Today, we are excited 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, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI ideas on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An distinguishing feature is its reinforcement learning (RL) action, which was utilized to improve the model's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down complex questions and factor through them in a detailed way. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, sensible reasoning and data interpretation tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for effective inference by routing inquiries to the most appropriate professional "clusters." This technique permits the design to specialize in different problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. 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 models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor model.
You can release 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 utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against crucial security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine 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 use. 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 increase, produce a limit boost request and connect to your account team.
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) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and examine models against essential security criteria. You can carry out security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess 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 create 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 inference. After receiving the model'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 showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives 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 steps:
1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't 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 provides necessary details about the design's capabilities, pricing structure, and execution standards. You can find detailed use guidelines, including sample API calls and code bits for integration. The design supports different text generation jobs, consisting of content development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities.
The page also consists of implementation choices and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.
You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a variety of instances (in between 1-100).
6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can explore various prompts and adjust model parameters like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for inference.
This is an exceptional way to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, assisting 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 playground through the UI. However, to conjure up the released design 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 released DeepSeek-R1 design through Amazon Bedrock using 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 actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a demand to generate text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the approach that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model browser shows available models, with details like the supplier name and model abilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals essential details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to see the model details page.
The design details page consists of the following details:
- The model 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 specs.
- Usage guidelines
Before you release the design, it's suggested to examine the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, use the instantly generated name or create a custom one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of instances (default: 1). Selecting proper circumstances types and counts is essential for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
- Review all configurations for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the design.
The deployment procedure can take several minutes to finish.
When release is complete, your endpoint status will alter to InService. At this point, wiki.vst.hs-furtwangen.de the model is prepared to accept inference demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime customer 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 needed 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 deploying the model is provided 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To prevent unwanted charges, complete the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed deployments section, locate the endpoint you want 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 expenses if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 Getting begun with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop innovative solutions using AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference performance of big language designs. In his downtime, Vivek delights in treking, viewing films, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about developing services that assist consumers accelerate their AI journey and unlock business worth.