Today, we are excited 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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its reinforcement knowing (RL) action, which was used to refine the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's equipped to break down intricate inquiries and reason through them in a detailed way. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be integrated into different workflows such as agents, sensible reasoning and data analysis 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 specifications, enabling efficient reasoning by routing inquiries to the most appropriate expert "clusters." This method enables the design to specialize in various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires 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 design. 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 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you need 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 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 deploying. To request a limitation increase, develop a limitation boost demand and connect to your account team.
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) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and assess models against crucial security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
The general circulation includes the following steps: First, the system receives 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 design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show inference 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, complete the following steps:
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
The model detail page offers necessary details about the design's abilities, pricing structure, and application guidelines. You can find detailed usage directions, consisting of sample API calls and code bits for combination. The model supports various text generation jobs, including material production, code generation, and concern answering, using its support finding out optimization and CoT reasoning capabilities.
The page likewise includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be triggered to set up the deployment 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 Number of instances, enter a variety of circumstances (in between 1-100).
6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can try out various triggers and change model criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, material for reasoning.
This is an outstanding way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the model responds to various inputs and letting you tweak your prompts for ideal results.
You can quickly test the design in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing 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 create 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, 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) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical techniques: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the method that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model web browser shows available models, with details like the supplier name and design capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card reveals essential details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the model card to view the design details page.
The design details page consists of the following details:
- The model name and company details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specs.
- Usage standards
Before you deploy the model, it's advised to examine the model details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the automatically created name or create a customized one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the design.
The deployment process can take numerous minutes to complete.
When deployment is total, your endpoint status will alter to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
You can run extra requests against the predictor:
Implement guardrails and run inference 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 execute it as displayed in the following code:
Tidy up
To prevent unwanted charges, finish the steps in this section to clean up your resources.
Delete the Marketplace deployment
If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. - In the Managed releases area, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, trademarketclassifieds.com see Delete Endpoints and Resources.
Conclusion
In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 Beginning 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 solutions utilizing AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of large language models. In his spare time, Vivek delights in treking, enjoying films, and trying various 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 technology and Bioinformatics.
Jonathan Evans is a Specialist 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 enthusiastic about developing services that help clients accelerate their AI journey and unlock business worth.