DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart (#1) · Issues · Elke Jasso / nurseportal

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, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that utilizes reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) action, which was used to refine the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down intricate queries and reason through them in a detailed manner. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, rational thinking and information analysis jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient inference by routing queries to the most appropriate specialist "clusters." This approach enables the design to specialize in various problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning 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 describes a procedure of training smaller, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess models against key 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 various usage cases and apply them to the DeepSeek-R1 model, 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 request a limitation boost, develop a limit increase request and connect to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and examine models against essential safety requirements. 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 actions 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 create the guardrail, see the GitHub repo.

The general flow includes 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 to the model for inference. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the last outcome. 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 phase. The examples showcased in the following sections demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Marketplace

Amazon Bedrock Marketplace offers 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 brochure under Foundation models in the navigation pane. At the time of writing 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 company and select the DeepSeek-R1 model.

The model detail page supplies essential details about the design's capabilities, rates structure, and application standards. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, including material development, code generation, and concern answering, using its reinforcement learning optimization and CoT thinking capabilities. The page also consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be triggered to set up the deployment 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 circumstances, enter a number of instances (in between 1-100). 6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might desire to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in play area to access an interactive user interface where you can try out various prompts and change design parameters like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference.

This is an exceptional method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your prompts for ideal results.

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

Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a demand to generate text based on 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 just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that finest fits your requirements.

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 produce a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design browser shows available models, with details like the company name and design capabilities.

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

- Model name

Provider name
Task category (for raovatonline.org example, Text Generation). Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model

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

The design details page includes 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 includes important details, such as:

- Model description.
License details.
Technical specs.
Usage guidelines

Before you deploy the design, it's recommended to examine the design details and license terms to verify compatibility with your usage case.

6. Choose Deploy to proceed with release.

7. For Endpoint name, use the automatically generated name or create a customized one.

For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
For Initial circumstances count, go into the number of circumstances (default: 1). Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your release 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.
Review all configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
Choose Deploy to release the design.

The release process can take a number of minutes to finish.

When release is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment 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 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 approvals and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied 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 utilize 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:

Tidy up

To prevent undesirable charges, finish the steps in this area to tidy up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you released the design using Amazon Bedrock Marketplace, total the following actions:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
In the Managed implementations section, locate the endpoint you desire to erase.
Select the endpoint, and on the Actions menu, choose Delete.
Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name.
Model name.
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 erase the endpoint if you wish 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 begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 construct ingenious services utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of large language models. In his leisure time, Vivek delights in hiking, seeing 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 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 tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing services that help customers accelerate their AI journey and unlock service worth.

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](https://www.florevit.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](http://git.r.tender.pro) concepts on AWS. 
 In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs also. 
 Overview of DeepSeek-R1 
 DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://111.47.11.70:3000) that utilizes reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) action, which was used to refine the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down intricate queries and reason through them in a detailed manner. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based [fine-tuning](http://vts-maritime.com) with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, rational thinking and information analysis jobs. 
 DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient inference by routing queries to the most appropriate specialist "clusters." This approach enables the design to specialize in various problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 [GPUs providing](http://carvis.kr) 1128 GB of GPU memory. 
 DeepSeek-R1 distilled models bring the reasoning 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 describes a procedure of training smaller, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design. 
 You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess models against key security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and [Bedrock](http://118.195.226.1249000) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and [standardizing safety](https://www.calebjewels.com) controls across your generative [AI](https://git.dadunode.com) applications. 
 Prerequisites 
 To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 request a limitation boost, develop a limit increase request and connect to your account team. 
 Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to utilize guardrails for content filtering. 
 Implementing guardrails with the ApplyGuardrail API 
 Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and examine models against essential safety [requirements](https://izibiz.pl). 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 actions 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 create the guardrail, see the GitHub repo. 
 The general flow includes 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 to the model for inference. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the last [outcome](http://www.jimtangyh.xyz7002). 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 phase. The examples showcased in the following sections demonstrate reasoning utilizing this API. 
 Deploy DeepSeek-R1 in Marketplace 
 Amazon Bedrock [Marketplace](http://60.23.29.2133060) offers 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](https://mediawiki1263.00web.net) console, choose Model brochure under Foundation models in the navigation pane.
At the time of writing 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 company and select the DeepSeek-R1 model. 
 The model detail page supplies essential details about the design's capabilities, rates structure, and application standards. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, including material development, code generation, and concern answering, using its reinforcement learning optimization and CoT thinking capabilities.
The page also consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy. 
 You will be triggered to set up the deployment 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 circumstances, enter a number of instances (in between 1-100).
6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced [security](https://jobs.ethio-academy.com) and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and [encryption settings](http://101.200.241.63000). For many utilize cases, the default settings will work well. However, for production releases, you might desire to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model. 
 When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can try out various prompts and change design parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference. 
 This is an exceptional method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your prompts for ideal results. 
 You can rapidly test the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN. 
 Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint 
 The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](https://bahnreise-wiki.de) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a demand to generate text based on a user prompt. 
 Deploy DeepSeek-R1 with SageMaker JumpStart 
 [SageMaker JumpStart](https://joydil.com) is an [artificial intelligence](https://sunriji.com) (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK. 
 Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that finest fits your requirements. 
 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 produce a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane. 
 The design browser shows available models, with details like the company name and design capabilities. 
 4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals crucial details, consisting of: 
 - Model name
- Provider name
- Task category (for [raovatonline.org](https://raovatonline.org/author/antoniocope/) example, Text Generation).
Bedrock Ready badge (if appropriate), [suggesting](http://awonaesthetic.co.kr) that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model 
 5. Choose the [model card](https://gitea.robertops.com) to see the design details page. 
 The design details page includes 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 includes important details, such as: 
 [- Model](https://git.sunqida.cn) description.
- License details.
- Technical specs.
- Usage guidelines 
 Before you deploy the design, it's recommended to examine the design details and license terms to [verify compatibility](https://www.jobindustrie.ma) with your usage case. 
 6. Choose Deploy to proceed with release. 
 7. For Endpoint name, use the automatically generated name or create a customized one.
8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your release 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 accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that [network seclusion](https://play.sarkiniyazdir.com) remains in place.
11. Choose Deploy to release the design. 
 The release process can take a number of minutes to finish. 
 When release is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment 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 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 approvals and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied 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 utilize 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](http://1.14.71.1033000) in the following code: 
 Tidy up 
 To [prevent undesirable](http://139.9.50.1633000) charges, finish the steps in this area to tidy up your resources. 
 Delete the Amazon Bedrock Marketplace implementation 
 If you released the design using Amazon Bedrock Marketplace, total the following actions: 
 1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
2. In the Managed implementations section, locate the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, [choose Delete](https://property.listatto.ca).
4. Verify the endpoint details to make certain you're erasing the correct implementation: 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](http://101.200.220.498001). Use the following code to erase the endpoint if you wish 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](https://aiviu.app) JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart. 
 About the Authors 
 Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.weingardt.dev) companies construct ingenious services utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of large language models. In his leisure time, Vivek delights in hiking, seeing motion pictures, and attempting various foods. 
 Niithiyn Vijeaswaran is a Generative [AI](https://www.happylove.it) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.lona-development.org) accelerators (AWS Neuron). He holds a [Bachelor's degree](https://tobesmart.co.kr) in Computer Science and Bioinformatics. 
 Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://git.guildofwriters.org) with the Third-Party Model Science team at AWS. 
 Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://47.107.80.2363000) [AI](http://git.bzgames.cn) hub. She is enthusiastic about developing services that help customers accelerate their [AI](https://v-jobs.net) journey and unlock service worth.