Today, we are delighted 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 model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses support learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement learning (RL) step, which was utilized to refine the model's reactions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complex questions and reason through them in a detailed way. This guided thinking process enables the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, rational thinking and information interpretation tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and garagesale.es is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, allowing effective reasoning by routing inquiries to the most appropriate professional "clusters." This method permits the design to concentrate on different issue domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities 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 sized, more efficient designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor model.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine models against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine 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 instance in the AWS Region you are releasing. To request a limit increase, produce a limit boost demand and reach out to your account group.
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) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, prevent harmful content, and assess models against essential security requirements. You can implement security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
The basic circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last outcome. 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 took place 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can utilize 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 company and pick the DeepSeek-R1 design.
The model detail page supplies necessary details about the model's abilities, pricing structure, and application guidelines. You can discover detailed usage directions, including sample API calls and code snippets for integration. The model supports different text generation tasks, including material creation, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning capabilities.
The page also consists of and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be prompted to configure the release 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 instances, go into a variety of circumstances (in between 1-100).
6. For Instance type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive user interface where you can experiment with various prompts and adjust model parameters like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, material for reasoning.
This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your prompts for optimum outcomes.
You can quickly check the design in the play area through the UI. However, to invoke the released design 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 perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a request to create text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of 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 design through SageMaker JumpStart provides 2 practical methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the method that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing 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 browser shows available designs, with details like the service provider name and design abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to see the model details page.
The model details page includes the following details:
- The design name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you deploy the model, it's suggested to review the design details and license terms to confirm compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the immediately produced name or create a customized one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the number of circumstances (default: 1). Selecting proper circumstances types and counts is essential for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the model.
The implementation procedure can take several minutes to finish.
When implementation is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
You can run additional requests against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also 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 revealed 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 release
If you released the design utilizing 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 area, locate 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 erasing the appropriate implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released will sustain costs 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe 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 helps emerging generative AI companies build innovative solutions utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of big language models. In his free time, Vivek takes pleasure in hiking, seeing movies, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 dealing with 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 enthusiastic about constructing options that help clients accelerate their AI journey and unlock organization worth.