Today, we are delighted 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, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly 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 similar steps to release the distilled versions 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 boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its support learning (RL) action, which was used to refine the model's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's equipped to break down intricate queries and factor through them in a detailed manner. This directed reasoning procedure enables the model 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 abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, rational thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient inference by routing queries to the most appropriate specialist "clusters." This method allows the design to specialize in various problem domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model 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, more efficient designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, produce a limit increase request and connect to your account team.
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) consents to use Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging content, and assess models against essential security criteria. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The general flow involves 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 design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the final result. 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 show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke 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 essential details about the model's abilities, rates structure, and execution guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports various text generation jobs, consisting of content production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning abilities.
The page likewise consists of deployment choices and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.
You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, get in a number of circumstances (in between 1-100).
6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive interface where you can try out various prompts and adjust model criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, content for inference.
This is an outstanding method to explore the design's thinking and text generation capabilities before incorporating 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 triggers for optimum results.
You can rapidly evaluate the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out a demand to create text based upon 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 solutions that you can deploy with just a few 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 uses 2 convenient techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the method that best suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design web browser shows available designs, larsaluarna.se with details like the company name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals crucial details, including:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), showing 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 model details page.
The design details page includes the following details:
- The model name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical specs.
- Usage standards
Before you release the model, it's recommended to evaluate the design details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the instantly produced name or develop a custom-made one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the number of instances (default: 1). Selecting appropriate instance types and counts is essential for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this design, we highly advise adhering 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 several minutes to finish.
When implementation is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
You can run additional requests against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To prevent unwanted charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. - In the Managed releases section, locate 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 appropriate release: 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 release the DeepSeek-R1 model 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 Getting begun with Amazon SageMaker .
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business build ingenious solutions using AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the inference performance of large language designs. In his spare time, Vivek delights in treking, seeing movies, and trying 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 an Expert 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 generative AI center. She is passionate about building solutions that help clients accelerate their AI journey and unlock organization worth.