Today, we are delighted 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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes support discovering to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support knowing (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down intricate queries and reason through them in a detailed way. This guided reasoning procedure permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, sensible thinking and data analysis tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most pertinent professional "clusters." This approach enables the design to specialize in different issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 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 comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several 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 release the DeepSeek-R1 design, you require 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 using ml.p5e.48 xlarge for endpoint usage. 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, produce a limitation increase request and connect to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and assess designs against crucial security requirements. You can execute security measures for the DeepSeek-R1 design 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 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 involves 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 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 final 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 took place at the input or output stage. The examples showcased in the following sections demonstrate reasoning utilizing 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 actions:
1. On the Amazon Bedrock console, setiathome.berkeley.edu choose Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize 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 company and choose the DeepSeek-R1 model.
The model detail page supplies essential details about the model's capabilities, pricing structure, and execution standards. You can find detailed usage guidelines, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of material production, code generation, pipewiki.org and concern answering, using its support finding out optimization and CoT reasoning capabilities.
The page also includes release choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, pick Deploy.
You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, go into a number of circumstances (between 1-100).
6. For Instance type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust design parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, material for inference.
This is an excellent method to check out the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies instant feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.
You can rapidly test the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends 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, integrated algorithms, and prebuilt ML solutions that you can release with just 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 design through SageMaker JumpStart provides two practical approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the technique that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the console, pick Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model internet browser displays available designs, with details like the service provider name and model capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the design card to view the model details page.
The design details page includes the following details:
- The design name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the model, it's recommended to examine the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, use the immediately generated name or produce a custom-made one.
- For example type ¸ choose an instance type (default: raovatonline.org ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your deployment 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 configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The release process can take several minutes to complete.
When release is complete, your endpoint status will change to InService. At this point, the design is prepared to accept reasoning requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and run 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 utilize 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 shown in the following code:
Tidy up
To prevent undesirable charges, complete the actions in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. - In the Managed releases section, locate the endpoint you want to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design 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 checked out 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 going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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 construct ingenious solutions using AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his downtime, Vivek enjoys treking, watching motion pictures, and trying different foods.
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 technology and Bioinformatics.
Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing options that help customers accelerate their AI journey and unlock service worth.