Today, we are thrilled 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, together with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to begin 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 big language design (LLM) established by DeepSeek AI that utilizes support finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) action, which was utilized to refine the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's geared up to break down intricate queries and factor through them in a detailed manner. This assisted thinking process enables the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the market'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 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by routing inquiries to the most appropriate professional "clusters." This technique permits the model to focus on different problem domains while maintaining overall efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 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 upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to imitate 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 design, we recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against key safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 releasing. To request a limit increase, produce a limit increase demand and reach out to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous material, and assess models against key security criteria. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop 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 to the design 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 intervened 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 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, total the following actions:
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
The design detail page provides vital details about the model's abilities, pricing structure, and application guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports numerous text generation jobs, including material production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning capabilities.
The page also includes implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a number of instances (between 1-100).
6. For example type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the model.
When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can explore different triggers and change model criteria like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, content for inference.
This is an excellent way to explore the model's thinking and text generation capabilities before incorporating it into your applications. The play area offers immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.
You can quickly test the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing 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 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a demand to create 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 couple of clicks. With SageMaker JumpStart, you can tailor setiathome.berkeley.edu pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical methods: 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 release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design internet browser shows available models, with details like the service provider name and design abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card reveals essential details, including:
- 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 conjure up the design
5. Choose the design card to view the model details page.
The model details page consists of 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 consists of essential 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 validate compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the automatically created name or produce a customized one.
- For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting proper circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to adjust 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 configurations for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The release process can take a number of minutes to finish.
When implementation is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning 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 complete, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and DeepSeek-R1 for reasoning 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 requests against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also utilize 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 avoid unwanted charges, finish the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed releases area, find the endpoint you wish to delete.
- 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 model you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 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 construct innovative solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his free time, Vivek enjoys hiking, viewing movies, 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 Science 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 product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building services that help clients accelerate their AI journey and unlock service value.