Today, we are excited 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, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI ideas 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 steps to release the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement knowing (RL) step, which was used to refine the model's responses beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate queries and factor through them in a detailed manner. This assisted thinking process enables the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical thinking and data analysis jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, allowing effective inference by routing inquiries to the most pertinent specialist "clusters." This approach permits the design to concentrate on various problem domains while maintaining total 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 instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, pediascape.science 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor 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 place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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 deploying. To ask for a limitation boost, create a limitation boost request and reach out to your account group.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and evaluate models against essential security criteria. You can implement safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, systemcheck-wiki.de 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 out to the design for inference. After receiving the model's output, another guardrail check is used. If the output passes this last 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 occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides 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, choose Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
The model detail page offers essential details about the design's abilities, rates structure, and execution guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for integration. The model supports numerous text generation tasks, including content development, code generation, and question answering, using its support finding out optimization and CoT reasoning capabilities.
The page likewise includes release alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of instances (in between 1-100).
6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might want to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust design criteria like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for reasoning.
This is an outstanding way to explore the design's thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, helping you understand how the design responds to numerous inputs and letting you tweak your prompts for ideal outcomes.
You can quickly check the model in the play area through the UI. However, to conjure up the deployed 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 shows how to carry out reasoning using a deployed DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference criteria, 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, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free methods: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that finest fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps 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 produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design web browser displays available designs, 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 key details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), showing that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the design card to view the design details page.
The design details page consists of the following details:
- The model name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you release the design, it's advised to evaluate the design details and license terms to validate compatibility with your use case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, use the instantly created name or develop a custom one.
- For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of instances (default: 1). Selecting proper circumstances types and counts is essential for expense and efficiency optimization. Monitor your release to adjust 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 configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the model.
The implementation procedure can take numerous minutes to complete.
When release is total, your endpoint status will change to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad and range 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and bytes-the-dust.com execute it as shown in the following code:
Clean up
To avoid unwanted charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. - In the Managed implementations section, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the proper 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 model 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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 utilizing AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning performance of large language models. In his downtime, Vivek delights in treking, enjoying films, and attempting different cuisines.
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 Science and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that assist consumers accelerate their AI journey and unlock service value.