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

Today, we are excited to announce 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, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its reinforcement learning (RL) action, forum.batman.gainedge.org which was used to improve the design’s responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and bio.rogstecnologia.com.br objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it’s equipped to break down complicated questions and reason through them in a detailed way. This guided thinking process enables the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the industry’s attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, logical reasoning and information analysis tasks.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient by routing inquiries to the most relevant specialist “clusters.” This method permits 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 instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based on popular open models 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 mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and assess models against essential safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy 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, choose Amazon SageMaker, and validate 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 releasing. To ask for 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) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful material, and assess designs against key security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The general flow includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent out to the model for inference. After receiving the design’s output, another guardrail check is used. 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 happened at the input or output stage. The examples showcased in the following sections show 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, total the following actions:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.

The model detail page offers vital details about the model’s capabilities, pricing structure, and application guidelines. You can find detailed usage directions, consisting of sample API calls and code bits for integration. The model supports numerous text generation jobs, including material development, code generation, and question answering, utilizing its support discovering optimization and CoT thinking abilities.
The page also consists of implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, enter a variety of circumstances (in between 1-100).
6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might want to examine these settings to align with your company’s security and compliance requirements.
7. Choose Deploy to start using the design.

When the implementation is total, you can test DeepSeek-R1’s abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change model specifications like temperature and optimum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimal outcomes. For example, content for inference.

This is an outstanding way to check out the design’s reasoning and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimum results.

You can rapidly evaluate the design in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released DeepSeek-R1 model 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 develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a demand 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 release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s check out both techniques to assist you select the method that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

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

1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The design browser displays available models, with details like the supplier name and design abilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows key details, including:

– Model name
– Provider name
– Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), showing that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model

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

The model details page consists of the following details:

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

The About tab includes essential details, such as:

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

Before you release the model, it’s suggested to evaluate the design details and license terms to validate compatibility with your use case.

6. Choose Deploy to continue with implementation.

7. For Endpoint name, use the automatically produced name or develop a custom-made one.
8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of circumstances (default: 1).
Selecting appropriate circumstances types and counts is important for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the design.

The implementation procedure can take numerous minutes to complete.

When implementation is complete, your endpoint status will alter to InService. At this moment, hb9lc.org the model is prepared to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is provided 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 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 avoid unwanted charges, finish the actions in this section to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the model using Amazon Bedrock Marketplace, complete the following steps:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.
2. In the Managed releases section, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re deleting the appropriate release: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you deployed 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 going with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business develop ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of large language models. In his spare time, Vivek takes pleasure in hiking, viewing motion pictures, and trying different cuisines.

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 item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is enthusiastic about constructing solutions that help customers accelerate their AI journey and unlock service value.