Today, we are excited 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 versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses support discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its support learning (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated inquiries and factor through them in a detailed way. This directed reasoning procedure allows the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, rational reasoning and information interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, enabling effective inference by routing inquiries to the most pertinent professional "clusters." This approach enables the design to focus on various issue domains while maintaining general effectiveness. 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 providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model 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 simulate the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate models against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety 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, choose Amazon SageMaker, and validate 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 limitation increase, produce a limit increase request and reach out to your account team.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful content, and examine models against crucial safety criteria. You can execute security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model actions released 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 produce the guardrail, see the GitHub repo.
The basic flow includes the following steps: 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 inference. 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 occurred at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing 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 actions:
1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
The model detail page supplies essential details about the model's abilities, pricing structure, and application standards. You can find detailed use instructions, including sample API calls and code snippets for integration. The design supports numerous text generation tasks, including content production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning capabilities.
The page also consists of implementation options and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of instances (between 1-100).
6. For Instance type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start using the design.
When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive user interface where you can try out various prompts and change model specifications like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, material for inference.
This is an excellent way to check out the design's thinking and text generation abilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the design reacts to different inputs and letting you tweak your prompts for optimal results.
You can rapidly check the design in the playground 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 released DeepSeek-R1 endpoint
The following code example shows how to perform inference 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 develop the guardrail, see the GitHub repo. After you have actually the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a demand to generate text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the method that finest fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions 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 develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model internet browser displays available models, with details like the supplier name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows crucial details, including:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
5. Choose the design card to view the design details page.
The model details page includes the following details:
- The model name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab includes essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you release the model, it's suggested to evaluate the design details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the immediately created name or produce a customized one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting proper circumstances types and counts is important for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to release the model.
The release process can take numerous minutes to complete.
When deployment is total, your endpoint status will change to InService. At this point, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime client and incorporate 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 permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied 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 inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
Clean up
To prevent undesirable charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed deployments section, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the proper release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing 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 models, SageMaker JumpStart pretrained designs, 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 assists emerging generative AI companies build ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, wakewiki.de Vivek enjoys hiking, seeing movies, and attempting various cuisines.
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 dealing with generative AI with the Third-Party Model Science group 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 constructing solutions that help clients accelerate their AI journey and unlock organization value.