Today, we are delighted 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 design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement learning (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided thinking process allows the model to produce more precise, 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 captured the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, logical thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing effective reasoning by routing questions to the most relevant expert "clusters." This method allows the design to concentrate on different issue domains while maintaining overall performance. 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 circumstances to release the design. 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 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 effective models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and apply 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 instance. To check if you have quotas for P5e, open the Service Quotas and under AWS Services, pick Amazon SageMaker, and verify 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 limit increase, create a limitation increase request and connect to your account group.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish consents to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and examine designs against crucial security criteria. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design reactions deployed 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 circulation involves 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 getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last 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 provides you access to over 100 popular, emerging, and specialized structure 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 conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.
The design detail page provides vital details about the model's capabilities, pricing structure, and implementation guidelines. You can discover detailed usage directions, including sample API calls and code snippets for integration. The model supports different text generation tasks, including content production, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking abilities.
The page also consists of implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be prompted to configure the release details for DeepSeek-R1. The model 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, go into a number of instances (between 1-100).
6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your organization'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 playground to access an interactive interface where you can explore various triggers and change model specifications like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material for reasoning.
This is an exceptional way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal results.
You can quickly check the design in the play ground 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 demonstrates how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, 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, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the approach that best matches your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps 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, choose JumpStart in the navigation pane.
The model web browser shows available models, with details like the service provider name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card reveals essential details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating 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 company 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 standards
Before you release the design, it's suggested to evaluate the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, utilize the immediately generated name or create a custom one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of instances (default: engel-und-waisen.de 1). Selecting suitable instance types and counts is vital for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
- Review all configurations for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the design.
The release procedure can take numerous minutes to finish.
When deployment is complete, your endpoint status will alter to InService. At this point, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and range 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
Clean up
To avoid unwanted charges, finish the actions in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
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, select Marketplace deployments. - In the Managed deployments section, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete 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 release 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 designs, 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 assists emerging generative AI business build innovative solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference performance of large language models. In his leisure time, Vivek enjoys treking, enjoying movies, 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 Science 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 tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building solutions that help consumers accelerate their AI journey and unlock business worth.