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

Today, we are thrilled 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 release DeepSeek AI‘s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to get going 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 big language design (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its reinforcement knowing (RL) step, which was utilized to refine the model’s actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it’s equipped to break down complicated inquiries and factor pipewiki.org through them in a detailed manner. This directed thinking procedure enables the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry’s attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and information analysis tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing inquiries to the most relevant expert “clusters.” This method enables the design to focus on various problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based upon 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, more efficient models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, forum.batman.gainedge.org pick Amazon SageMaker, and confirm you’re utilizing 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 request a limitation increase, create a limitation increase request 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 directions, see Establish authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and examine designs against essential security criteria. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or gratisafhalen.be the API. For the example code to create the guardrail, see the GitHub repo.

The general circulation involves the following steps: 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 out to the design for reasoning. After receiving the model’s output, another guardrail check is used. If the output passes this final check, it’s returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn’t support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.

The model detail page offers vital details about the design’s capabilities, rates structure, and application standards. You can discover detailed use guidelines, consisting of sample API calls and code bits for combination. The design supports various text generation jobs, including material production, code generation, and question answering, using its support discovering optimization and CoT thinking abilities.
The page also consists of deployment alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, go into a number of circumstances (in between 1-100).
6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, systemcheck-wiki.de for production releases, you may desire to evaluate these settings to align with your organization’s security and compliance requirements.
7. Choose Deploy to start using the design.

When the release 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 user interface where you can experiment with different triggers and change model criteria like temperature and maximum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for ideal results. For example, material for reasoning.

This is an excellent way to check out the design’s thinking and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for optimal outcomes.

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

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out inference 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up criteria, and sends out a request to create text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, it-viking.ch and prebuilt ML options 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 practical approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let’s check out both approaches to help you select the method that best fits 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, pick Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model web browser shows available designs, with details like the supplier name and design abilities.

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

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

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

The design details page includes the following details:

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

The About tab consists of essential details, such as:

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

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

6. Choose Deploy to continue with deployment.

7. For Endpoint name, gratisafhalen.be utilize the automatically generated name or produce a custom-made one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1).
Selecting proper circumstances types and counts is vital for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.

The deployment process can take several minutes to finish.

When implementation is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.

You can run additional demands 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:

Clean up

To avoid undesirable charges, finish the actions in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

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

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations.
2. In the Managed implementations area, locate the endpoint you want 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 right implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you deployed will sustain costs 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 get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 helps emerging generative AI companies construct ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of large language designs. In his downtime, Vivek enjoys hiking, seeing films, and trying 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 Science 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 tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about constructing solutions that help clients accelerate their AI journey and unlock company worth.