Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI concepts on AWS.

In this post, we demonstrate how to begin 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 thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support knowing (RL) step, which was used to fine-tune the design's reactions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down intricate queries and factor through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be integrated into different workflows such as agents, logical reasoning and data analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, enabling effective inference by routing questions to the most relevant professional "clusters." This technique permits the design to concentrate on different problem 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 utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based on 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 effective designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate models against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and apply 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit boost, develop 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 appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and evaluate models against crucial security criteria. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design 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 produce the guardrail, see the GitHub repo.

The general circulation includes 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, setiathome.berkeley.edu it's sent to the design for reasoning. After getting the model'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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 actions:

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 use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.

The model detail page provides essential details about the design's capabilities, pricing structure, and application guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports various text generation jobs, consisting of material development, code generation, and question answering, using its support discovering optimization and CoT thinking abilities.
The page likewise includes implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be prompted to set up 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 Variety of instances, go into a number of instances (in between 1-100).
6. For Instance type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, 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 line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.

When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can explore various prompts and change model specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, <｜begin▁of▁sentence｜><｜User｜>material for reasoning<｜Assistant｜>.

This is an exceptional way to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the model responds to various inputs and letting you fine-tune your triggers for ideal results.

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

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to create 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 options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical approaches: using 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 technique that finest fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

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

The model internet browser shows available models, with details like the company name and model capabilities.

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

- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model

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

The design details page consists of the following details:

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

The About tab includes essential details, such as:

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

Before you release the design, it's recommended to evaluate the design details and license terms to verify compatibility with your usage case.

6. Choose Deploy to proceed with implementation.

7. For Endpoint name, use the instantly created name or develop a custom one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the design.

The release procedure can take a number of minutes to finish.

When release is total, your endpoint status will alter to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 utilizing 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 needed AWS approvals 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 notebook and run from SageMaker Studio.

You can run extra 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 displayed in the following code:

Tidy up

To prevent unwanted charges, complete the actions in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace release

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

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
2. In the Managed deployments area, locate the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're deleting the correct release: 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 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 assists emerging generative AI business build ingenious options using AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference performance of big language designs. In his spare time, Vivek delights in treking, enjoying movies, and trying various foods.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building services that help clients accelerate their AI journey and unlock company worth.