diff --git a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md index 4739808..8c52ad4 100644 --- a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -1,93 +1,93 @@ -
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://carepositive.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](http://gitlab.pakgon.com) ideas on AWS.
-
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs too.
+
Today, we are thrilled to reveal that R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:BrianneDupree41) Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://mooel.co.kr)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://twwrando.com) [concepts](http://116.62.118.242) on AWS.
+
In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs as well.

Overview of DeepSeek-R1
-
DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://gitlab.lycoops.be) that utilizes reinforcement finding out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement learning (RL) action, which was used to fine-tune the design's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated inquiries and reason through them in a [detailed](http://www.isexsex.com) way. This assisted thinking [process](https://community.cathome.pet) allows the design to produce more accurate, transparent, and detailed responses. This model integrates [RL-based fine-tuning](http://111.230.115.1083000) with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, rational reasoning and information [interpretation jobs](https://bestremotejobs.net).
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DeepSeek-R1 utilizes a Mix of [Experts](http://git.eyesee8.com) (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, making it possible for effective inference by routing queries to the most appropriate professional "clusters." This approach enables the design to concentrate on various issue domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. 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 providing 1128 GB of GPU memory.
-
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective 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 efficient models to mimic the behavior and thinking patterns of the bigger 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 advise releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and assess designs against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://afacericrestine.ro) applications.
+
DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://candays.com) that utilizes support finding out to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) step, which was used to fine-tune the model's responses beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate inquiries and reason through them in a detailed manner. This directed thinking process allows the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based [fine-tuning](https://gitlab.ineum.ru) with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually [recorded](https://www.workinternational-df.com) the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, logical reasoning and data analysis tasks.
+
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, enabling effective reasoning by routing questions to the most [relevant professional](https://cvmira.com) "clusters." This approach enables the design to specialize in various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on 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 sized, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and examine designs against essential safety criteria. At the time of [writing](https://gitea.uchung.com) this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](https://www.codple.com). You can develop multiple guardrails [tailored](https://workmate.club) to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://my-estro.it) applications.

Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, 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 instance in the AWS Region you are releasing. To ask for a limitation boost, create a limitation boost request and reach out to your account group.
-
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
-
Implementing guardrails with the ApplyGuardrail API
-
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging content, and evaluate models against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
-
The general flow includes the following steps: First, the system gets an input for the design. 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 applied. If the output passes this last check, it's returned as the 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 occurred at the input or output stage. The examples showcased in the following areas show reasoning using this API.
+
To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for 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 circumstances in the AWS Region you are releasing. To request a limit boost, create a limit boost demand and connect to your account team.
+
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) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for content filtering.
+
Implementing guardrails with the [ApplyGuardrail](http://222.85.191.975000) API
+
Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and evaluate designs against key [security requirements](https://www.remotejobz.de). You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The basic circulation includes the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for [reasoning](https://video-sharing.senhosts.com). After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is [stepped](http://tv.houseslands.com) in by the guardrail, a [message](https://lr-mediconsult.de) is returned showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas [demonstrate reasoning](https://20.112.29.181) using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
-
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
-
1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. -At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't 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 provides vital details about the model's abilities, prices structure, and execution standards. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports various text generation jobs, consisting of material development, code generation, and concern answering, using its reinforcement discovering optimization and CoT reasoning capabilities. -The page likewise includes release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. -3. To start utilizing DeepSeek-R1, pick 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 circumstances, go into a variety of instances (in between 1-100). -6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a [GPU-based circumstances](http://mtmnetwork.co.kr) type like ml.p5e.48 xlarge is suggested. -Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, [service role](http://git.estoneinfo.com) permissions, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may desire to [evaluate](https://www.jpaik.com) these settings to align with your company's security and compliance requirements. -7. Choose Deploy to begin utilizing the model.
-
When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. -8. Choose Open in playground to access an interactive interface where you can explore different prompts and adjust model parameters like temperature level and maximum length. -When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For example, material for inference.
-
This is an outstanding way to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play area provides instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for [optimal outcomes](http://steriossimplant.com).
-
You can rapidly test the design in the play area through the UI. However, to [conjure](https://www.lightchen.info) up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
-
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
-
The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends a request to based upon a user timely.
+
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
+
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 use the [InvokeModel API](https://git.bubbleioa.top) to invoke the design. It doesn't [support Converse](https://www.ubom.com) APIs and other [Amazon Bedrock](https://tiktokbeans.com) tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
+
The model detail page offers necessary details about the model's capabilities, rates structure, and execution standards. You can discover [detailed](https://www.passadforbundet.se) use guidelines, including sample API calls and code bits for combination. The model supports various text generation jobs, consisting of material development, code generation, and question answering, using its support finding out optimization and CoT thinking abilities. +The page likewise consists of release choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
+
You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/britney83x24) Number of circumstances, go into a number of instances (in between 1-100). +6. For example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and [yewiki.org](https://www.yewiki.org/User:JonathonCorrea2) encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might want to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
+
When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can try out different triggers and change model criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for reasoning.
+
This is an excellent way to check out the model'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 numerous inputs and letting you tweak your triggers for ideal outcomes.
+
You can [rapidly](http://gitea.smartscf.cn8000) check the design in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to [implement guardrails](https://kronfeldgit.org). The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a demand to [produce text](https://www.outletrelogios.com.br) based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart
-
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can [release](https://teba.timbaktuu.com) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
-
[Deploying](http://www.amrstudio.cn33000) DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out [programmatically](http://101.200.241.63000) through the SageMaker Python SDK. Let's check out both approaches to help you pick the technique that best matches your requirements.
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of 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](https://rsh-recruitment.nl) DeepSeek-R1 design through SageMaker JumpStart uses 2 [convenient](https://www.scikey.ai) techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the approach that best suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane. -2. First-time users will be triggered to produce a domain. -3. On the [SageMaker Studio](http://45.55.138.823000) console, pick JumpStart in the navigation pane.
-
The model browser displays available designs, with details like the provider name and design capabilities.
-
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. -Each design card reveals key details, including:
+2. [First-time](https://club.at.world) users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
+
The design browser displays available models, with details like the service provider name and design abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows key details, including:

