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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement learning (RL) step, which was used to improve the design's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down complex queries and reason through them in a detailed way. This assisted reasoning process permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, logical thinking and data interpretation jobs.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing effective inference by routing questions to the most pertinent professional "clusters." This approach permits the model to specialize in different issue domains while maintaining general efficiency. 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 deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model 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 effective designs to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate models against key security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, 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 circumstances. To examine 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 use. 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, create a limit boost request and reach out to your account team.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Establish approvals to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and evaluate models against essential security requirements. You can execute safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model 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 circulation includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
The design detail page supplies necessary details about the model's capabilities, prices structure, and execution guidelines. You can discover detailed usage directions, consisting of sample API calls and code bits for integration. The model supports numerous text generation jobs, consisting of content production, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking capabilities.
The page also includes release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of circumstances (in between 1-100).
6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can explore various prompts and change design 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 instance, material for inference.
This is an excellent method to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, assisting you understand how the design responds to various inputs and letting you fine-tune your triggers for optimum results.
You can quickly test the model in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends out a request 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, wiki.myamens.com and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the method that finest fits 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 triggered to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model browser shows available designs, with details like the service provider name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows key details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
5. Choose the model card to view the model details page.
The model details page consists of 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 specifications.
- Usage guidelines
Before you release the design, it's recommended to review the model details and license terms to confirm compatibility with your use case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, use the automatically created name or develop a custom-made one.
- For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to release the model.
The release procedure can take numerous minutes to finish.
When deployment is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up 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 deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied 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 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 implement it as displayed in the following code:
Clean up
To avoid undesirable charges, finish the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. - In the Managed releases section, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
- Model name.
- status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed 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 checked out 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 going. 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 business build ingenious solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek delights in hiking, enjoying movies, and trying various 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 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 product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building services that assist customers accelerate their AI journey and unlock company worth.