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, in addition to the distilled variations ranging from 1.5 to 70 billion criteria 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 actions to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support discovering to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its support knowing (RL) step, which was utilized to refine the model's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down complex inquiries and reason through them in a detailed way. This assisted thinking process permits the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be integrated into different workflows such as agents, sensible thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most appropriate specialist "clusters." This approach permits the model to concentrate on different issue domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning abilities 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 describes a procedure of training smaller sized, more effective models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.
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 location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and assess models against essential security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply 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 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 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 ask for a limitation increase, create a limit increase request and connect to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and assess designs against essential security requirements. You can carry out precaution 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 utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The general circulation 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 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 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 gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane.
At the time of composing 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 company and choose the DeepSeek-R1 design.
The design detail page provides essential details about the design's capabilities, pricing structure, and execution standards. You can find detailed use instructions, including sample API calls and code snippets for integration. The design supports different text generation tasks, including material creation, code generation, and question answering, using its support learning optimization and CoT thinking capabilities.
The page likewise includes deployment choices and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.
You will be prompted 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 instances, go into a number of circumstances (in between 1-100).
6. For Instance type, select 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 sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start using the design.
When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can experiment with different prompts and change design specifications like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, content for inference.
This is an outstanding method to check out the model's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you comprehend how the model reacts to different inputs and letting you tweak your prompts for optimum results.
You can quickly test the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing 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 produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a demand to produce text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the technique that finest matches your requirements.
Deploy DeepSeek-R1 through UI
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select 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 design browser displays available designs, with details like the supplier name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals key details, consisting of:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if appropriate), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the design 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 deploy the design. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you deploy the design, it's recommended to review the model details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, use the instantly created name or develop a custom-made one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the number of instances (default: 1). Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all configurations 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 implementation process can take a number of minutes to complete.
When release is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime client and incorporate 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 permissions 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 design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
You can run additional demands against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To avoid undesirable charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. - In the Managed releases area, locate the endpoint you wish to delete.
- Select the endpoint, systemcheck-wiki.de and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire 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 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 started 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 utilizing AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of big language models. In his spare time, Vivek delights in hiking, watching movies, and trying different 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 Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that assist customers accelerate their AI journey and unlock business value.