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 designs 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, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support discovering to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement learning (RL) action, which was utilized to improve the model’s actions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it’s equipped to break down complex inquiries and factor through them in a detailed manner. This guided reasoning process enables 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 focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry’s attention as a flexible text-generation model that can be incorporated into different workflows such as agents, logical thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most pertinent professional “clusters.” This approach enables the design to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, archmageriseswiki.com we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs 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, wiki.lafabriquedelalogistique.fr and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can deploy 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 location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine 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 instance in the AWS Region you are releasing. To request a limitation increase, develop a limit increase request and reach out 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) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging content, and evaluate models against key security requirements. You can execute safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design actions released 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 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 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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 actions:

1. On the Amazon Bedrock console, select Model catalog under Foundation designs 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 provider and pick the DeepSeek-R1 design.

The design detail page supplies necessary details about the design’s abilities, pricing structure, and application standards. You can find detailed usage instructions, consisting of sample API calls and code bits for integration. The model supports different text generation jobs, including content development, code generation, and concern answering, using its support finding out optimization and CoT reasoning capabilities.
The page also consists of implementation alternatives and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.

You will be prompted to configure the implementation 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, get in a variety of instances (in between 1-100).
6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you may desire to examine 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 test 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 adjust design criteria like temperature level and maximum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimum outcomes. For instance, content for reasoning.

This is an outstanding way to explore the model’s reasoning and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, helping you comprehend how the model responds to various inputs and letting you fine-tune your prompts for optimum outcomes.

You can rapidly check the design in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, oeclub.org you need to get the endpoint ARN.

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, genbecle.com and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical techniques: 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 needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps 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 develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design browser shows 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 key details, including:

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

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

The design 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 consists of essential details, such as:

– Model description.
– License details.
– Technical specifications.
– Usage guidelines

Before you deploy the model, it’s advised 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 created name or create a custom-made one.
8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of instances (default: 1).
Selecting suitable instance types and counts is crucial for cost and efficiency 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.
10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.

The deployment process can take several minutes to complete.

When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and yewiki.org use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.

You can run additional requests against the predictor:

Implement guardrails and run reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:

Tidy up

To avoid undesirable charges, complete the actions in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, forum.pinoo.com.tr select Marketplace deployments.
2. In the Managed implementations area, find the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re erasing the correct release: 1. Endpoint name.
2. Model name.
3. 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 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 design 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 Starting with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies construct ingenious options utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning efficiency of large language designs. In his totally free time, Vivek takes pleasure in hiking, seeing films, and trying various cuisines.

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 Science 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 product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about building solutions that assist customers their AI journey and unlock company value.