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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to develop, pipewiki.org experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) action, which was utilized to refine the design's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's geared up to break down complex inquiries and reason through them in a detailed way. This directed thinking process allows the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, rational thinking and information interpretation tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing questions to the most pertinent professional "clusters." This method enables the model to concentrate on different problem domains while maintaining general effectiveness. 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 deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To release 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 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 limit increase, produce a limit increase request and connect to your account team.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and assess models against crucial security criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 actions: 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 design 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 stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened 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, select Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
The model detail page offers important details about the design's capabilities, rates structure, and application standards. You can discover detailed usage directions, including sample API calls and code bits for combination. The design supports different text generation tasks, consisting of material production, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities.
The page also consists of release options and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To start 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, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a variety of instances (between 1-100).
6. For example type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive interface where you can try out different triggers and adjust design criteria like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, content for inference.
This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design responds to numerous inputs and letting you tweak your triggers for ideal outcomes.
You can quickly evaluate the design in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 developed the guardrail, utilize the following code to carry out guardrails. The the bedrock_runtime client, configures inference criteria, and sends out a request to generate text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a couple of 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 offers two hassle-free approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the method that best fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps 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 produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model web browser shows available models, with details like the supplier name and design abilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals key details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), showing that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
5. Choose the design card to see the design details page.
The design 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 consists of crucial details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you release the design, it's recommended to review the model details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, use the automatically created name or create a custom one.
- For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable circumstances types and counts is vital for expense and performance optimization. Monitor your release 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 accuracy. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The deployment process can take several minutes to finish.
When deployment is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can invoke the model utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need 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 shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Clean up
To prevent unwanted charges, complete the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
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, pick Marketplace deployments. - In the Managed implementations area, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the correct release: 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 want 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 companies construct innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek delights in hiking, viewing films, and attempting different foods.
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 technology and Bioinformatics.
Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing options that help consumers accelerate their AI journey and unlock organization worth.