Today, we are delighted to reveal 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 model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement learning to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was utilized to fine-tune the model's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's geared up to break down complex inquiries and reason through them in a detailed way. This directed reasoning process permits the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, rational thinking and data analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient reasoning by routing queries to the most relevant specialist "clusters." This technique permits the design to focus on various problem domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and gratisafhalen.be Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or larsaluarna.se Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine models against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit increase, create a limitation increase demand and reach out to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and evaluate models against essential security criteria. You can implement security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design actions 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 create the guardrail, see the GitHub repo.

The basic flow includes the following actions: First, the system receives 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 design for reasoning. After receiving 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 showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure 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 brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.

The model detail page provides important details about the design's abilities, prices structure, and implementation guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, consisting of content development, code generation, and concern answering, using its support learning optimization and CoT thinking abilities.
The page likewise includes deployment options and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the deployment 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 number of circumstances (between 1-100).
6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may want to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.

When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can try out different prompts and change design parameters 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, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an exceptional way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play area provides instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.

You can quickly check the design in the playground through the UI. However, to conjure up the released design 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 shows how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a demand to produce 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 deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that finest fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release 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 design browser shows available models, with details like the company name and wavedream.wiki design capabilities.

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

- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, bytes-the-dust.com allowing you to use Amazon Bedrock APIs to conjure up the model

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

The model details page includes the following details:

- The design name and supplier 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 guidelines

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

6. Choose Deploy to continue with deployment.

7. For Endpoint name, utilize the immediately generated name or develop a customized one.
8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1).
Selecting appropriate circumstances types and counts is important for cost and performance 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 configurations for precision. For this model, we highly advise adhering 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 several minutes to complete.

When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.

You can run extra requests against the predictor:

Implement guardrails and run inference with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise utilize 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:

Clean up

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

Delete the Amazon Bedrock Marketplace deployment

If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
2. In the Managed implementations section, locate the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're erasing the correct implementation: 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 wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion

In this post, we explored 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 started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 assists emerging generative AI companies build ingenious services using AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of large language designs. In his leisure time, Vivek enjoys hiking, watching films, and attempting various cuisines.

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

Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing options that help consumers accelerate their AI journey and unlock service value.