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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging 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 get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy 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 reinforcement discovering to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) action, which was used to improve the model's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's geared up to break down intricate inquiries and factor through them in a detailed manner. This guided thinking procedure enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and information interpretation jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, enabling effective reasoning by routing queries to the most relevant expert "clusters." This technique permits the model to focus on different issue domains while maintaining overall efficiency. 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on 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 mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor yewiki.org model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and evaluate models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using 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 demand 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) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous material, and assess designs against crucial security requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The basic flow includes 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 to the design for inference. After getting 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 occurred at the input or output phase. The examples showcased in the following areas show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, select 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 design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
The model detail page supplies important details about the design's capabilities, prices structure, and application standards. You can find detailed usage instructions, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of material production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities.
The page also includes deployment choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of circumstances (in between 1-100).
6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may desire to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start 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 area to access an interactive interface where you can experiment with various triggers and change model parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, content for reasoning.
This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your triggers for ideal outcomes.
You can rapidly check the design in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using 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 created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends out a request to produce text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 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 help you select the method that finest matches your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart 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 create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model web browser shows available designs, with details like the company name and model capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design 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, permitting you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the design card to see the design details page.
The model details page consists of 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 includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you deploy the model, it's advised to review 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 create a custom-made one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of instances (default: 1). Selecting suitable circumstances types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust 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 setups for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the model.
The deployment procedure can take several minutes to finish.
When release is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require 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 shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
Clean up
To prevent unwanted charges, finish the actions in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
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, pick Marketplace releases. - In the Managed implementations area, locate the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 build innovative options utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference performance of large language models. In his leisure time, Vivek enjoys hiking, seeing films, and attempting 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 dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing options that assist consumers accelerate their AI journey and unlock business value.