Today, we are thrilled 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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging 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 start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its reinforcement learning (RL) action, which was used to improve the design's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated inquiries and factor through them in a detailed manner. This directed reasoning procedure enables the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, sensible 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 specifications, enabling effective inference by routing queries to the most relevant specialist "clusters." This approach enables the model to focus on various problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the habits and reasoning patterns of the larger 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 advise releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check 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 releasing. To request a limitation boost, develop a limit boost demand and reach out 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) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous content, and evaluate designs against essential safety requirements. You can execute safety procedures for the DeepSeek-R1 the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model actions 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 develop 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 model for inference. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, choose 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 design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
The model detail page provides important details about the model's capabilities, pricing structure, and implementation standards. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports different text generation jobs, including content production, code generation, and question answering, using its support discovering optimization and CoT reasoning capabilities.
The page also includes deployment alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For optimal efficiency 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 approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might desire to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start using the design.
When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can try out different triggers and adjust design criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For example, material for reasoning.
This is an exceptional method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the design reacts to various inputs and letting you tweak your prompts for optimal outcomes.
You can rapidly test the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to generate 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 options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 uses two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the method that best fits your needs.
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 create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The design internet browser shows available models, with details like the supplier name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows crucial details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
5. Choose the model card to see the design details page.
The design details page consists of the following details:
- The model name and service provider details. Deploy button to deploy the design. 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 deploy the design, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the immediately created name or create a customized one.
- For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of circumstances (default: 1). Selecting appropriate instance types and counts is essential for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the model.
The release procedure can take a number of minutes to complete.
When deployment is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can invoke the design using a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations 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 deploying the model is supplied in the Github here. You can clone the notebook and range 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 using the Amazon Bedrock console or the API, and implement it as shown in the following code:
Tidy up
To prevent undesirable charges, complete the steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. - In the Managed deployments area, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're erasing the correct deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released will sustain costs 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 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 construct innovative services using AWS services and sped up compute. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference performance of large language models. In his spare time, Vivek takes pleasure in hiking, viewing movies, 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 Science 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 item, engineering, and surgiteams.com strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing services that help consumers accelerate their AI journey and unlock company worth.