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 deploy DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and . You can follow similar steps to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support knowing (RL) action, which was utilized to refine the model’s responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it’s geared up to break down complex inquiries and factor through them in a detailed manner. This directed thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market’s attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, rational thinking and data analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most relevant specialist “clusters.” This method allows the model to specialize in various problem domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design 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, more efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine models against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, systemcheck-wiki.de open the Service Quotas console and under AWS Services, select 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 circumstances in the AWS Region you are releasing. To request a limit increase, create 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) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and evaluate models against key safety requirements. You can implement security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic circulation includes the following actions: First, the system receives 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 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 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 show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not 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 important details about the design’s capabilities, pricing structure, and implementation standards. You can discover detailed use instructions, consisting of sample API calls and code bits for integration. The design supports various text generation jobs, consisting of material creation, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities.
The page likewise includes deployment options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The model 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, get in a variety of circumstances (in between 1-100).
6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to align with your company’s security and compliance requirements.
7. Choose Deploy to start using the model.

When the implementation is complete, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can try out different triggers and change design specifications like temperature level and maximum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat template for optimal outcomes. For instance, content for inference.

This is an outstanding method to check out the model’s reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the design responds to numerous inputs and letting you tweak your triggers for ideal outcomes.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model 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, raovatonline.org see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a request to create text based upon a user timely.

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 couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, wavedream.wiki and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s explore both techniques to assist you choose the approach that best matches 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, pick Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design web browser shows available models, with details like the provider name and model abilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card reveals key details, consisting of:

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

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

The design details page consists of the following details:

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

The About tab includes crucial details, such as:

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

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

6. Choose Deploy to proceed with release.

7. For Endpoint name, use the instantly generated name or produce a custom one.
8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1).
Selecting proper circumstances types and counts is important for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.

The deployment procedure can take a number of minutes to complete.

When deployment is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can monitor it-viking.ch the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release 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 started with DeepSeek-R1 using the SageMaker Python SDK, you will require 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 shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is offered 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 reasoning 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 implement it as displayed in the following code:

Tidy up

To avoid unwanted charges, finish the steps in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace deployment

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, select Marketplace implementations.
2. In the Managed deployments area, find 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 correct release: gratisafhalen.be 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and surgiteams.com Resources.

Conclusion

In this post, we checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 business develop innovative options utilizing AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his downtime, Vivek takes pleasure in treking, viewing films, and trying different foods.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer Science and Bioinformatics.

Jonathan Evans is a Professional Solutions Architect dealing with 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 hub. She is passionate about developing services that assist customers accelerate their AI journey and unlock business worth.