Today, we are thrilled to announce 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 design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support discovering to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying feature is its support knowing (RL) action, which was utilized to refine the design's actions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex inquiries and factor through them in a detailed way. This directed thinking procedure permits the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, rational reasoning and information analysis tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most appropriate specialist "clusters." This method allows the design to concentrate on different problem domains while maintaining general effectiveness. 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 release the model. ml.p5e.48 xlarge features 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 effective 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, more effective designs to simulate the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor 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 site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation boost, produce a limitation boost request and reach out 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) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and evaluate models against crucial security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
The basic circulation includes the following steps: 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 out to the model for inference. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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 catalog under Foundation models 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 supplier and choose the DeepSeek-R1 design.
The design detail page supplies vital details about the design's abilities, pricing structure, and application standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, including content development, code generation, and question answering, using its support finding out optimization and CoT reasoning abilities.
The page also consists of deployment options and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, get in a number of circumstances (between 1-100).
6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may want to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start using the model.
When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can try out different prompts and change design specifications like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for reasoning.
This is an excellent way to check out the design's thinking and text generation capabilities before integrating it into your applications. The playground offers instant feedback, surgiteams.com assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimal results.
You can quickly check the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up 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 just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the method that best fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick 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 displays available models, with details like the supplier name and model capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows crucial details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The model details page includes the following details:
- The model name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you release the model, it's advised to evaluate the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, utilize the immediately produced name or develop a custom-made one.
- For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the number of instances (default: 1). Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The implementation process can take several minutes to complete.
When release is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up 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 release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
You can run extra requests 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 develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To prevent undesirable charges, complete the steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the design using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed releases area, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
- Model name.
- Endpoint 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 explored how you can access and deploy 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 models, 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 helps emerging generative AI business build innovative options using AWS and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his free time, Vivek takes pleasure in treking, viewing films, and trying different cuisines.
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 working on 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 hub. She is passionate about developing solutions that assist customers accelerate their AI journey and unlock service worth.