Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly 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 models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's geared up to break down intricate inquiries and reason through them in a detailed manner. This guided thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, rational reasoning and information analysis jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, enabling effective inference by routing queries to the most appropriate expert "clusters." This approach enables the model to concentrate on various problem domains while maintaining total performance. DeepSeek-R1 needs at least 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 comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient 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 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and assess designs against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To ask for a limitation boost, create a limit boost demand and reach out to your account group.
Because you will be deploying this design 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 guidelines, see Set up approvals to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful content, and examine models against key security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate 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 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 design 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 result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can use 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 supplier and pick the DeepSeek-R1 model.
The model detail page provides vital details about the design's abilities, rates structure, and execution standards. You can discover detailed use guidelines, including sample API calls and code snippets for integration. The design supports various text generation jobs, including content development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities.
The page also includes release options and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.
You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of circumstances (between 1-100).
6. For Instance type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, disgaeawiki.info for production implementations, you might desire to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can experiment with various prompts and change design specifications like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, material for inference.
This is an outstanding method to check out the model's thinking and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, assisting you comprehend how the design responds to different inputs and letting you tweak your triggers for optimum outcomes.
You can rapidly check the model in the playground through the UI. However, to conjure up the deployed design programmatically with any APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a demand to produce text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center 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 use case, with your information, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the technique that best matches 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, pick Studio in the navigation pane.
2. First-time users will be prompted to develop 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 provider name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows essential details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the model 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 requirements.
- Usage guidelines
Before you release the model, it's advised to examine the model details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the immediately produced name or produce a custom one.
- For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of circumstances (default: 1). Selecting suitable instance types and counts is vital for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The release procedure can take a number of minutes to finish.
When implementation is complete, your endpoint status will change to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS consents 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 releasing the model is provided in the Github here. You can clone the notebook and run 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Clean up
To avoid undesirable charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. - In the Managed releases 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 erasing the appropriate deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain expenses 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 begin. 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 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 construct ingenious services utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning efficiency of large language designs. In his leisure time, Vivek enjoys hiking, watching movies, and attempting different foods.
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 technology and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group 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 building services that help customers accelerate their AI journey and unlock service worth.