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

Today, we are delighted 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, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts 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 also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement finding out to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement learning (RL) action, which was utilized to improve the model’s actions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it’s equipped to break down complex inquiries and reason through them in a detailed way. This guided thinking process allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the market’s attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and data interpretation 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 criteria, enabling effective inference by routing queries to the most pertinent expert “clusters.” This approach enables the design to concentrate on different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release 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 design to more efficient 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 models to mimic the behavior and thinking patterns of the larger 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 design, we recommend deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various 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 design, 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, choose Amazon SageMaker, and confirm 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 limitation increase, develop a limit increase request and connect to your account team.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and examine models against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model actions deployed 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 create the guardrail, see the GitHub repo.

The basic 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 to the model for inference. After getting the design’s output, another guardrail check is used. If the output passes this last check, it’s returned as the final result. 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 took place at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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 steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.

The design detail page offers necessary details about the model’s abilities, prices structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The model supports numerous text generation tasks, consisting of content production, code generation, and concern answering, using its support finding out optimization and CoT thinking abilities.
The page likewise consists of deployment alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, get in a number of instances (between 1-100).
6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to align with your organization’s security and compliance requirements.
7. Choose Deploy to begin utilizing the model.

When the deployment is total, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change design specifications like temperature level and maximum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for ideal outcomes. For example, material for inference.

This is an outstanding method to explore the model’s reasoning and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to numerous inputs and letting you tweak your prompts for optimum outcomes.

You can quickly check the design 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 inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to create 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 solutions 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 information, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s check out both approaches to assist you pick the approach that best fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to develop 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 design card shows essential details, including:

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

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

The model details page consists of the following details:

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

The About tab includes important details, such as:

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

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

6. Choose Deploy to proceed with release.

7. For Endpoint name, utilize the instantly created name or develop a customized one.
8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: archmageriseswiki.com 1).
Selecting suitable circumstances types and counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.

The release procedure can take numerous minutes to complete.

When release is complete, your endpoint status will change to InService. At this point, the model is all set to accept reasoning demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, yewiki.org you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided 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 reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using 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 section to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

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

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.
2. In the Managed deployments area, locate the endpoint you want to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re deleting the proper implementation: 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 erase the endpoint if you want 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 design 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 begun with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business build innovative options utilizing AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the inference performance of large language models. In his downtime, Vivek takes pleasure in hiking, enjoying films, pediascape.science and attempting different 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 a Specialist Solutions Architect dealing with 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 hub. She is enthusiastic about developing services that assist consumers accelerate their AI journey and unlock service value.