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 design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative AI ideas on AWS.
In this post, we demonstrate how to get going 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 design (LLM) developed by DeepSeek AI that uses support finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support knowing (RL) action, which was utilized to improve the design's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate queries and factor through them in a detailed way. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, sensible thinking and information analysis tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing questions to the most pertinent expert "clusters." This method permits the model to concentrate on various issue domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning abilities 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, more efficient models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess designs against crucial safety criteria. 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 create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you need access to an ml.p5e instance. 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 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, develop 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 correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Set up consents to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous content, and assess models against key security criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
The general circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After getting 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 show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through . To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can use 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 service provider and select the DeepSeek-R1 design.
The model detail page offers vital details about the design's abilities, wavedream.wiki rates structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, including material development, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking abilities.
The page also consists of deployment options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.
You will be prompted to configure 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 Number of instances, enter a variety of instances (in between 1-100).
6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may desire to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.
When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and adjust design parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for inference.
This is an excellent way to explore the design's thinking and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your triggers for optimum results.
You can quickly evaluate the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out a demand to generate text based on 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 designs to your usage case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the technique that best fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model browser shows available models, with details like the service provider name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows essential details, consisting of:
- 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 model card to view the design details page.
The model details page includes the following details:
- The design name and provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you release the model, it's advised to evaluate the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the immediately generated name or develop a custom one.
- For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of instances (default: 1). Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your release 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.
- Review all configurations for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The deployment procedure can take a number of minutes to complete.
When release 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 deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin 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 approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and run 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
Clean up
To avoid unwanted charges, complete the steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. - In the Managed deployments area, 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 appropriate implementation: 1. Endpoint name.
- Model name.
- 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 explored how you can access and deploy the DeepSeek-R1 design 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 designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 ingenious options 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 downtime, Vivek delights in treking, watching motion pictures, 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 technology and Bioinformatics.
Jonathan Evans is a Professional 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 center. She is passionate about constructing options that assist customers accelerate their AI journey and unlock organization value.