Today, we are excited to reveal 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, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support discovering to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement learning (RL) step, which was utilized to fine-tune the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated queries and reason through them in a detailed manner. This directed reasoning process allows the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, logical thinking and data analysis tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, enabling effective reasoning by routing inquiries to the most appropriate specialist "clusters." This method permits the design to specialize in different issue domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, 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 design, we recommend deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine models against essential security requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails just the ApplyGuardrail API. You can produce 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 deploy 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, setiathome.berkeley.edu and confirm 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 request a limit boost, create a limitation increase request and reach out to your account group.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and evaluate models against essential security criteria. You can carry out security measures for the DeepSeek-R1 design utilizing 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 create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The general circulation involves the following actions: First, the system receives an input for the design. 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 design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections demonstrate inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides 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 actions:
1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke 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 design detail page offers necessary details about the model's capabilities, pricing structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The model supports various text generation jobs, including material development, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities.
The page also includes release options and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.
You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of circumstances (in between 1-100).
6. For Instance type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and adjust design specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.
This is an excellent method to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.
You can quickly check the model in the playground 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 using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a request to generate text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions 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 using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the method that best matches your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps 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 develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design browser shows available models, with details like the company name and design abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows crucial details, including:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), showing that this model can be signed up with Amazon Bedrock, allowing 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 design name and supplier details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's suggested to evaluate the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the automatically created name or develop a custom-made one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the number of circumstances (default: 1). Selecting proper circumstances types and counts is essential for cost and performance 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 configurations for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The implementation process can take several minutes to finish.
When deployment is total, your endpoint status will alter to InService. At this point, the design 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 show appropriate metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize 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 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To prevent undesirable charges, finish the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. - In the Managed implementations 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 release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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 services using AWS services and sped up calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, Vivek takes pleasure in treking, watching movies, and trying various 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 a Professional Solutions Architect dealing with 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 hub. She is enthusiastic about developing solutions that help clients accelerate their AI journey and unlock organization value.