- Model name -[- Provider](http://112.74.93.6622234) name -- Task category (for example, Text Generation). -Bedrock Ready badge (if suitable), [suggesting](https://www.womplaz.com) that this model can be signed up with Amazon Bedrock, [permitting](https://gitea.itskp-odense.dk) you to utilize Amazon Bedrock APIs to conjure up the model
-
5. Choose the model card to view the model details page.
+- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](http://www.chinajobbox.com) APIs to conjure up the model
+
5. Choose the model card to view the design details page.

The model details page includes the following details:
-
- The model name and provider details. -Deploy button to deploy the design. +
- The model name and supplier details. +Deploy button to deploy the model. About and Notebooks tabs with detailed details
-
The About tab consists of essential details, such as:
+
The About tab consists of important details, such as:

- Model description. - License details. -[- Technical](http://www.dahengsi.com30002) [requirements](https://sahabatcasn.com). +- Technical requirements. - Usage guidelines
-
Before you deploy the design, it's suggested to review the model details and license terms to [confirm compatibility](https://git.daoyoucloud.com) with your usage case.
-
6. Choose Deploy to continue with deployment.
-
7. For Endpoint name, use the immediately generated name or create a custom one. -8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge). -9. For Initial instance count, go into the variety of instances (default: 1). -Selecting proper circumstances types and counts is [crucial](https://pioneercampus.ac.in) for [expense](https://heatwave.app) and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. -10. Review all setups for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. -11. [Choose Deploy](https://jvptube.net) to release the model.
-
The implementation procedure can take a number of minutes to complete.
-
When release is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning requests through the [endpoint](https://cv4job.benella.in). You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your [applications](https://app.galaxiesunion.com).
-
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
-
To begin with DeepSeek-R1 utilizing 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 demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
+
Before you release the model, it's recommended to examine the design details and license terms to confirm compatibility with your use case.
+
6. Choose Deploy to proceed with release.
+
7. For Endpoint name, utilize the instantly produced name or develop a custom one. +8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is crucial for [expense](https://git.wo.ai) and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for [accuracy](http://www.vmeste-so-vsemi.ru). For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
+
The implementation process can take numerous minutes to finish.
+
When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and [integrate](https://gitea.shoulin.net) it with your [applications](http://110.90.118.1293000).
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require 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 utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.

You can run additional demands 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise utilize 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 prevent undesirable charges, finish the actions in this section to tidy up your [resources](https://careers.ebas.co.ke).
+
To [prevent unwanted](https://medatube.ru) charges, complete the steps in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace release
-
If you released the model using Amazon Bedrock Marketplace, total the following actions:
+
If you [deployed](https://heyplacego.com) the design utilizing Amazon Bedrock Marketplace, complete the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. -2. In the Managed releases area, locate the [endpoint](http://111.2.21.14133001) you want to delete. -3. Select the endpoint, and on the Actions menu, [select Delete](https://mobishorts.com). -4. Verify the endpoint details to make certain you're erasing the correct release: 1. [Endpoint](http://www.amrstudio.cn33000) name. +2. In the Managed implementations area, find the endpoint you wish to delete. +3. Select the endpoint, and on the [Actions](https://followingbook.com) menu, choose Delete. +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 model you [deployed](http://ufiy.com) will sustain costs 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.
+
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](https://vydiio.com) in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
+
In this post, we checked out how you can access and release the DeepSeek-R1 model using 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 models, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://185.5.54.226) JumpStart Foundation Models, [oeclub.org](https://oeclub.org/index.php/User:Stacy0894161994) Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.

About the Authors
-
Vivek Gangasani is a Lead [Specialist Solutions](https://tiktack.socialkhaleel.com) Architect for [Inference](https://1.214.207.4410333) at AWS. He helps emerging generative [AI](https://mediawiki.hcah.in) business build ingenious options utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of large language designs. In his spare time, Vivek takes pleasure in hiking, watching motion pictures, and trying different cuisines.
-
Niithiyn Vijeaswaran is a Generative [AI](https://ttemployment.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://dcmt.co.kr) 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://redmonde.es) with the Third-Party Model Science team at AWS.
-
Banu Nagasundaram leads item, engineering, and tactical collaborations for [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:NoemiStrack042) Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.drawlfest.com) center. She is passionate about constructing options that assist customers accelerate their [AI](http://bc.zycoo.com:3000) journey and unlock company worth.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://www.refermee.com) with the Third-Party Model Science team at AWS.
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