Today, we are thrilled 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 design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative AI ideas on AWS.
In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy 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 uses reinforcement discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its reinforcement learning (RL) step, which was used to fine-tune the model's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's geared up to break down complicated queries and reason through them in a detailed way. This directed thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be integrated into different workflows such as agents, rational thinking and data interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient inference by routing inquiries to the most appropriate specialist "clusters." This technique permits the design to specialize in various issue domains while maintaining general effectiveness. DeepSeek-R1 requires 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 deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more effective architectures based on popular open designs 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 designs to mimic the behavior and thinking 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 deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against crucial security requirements. 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 numerous guardrails tailored to different usage cases and systemcheck-wiki.de apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit increase, create a limitation increase demand and reach out to your account group.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Set up consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and evaluate designs against crucial safety criteria. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model reactions 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 flow 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 out to the model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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 steps:
1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
The design detail page supplies important details about the model's capabilities, rates structure, and execution guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The model supports different text generation tasks, including content development, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities.
The page also includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.
You will be triggered to set up 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 Number of circumstances, go into a number of circumstances (between 1-100).
6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may desire to review these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can explore various triggers and change design criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for reasoning.
This is an outstanding method to check out the model's thinking and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, helping you comprehend how the design responds to different inputs and letting you fine-tune your triggers for optimal results.
You can quickly evaluate the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends out a request to create text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few 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 model through SageMaker JumpStart uses two practical approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the method that finest fits your needs.
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, choose JumpStart in the navigation pane.
The design browser shows available designs, with details like the company name and design capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card reveals key details, consisting of:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
5. Choose the model card to see the design details page.
The model details page consists of the following details:
- The model name and provider 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 standards
Before you release the model, it's recommended to review the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, utilize the immediately produced name or develop a custom one.
- For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the number of instances (default: 1). Selecting appropriate instance 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 selected by default. This is optimized for sustained traffic and low latency.
- Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The deployment process can take numerous minutes to complete.
When implementation is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra demands 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 using the Amazon Bedrock console or the API, and execute it as shown in the following code:
Tidy up
To prevent unwanted charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed releases section, find the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design 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 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 start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 helps emerging generative AI business develop ingenious solutions using AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his downtime, Vivek enjoys treking, viewing motion pictures, and attempting different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location 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 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 AI center. She is passionate about building services that help consumers accelerate their AI journey and unlock business worth.