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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support finding out to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying function is its reinforcement learning (RL) step, which was used to improve the model's reactions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided reasoning procedure permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create 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 different workflows such as representatives, rational thinking and information interpretation jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing effective reasoning by routing questions to the most appropriate specialist "clusters." This approach permits the model to concentrate on various issue domains while maintaining total performance. DeepSeek-R1 needs a minimum of 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and examine models against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, yewiki.org 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, select Amazon SageMaker, and validate you're using 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 ask for a limit increase, produce a limit increase demand and connect to your account group.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and assess models against essential safety requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model responses deployed 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 produce the guardrail, see the GitHub repo.
The basic flow involves 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 to the model for engel-und-waisen.de 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 stepped in 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 sections demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke 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 design's capabilities, prices structure, and application guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content production, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities.
The page likewise includes release choices and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be prompted to configure the implementation 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 circumstances, get in a number of instances (in between 1-100).
6. For Instance type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can try out various triggers and change design parameters like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, content for reasoning.
This is an exceptional method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The playground offers instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.
You can rapidly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning using 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 develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, 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, integrated algorithms, and prebuilt ML options that you can release 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 utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 using 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, select JumpStart in the navigation pane.
The model internet browser displays available designs, with details like the service provider name and model capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals essential details, including:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
5. Choose the design card to view the design details page.
The model details page consists of the following details:
- The model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you deploy the design, it's recommended to review the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, utilize the instantly created name or develop a customized one.
- For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the number of circumstances (default: 1). Selecting suitable circumstances types and counts is vital for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
- Review all setups for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to release the model.
The implementation process can take numerous minutes to finish.
When release is complete, your endpoint status will change to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can keep track of 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 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run 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 utilize 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 revealed in the following code:
Tidy up
To prevent undesirable charges, finish the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed 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 deployments. - In the Managed implementations section, 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 erasing the appropriate release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe 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 helps emerging generative AI companies build innovative services using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning efficiency of large language models. In his free time, Vivek takes pleasure in treking, seeing films, and attempting 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 Professional Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that assist customers accelerate their AI journey and unlock organization value.