Today, we are delighted 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 model, DeepSeek-R1, along 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 get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement discovering to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement knowing (RL) action, which was utilized to refine the design's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's geared up to break down intricate inquiries and reason through them in a detailed manner. This guided reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, logical thinking and information analysis jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient reasoning by routing questions to the most relevant professional "clusters." This approach enables the model to concentrate on different problem domains while maintaining total effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities 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 describes a process of training smaller sized, more efficient designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use 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 require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 boost, develop a limit increase demand and reach out to your account team.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and examine designs against key security criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design reactions released 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 produce the guardrail, see the GitHub repo.
The general flow includes the following actions: First, the system gets 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 design for inference. After receiving the design's output, another guardrail check is applied. 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 showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show reasoning utilizing 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, total the following steps:
1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can use 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 pick the DeepSeek-R1 design.
The model detail page provides important details about the model's capabilities, rates structure, and application guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports different text generation tasks, including material creation, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning abilities.
The page likewise consists of deployment options and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be triggered to configure the deployment 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 Number of circumstances, enter a variety of circumstances (in between 1-100).
6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate 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 area.
8. Choose Open in playground to access an interactive interface where you can explore various prompts and change model specifications like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for inference.
This is an outstanding way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you understand how the model reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.
You can quickly test the design in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to create text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the approach that finest matches your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The design browser shows available models, with details like the supplier name and design abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals crucial details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the model details page.
The design details page includes the following details:
- The model name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you deploy the model, it's advised to evaluate the design 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 generated name or produce a customized one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the number of instances (default: 1). Selecting suitable circumstances types and counts is essential for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The deployment procedure can take numerous minutes to finish.
When deployment is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the is complete, you can invoke the model using 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 use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
You can run extra demands against the predictor:
Implement guardrails and run inference 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 execute it as displayed in the following code:
Tidy 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 design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. - In the Managed releases section, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the proper 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 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 start. For more details, describe 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 helps emerging generative AI companies construct ingenious solutions using AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his spare time, Vivek enjoys treking, seeing films, archmageriseswiki.com and attempting different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building options that assist customers accelerate their AI journey and unlock organization worth.