Today, we are excited to announce 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, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI ideas on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement learning (RL) action, which was utilized to improve the model's responses beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and factor through them in a detailed manner. This guided reasoning procedure permits the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational thinking and data interpretation jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, allowing effective inference by routing inquiries to the most appropriate professional "clusters." This technique enables the design to concentrate on various issue domains while maintaining overall efficiency. DeepSeek-R1 needs 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 comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, wavedream.wiki and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, 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 model, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you require 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 validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation increase, produce a limit increase demand and connect 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 utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and evaluate models against key safety requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions 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 model. 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 receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final 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 phase. The examples showcased in the following sections show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, choose Model catalog 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 does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
The design detail page provides important details about the model's capabilities, prices structure, and application standards. You can discover detailed use instructions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of content creation, code generation, and concern answering, utilizing its support learning optimization and CoT thinking abilities.
The page likewise includes implementation options and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.
You will be prompted to set up the implementation 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 Variety of instances, go into a variety of instances (in 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 configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you may desire to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the model.
When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can experiment with various triggers and change model criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, content for reasoning.
This is an outstanding method to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground supplies instant feedback, assisting you comprehend how the design reacts to different inputs and letting you tweak your triggers for optimal results.
You can rapidly check the model 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 reasoning using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a request to create text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the technique that finest suits 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, select Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model browser shows available models, with details like the supplier name and design capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows essential details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the model card to see the design details page.
The design details page includes the following details:
- The design name and service provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you deploy the design, it's advised to evaluate the design details and license terms to validate compatibility with your use case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, use the immediately created name or produce a custom one.
- For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of circumstances (default: 1). Selecting appropriate instance types and counts is essential for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The deployment process can take several minutes to complete.
When release is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided 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 reasoning 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 using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Clean up
To prevent undesirable charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed implementations section, find the endpoint you want to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the appropriate release: 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 explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 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 options utilizing AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and optimizing the inference performance of large language models. In his spare time, Vivek takes pleasure in hiking, enjoying films, and trying different foods.
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 Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building services that help consumers accelerate their AI journey and unlock service value.