Today, we are excited 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 model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI ideas 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 deploy the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the design's reactions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated questions and factor through them in a detailed manner. This directed reasoning process allows the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and wiki.myamens.com user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, rational thinking and data interpretation jobs.

DeepSeek-R1 utilizes 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 effective inference by routing inquiries to the most pertinent expert "clusters." This method enables the model to concentrate on various issue domains while maintaining general efficiency. 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 release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities 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 procedure of training smaller, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and assess designs against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and apply 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 deploying. To ask for a limitation increase, develop a limitation boost demand and connect to your account group.

Because you will be deploying this design 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 Establish approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and assess designs against essential security criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design reactions released 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 produce the guardrail, see the GitHub repo.

The basic circulation includes the following steps: 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 out to the model for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the 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 occurred at the input or output stage. The examples showcased in the following areas demonstrate reasoning using 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 steps:

1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up 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 model.

The model detail page supplies vital details about the model's capabilities, systemcheck-wiki.de pricing structure, and application guidelines. You can discover detailed use directions, including sample API calls and code bits for integration. The model supports different text generation tasks, consisting of content development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities.
The page likewise consists of release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.

You will be prompted to configure the implementation 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, enter a variety of circumstances (between 1-100).
6. For example type, choose 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 facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.

When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and adjust model criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an exceptional method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimum outcomes.

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

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 design 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 produce 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 customer, sets up reasoning specifications, and sends out a demand to generate 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 options 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 provides two practical techniques: utilizing the user-friendly 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 steps to deploy DeepSeek-R1 using SageMaker JumpStart:

1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The design browser shows available models, with details like the service provider name and wakewiki.de design abilities.

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

- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, allowing you to use 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 design name and service provider details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details

The About tab consists of essential details, such as:

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

Before you deploy the model, it's recommended to examine the model details and license terms to confirm compatibility with your use case.

6. Choose Deploy to proceed with deployment.

7. For Endpoint name, use the instantly produced name or develop a customized one.
8. For Instance type ¸ select a circumstances type (default: trademarketclassifieds.com ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1).
Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.

The deployment process can take several minutes to complete.

When implementation is total, your endpoint status will change to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS permissions 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 releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.

You can run additional requests against the predictor:

Implement guardrails and run reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise use 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:

Clean up

To prevent unwanted charges, finish the actions in this section to tidy up your resources.

Delete the Amazon Bedrock Marketplace release

If you deployed the model using Amazon Bedrock Marketplace, total the following actions:

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

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete 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 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 Starting 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 construct ingenious services utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference performance of large language models. In his spare time, Vivek takes pleasure in treking, viewing films, and attempting various cuisines.

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 technology and Bioinformatics.

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

Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing solutions that help customers accelerate their AI journey and unlock company value.