DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

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 release DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) step, which was utilized to fine-tune the model’s actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it’s geared up to break down complex inquiries and factor through them in a detailed manner. This guided thinking process allows the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market’s attention as a versatile text-generation model that can be integrated into different workflows such as agents, logical 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, enabling effective reasoning by routing inquiries to the most relevant specialist “clusters.” This method allows the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, engel-und-waisen.de and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against key safety criteria. At the time of writing this blog, larsaluarna.se for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you’re using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit boost, produce a limit boost request and connect to your account team.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous content, and assess models against essential security requirements. You can execute precaution for the DeepSeek-R1 design 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 develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general flow includes the following actions: 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 to the model for inference. After receiving the design’s output, another guardrail check is applied. 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 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 foundation 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 composing this post, you can utilize the InvokeModel API to invoke the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.

The design detail page offers important details about the model’s capabilities, pricing structure, and execution guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, consisting of material creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities.
The page also consists of release choices and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.

You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number of instances (in between 1-100).
6. For example type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, approvals, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may want to evaluate these settings to line up with your organization’s security and compliance requirements.
7. Choose Deploy to start using the design.

When the implementation is complete, you can evaluate DeepSeek-R1’s abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can explore different prompts and adjust model specifications like temperature level and maximum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat template for optimum results. For instance, material for inference.

This is an exceptional method to check out the model’s thinking and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your triggers for optimal outcomes.

You can quickly test the design in the play ground 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 utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a demand to generate text based on a user timely.

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 models to your use case, with your information, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient methods: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s explore both methods to assist you pick the technique that finest suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:

1. On the SageMaker console, select 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 web browser displays available designs, with details like the company name and design abilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals essential details, consisting of:

– Model name
– Provider name
– Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model

5. Choose the model card to view the model details page.

The model details page includes the following details:

– The design name and service provider details.
Deploy button to release the design.
About and Notebooks tabs with detailed details

The About tab consists of crucial details, such as:

– Model description.
– License details.
– Technical specs.
– Usage standards

Before you deploy the model, it’s recommended to evaluate the design details and license terms to validate compatibility with your usage case.

6. Choose Deploy to proceed with implementation.

7. For Endpoint name, use the automatically produced name or create a custom one.
8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of instances (default: 1).
Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.

The release process can take numerous minutes to finish.

When implementation is total, your endpoint status will change to InService. At this point, the design is ready to accept inference demands 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 release is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad 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 create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:

Clean up

To prevent undesirable charges, finish the actions in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you released 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.
2. In the Managed deployments area, find the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you’re erasing the right deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model you deployed will sustain expenses 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, systemcheck-wiki.de 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 companies develop innovative options using AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of large language designs. In his leisure time, Vivek enjoys hiking, watching motion pictures, and attempting various 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 a Professional Solutions Architect working on 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 services that assist customers accelerate their AI journey and unlock business worth.