Today, we are thrilled 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 design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its support knowing (RL) action, which was used to fine-tune the model's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down intricate inquiries and reason through them in a detailed way. This directed thinking procedure allows the design to produce more precise, 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 extensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, rational reasoning and information interpretation tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most relevant expert "clusters." This method permits the design to concentrate on various issue domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and evaluate designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, 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 deploying. To request a limitation increase, produce a limitation boost demand and connect to your account team.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous content, and assess models against crucial security requirements. You can execute safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses deployed 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 actions: First, the system receives 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 inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (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 brochure under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
The design detail page provides vital details about the design's abilities, rates structure, and execution standards. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of material production, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities.
The page likewise includes release options and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.
You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a variety of circumstances (between 1-100).
6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might want to review 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 total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can experiment with different prompts and change model parameters like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
This is an outstanding method to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for optimum outcomes.
You can the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform inference using a released 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a request to generate text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a couple of 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 provides 2 practical techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the approach 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, choose JumpStart in the navigation pane.
The design web browser displays available designs, with details like the provider name and model abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows key details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The model details page consists of the following details:
- The model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you deploy the model, it's recommended to review the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, use the immediately generated name or develop a custom one.
- For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of circumstances (default: 1). Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, mediawiki.hcah.in Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the model.
The deployment procedure can take a number of minutes to complete.
When implementation 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 monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run 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 also use 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:
Clean up
To avoid undesirable charges, complete the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. - In the Managed releases section, find the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, select 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 SageMaker JumpStart model you released will sustain costs 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 models, SageMaker JumpStart pretrained models, 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 companies develop ingenious options utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his leisure time, Vivek delights in treking, viewing movies, and attempting different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location 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 collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building options that help consumers accelerate their AI journey and unlock company value.