DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart (#1) · Issues · Carlton Hinkler / amigomanpower

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

Today, we are excited 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, together with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the basic pre-training and fine-tuning procedure. By including RL, wiki.snooze-hotelsoftware.de DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate inquiries and reason through them in a detailed way. This directed thinking process allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, rational reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most relevant professional "clusters." This approach allows the design to concentrate on different problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.

You can deploy 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 place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and evaluate models against crucial safety requirements. 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 develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for higgledy-piggledy.xyz P5e, open the Service Quotas console and under AWS Services, select 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 request a limitation increase, develop a limit increase request and reach out to your account group.

Because you will be deploying this model 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 guidelines, see Set up approvals to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and assess models against key safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions 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 circulation includes 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 reasoning. After getting the design'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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. At the time of composing this post, you can use the InvokeModel API to invoke the model. 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 provides important details about the design's abilities, prices structure, and application guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content creation, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, get in a number of instances (in between 1-100). 6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and change model specifications like temperature and maximum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For example, content for reasoning.

This is an outstanding method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area provides instant feedback, assisting you understand how the to different inputs and letting you tweak your triggers for ideal results.

You can rapidly check the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a demand to produce text based upon a user prompt.

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 deploy with simply a few 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 offers 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the method 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, pick 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 internet browser displays available models, with details like the service provider name and design abilities.

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

- Model name

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

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

The design details page includes the following details:

- The design name and company 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 requirements.
Usage standards

Before you release the design, it's recommended to review the design details and license terms to validate compatibility with your use case.

6. Choose Deploy to proceed with deployment.

7. For Endpoint name, utilize the immediately produced name or produce a custom-made one.

For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
Review all setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
Choose Deploy to deploy the model.

The deployment procedure can take several minutes to finish.

When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the design utilizing 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 set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning 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 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 execute it as shown in the following code:

Clean up

To prevent unwanted charges, complete the actions in this area to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

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 deployments.
In the Managed implementations section, locate the endpoint you desire to delete.
Select the endpoint, and on the Actions menu, pick Delete.
Verify the endpoint details to make certain you're deleting the right deployment: 1. Endpoint name.
Model name.
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 delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion

In this post, we explored 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 designs, SageMaker JumpStart pretrained models, 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 companies build innovative options using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek delights in hiking, watching movies, and attempting various 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 Science and Bioinformatics.

Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.

Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that assist clients accelerate their AI journey and unlock company worth.

Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://topbazz.com) JumpStart. With this launch, you can now release DeepSeek [AI](https://doop.africa)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://www.kotlinx.com:3000) [concepts](https://955x.com) on AWS. 
 In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs too. 
 Overview of DeepSeek-R1 
 DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://washcareer.com) that uses support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the basic pre-training and fine-tuning procedure. By including RL, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DenishaHolyfield) DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate inquiries and reason through them in a detailed way. This directed thinking process allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, rational reasoning and information analysis tasks. 
 DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion [parameters](https://gitlab.tiemao.cloud) in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most relevant professional "clusters." This approach allows the design to concentrate on different problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory. 
 DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model. 
 You can deploy 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 place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and evaluate models against crucial safety requirements. 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 develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://workforceselection.eu) applications. 
 Prerequisites 
 To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:OliverLvx9511) P5e, open the Service Quotas [console](http://119.130.113.2453000) and under AWS Services, select 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](https://ramique.kr) in the AWS Region you are deploying. To request a limitation increase, develop a limit increase request and reach out to your account group. 
 Because you will be deploying this model 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 guidelines, see Set up approvals to use guardrails for material filtering. 
 Implementing guardrails with the ApplyGuardrail API 
 Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and assess models against key safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions 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 circulation includes 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 reasoning. After getting the design'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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show inference using this API. 
 Deploy DeepSeek-R1 in Amazon Bedrock Marketplace 
 Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](https://coolroomchannel.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions: 
 1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the model. 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 provides important details about the design's abilities, prices structure, and application guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content creation, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities.
The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy. 
 You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, get in a number of instances (in between 1-100).
6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up [advanced security](https://vlabs.synology.me45) and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to [start utilizing](http://bristol.rackons.com) the design. 
 When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and change model specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For example, content for reasoning. 
 This is an outstanding method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area provides instant feedback, assisting you understand how the to different inputs and letting you tweak your triggers for ideal results. 
 You can rapidly check the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN. 
 Run reasoning utilizing [guardrails](http://dcmt.co.kr) with the released DeepSeek-R1 endpoint 
 The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](https://udyogseba.com) customer, sets up inference specifications, and sends a demand to produce text based upon a user prompt. 
 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 deploy with simply a few 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](https://huconnect.org) offers 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or carrying out [programmatically](https://bestwork.id) through the SageMaker Python SDK. Let's explore both approaches to assist you choose the method 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, pick 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 internet browser displays available models, with details like the service provider name and design abilities. 
 4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card reveals key details, including: 
 - Model name
- Provider name
- Task category (for example, Text Generation).
[Bedrock Ready](https://saghurojobs.com) badge (if suitable), showing that this model can be signed up with Amazon Bedrock, [allowing](https://huconnect.org) you to utilize Amazon Bedrock APIs to conjure up the design 
 5. Choose the design card to view the model details page. 
 The design details page includes the following details: 
 - The design name and [company details](https://git.whitedwarf.me).
Deploy button to [release](https://2t-s.com) 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 standards 
 Before you release the design, it's recommended to review the design details and license terms to validate compatibility with your use case. 
 6. Choose Deploy to proceed with deployment. 
 7. For Endpoint name, utilize the immediately produced name or produce a custom-made one.
8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your implementation to adjust 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 setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the model. 
 The deployment procedure can take several minutes to finish. 
 When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference demands through the endpoint. You can monitor the deployment progress on the [SageMaker console](http://video.firstkick.live) Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the design utilizing 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 set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning 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 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](https://git.wsyg.mx) or the API, and [execute](http://fujino-mori.com) it as shown in the following code: 
 Clean up 
 To prevent unwanted charges, complete the actions in this area to tidy up your resources. 
 Delete the Amazon Bedrock Marketplace deployment 
 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 deployments.
2. In the Managed implementations section, locate the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're deleting the right deployment: 1. Endpoint name.
2. Model name.
3. [Endpoint](https://shiatube.org) status 
 Delete the SageMaker JumpStart predictor 
 The SageMaker JumpStart model you [deployed](http://121.196.13.116) will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources. 
 Conclusion 
 In this post, we explored 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](http://175.27.189.803000) now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](https://www.teacircle.co.in) [pretrained](https://gitea.ashcloud.com) models, 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](https://www.social.united-tuesday.org) companies build innovative options using [AWS services](http://83.151.205.893000) and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek delights in hiking, watching movies, and attempting various cuisines. 
 Niithiyn Vijeaswaran is a Generative [AI](http://n-f-l.jp) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.bwt.com.de) 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](https://gitea.ashcloud.com) with the Third-Party Model Science group at AWS. 
 Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://sb.mangird.com) center. She is passionate about [developing solutions](https://app.hireon.cc) that assist clients accelerate their [AI](https://deepsound.goodsoundstream.com) journey and unlock company worth.