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, together with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly 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 similar steps to release the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's geared up to break down intricate queries and reason through them in a detailed way. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible thinking and information analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient inference by routing questions to the most pertinent expert "clusters." This method allows the design to concentrate on different issue domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate designs against crucial security criteria. 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 produce several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're using 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 releasing. To request a limitation boost, create a limit boost demand and reach out to your account team.

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) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and evaluate models against crucial security requirements. You can implement safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using 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 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 to the design for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.

The model detail page provides necessary details about the design's abilities, pricing structure, and implementation standards. You can find detailed use guidelines, including sample API calls and code snippets for integration. The model supports numerous text generation tasks, including content production, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities.
The page likewise consists of deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, go into a number of instances (between 1-100).
6. For Instance type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may desire to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin using the design.

When the release 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 various prompts and adjust design parameters like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an outstanding way to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you understand how the design reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.

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

Run inference using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a demand to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the technique that best suits 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, pick 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 web browser displays available designs, with details like the service provider name and model abilities.

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

- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design

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

The model details page includes the following details:

- The design name and provider details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details

The About tab consists of crucial details, such as:

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

Before you deploy the design, it's advised to evaluate the model details and license terms to validate compatibility with your usage case.

6. Choose Deploy to continue with release.

7. For Endpoint name, larsaluarna.se utilize the instantly created name or develop a customized one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For hb9lc.org Initial instance count, go into the number of instances (default: 1).
Selecting suitable instance types and counts is vital for expense and performance optimization. Monitor your implementation to change 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 setups for accuracy. For this model, we highly 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 process can take a number of minutes to complete.

When release is total, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, surgiteams.com which will display pertinent metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required 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 deploying the design is provided in the Github here. You can clone the notebook and range 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 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 unwanted charges, finish the actions in this area to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
2. In the Managed implementations area, find the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're deleting the proper deployment: 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 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For surgiteams.com 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 Getting going 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 calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, 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 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 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 center. She is enthusiastic about developing services that help consumers accelerate their AI journey and unlock organization worth.