic circulation 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 model for inference. After getting the model’s output, another guardrail check is applied. If the output passes this final check, it’s returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete 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 InvokeModel API to invoke 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 design detail page supplies vital details about the design’s abilities, rates structure, and application standards. You can discover detailed use instructions, including sample API calls and code bits for combination. The design supports various text generation tasks, consisting of material creation, code generation, and question answering, pipewiki.org utilizing its support finding out optimization and CoT thinking abilities.
The page also consists of release alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the deployment 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 instances, get in a number of circumstances (between 1-100).
6. For Instance type, select 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 advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may want to examine these settings to align with your company’s security and compliance requirements.
7. Choose Deploy to begin using the design.

When the deployment is complete, you can evaluate DeepSeek-R1’s capabilities 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 change model criteria like temperature and optimum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for ideal results. For instance, material for inference.

This is an excellent method to explore the design’s thinking and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your triggers for optimum results.

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

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends a demand to create text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

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

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s explore both techniques to help you choose the technique that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, select 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 displays available models, with details like the provider name and design capabilities.

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

– Model name
– Provider name
– Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model

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

The model 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 consists of crucial details, such as:

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

Before you release the design, it’s suggested to review the model details and license terms to confirm compatibility with your use 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 an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of circumstances (default: 1).
Selecting suitable 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 inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly 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 several minutes to complete.

When implementation is complete, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime client 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 require to install the SageMaker Python SDK and make certain you have the essential AWS consents 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 deploying the model is supplied in the Github here. You can clone the note pad and range 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 utilize 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 shown in the following code:

Tidy up

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

Delete the Amazon Bedrock Marketplace implementation

If you released the model using Amazon Bedrock Marketplace, complete the following actions:

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

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion

In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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, wiki.vst.hs-furtwangen.de and Getting begun with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build ingenious services utilizing AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his free time, Vivek takes pleasure in hiking, watching motion pictures, and attempting 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 technology 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 strategic partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about developing solutions that assist customers accelerate their AI journey and unlock business value.