From 0b782cf24f330f06942b205a98d678f4acccc7de Mon Sep 17 00:00:00 2001 From: Adeline Harbison Date: Tue, 8 Apr 2025 18:54:11 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 146 +++++++++--------- 1 file changed, 73 insertions(+), 73 deletions(-) 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 d5bb308..8d92f5e 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 [thrilled](https://app.galaxiesunion.com) 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](https://jobpile.uk)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your [generative](http://otyjob.com) [AI](https://gitlab.vog.media) ideas on AWS.
-
In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models too.
+
Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://igazszavak.info) concepts on AWS.
+
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs as well.

Overview of DeepSeek-R1
-
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://www.etymologiewebsite.nl) that uses support learning to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying feature is its support learning (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately boosting both significance and clearness. In addition, [garagesale.es](https://www.garagesale.es/author/christiefit/) DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down complex inquiries and factor through them in a detailed manner. This assisted reasoning procedure enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible thinking and data analysis tasks.
-
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://git.freesoftwareservers.com) in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing questions to the most pertinent specialist "clusters." This method permits the design to concentrate on various issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 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 efficient designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
-
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest [releasing](https://open-gitlab.going-link.com) this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine models against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://www.philthejob.nl) only the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](http://111.229.9.19:3000) applications.
+
DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://git.dev-store.ru) that utilizes support finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its [reinforcement knowing](http://xn--289an1ad92ak6p.com) (RL) step, which was used to fine-tune the [model's reactions](https://ejamii.com) beyond the [basic pre-training](http://unired.zz.com.ve) and [tweak process](http://cjma.kr). By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate inquiries and factor through them in a detailed manner. This assisted thinking process permits the design to produce more accurate, transparent, and detailed answers. This design [integrates RL-based](http://www.thegrainfather.co.nz) fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, rational thinking and data interpretation tasks.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing queries to the most appropriate expert "clusters." This approach permits the model to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for [inference](https://gitlab.companywe.co.kr). In this post, we will [utilize](https://germanjob.eu) an ml.p5e.48 [xlarge instance](http://release.rupeetracker.in) to [release](https://salesupprocess.it) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to [simulate](https://salesupprocess.it) the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.
+
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine models against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can [develop](https://heatwave.app) several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://bristol.rackons.com) applications.

Prerequisites
-
To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 releasing. To ask for a limit boost, create a limitation boost demand and connect to your account team.
-
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to [utilize Amazon](https://impactosocial.unicef.es) Bedrock Guardrails. For guidelines, see Establish consents to utilize guardrails for content [filtering](http://gitlab.nsenz.com).
+
To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit increase, develop a limit boost request and reach out to your account team.
+
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:FernandoKinross) Gain Access To Management (IAM) consents to use [Amazon Bedrock](https://git.gz.internal.jumaiyx.cn) Guardrails. For directions, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API
-
Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful content, and examine models against key safety criteria. You can [implement precaution](https://49.12.72.229) for the DeepSeek-R1 model using the [Amazon Bedrock](http://git.iloomo.com) ApplyGuardrail API. This [enables](https://zenabifair.com) you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://talentup.asia). 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 basic 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 to the model for reasoning. After getting the design's output, another guardrail check is used. If the [output passes](https://allcallpro.com) this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show [inference utilizing](http://219.150.88.23433000) this API.
+
Amazon Bedrock Guardrails permits you to introduce safeguards, prevent damaging material, and evaluate models against key safety criteria. You can implement safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions 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 basic flow involves the following actions: 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 design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last result. However, if either the input or output is [stepped](https://gitea.malloc.hackerbots.net) in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show reasoning 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, complete the following actions:
+
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, select Model catalog under Foundation designs in the navigation pane. -At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock [tooling](http://music.afrixis.com). -2. Filter for DeepSeek as a [provider](http://expertsay.blog) and select the DeepSeek-R1 design.
-
The design detail page offers important details about the model's capabilities, prices structure, and application standards. You can find detailed use directions, including sample API calls and code bits for combination. The model supports different text generation jobs, including material production, code generation, and concern answering, using its support finding out optimization and CoT thinking abilities. -The page likewise consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications. -3. To start using DeepSeek-R1, pick Deploy.
-
You will be prompted to configure the release 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 Variety of instances, go into a variety of circumstances (in between 1-100). -6. For example type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. -Optionally, you can set up sophisticated security and facilities settings, consisting of 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 desire to evaluate these settings to line up with your [organization's security](https://www.sexmasters.xyz) and compliance requirements. -7. Choose Deploy to begin using the model.
-
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. -8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change model parameters like temperature and optimum length. -When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, material for reasoning.
-
This is an exceptional way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the design responds to various inputs and letting you tweak your prompts for [ideal outcomes](https://rca.co.id).
-
You can quickly evaluate the design in the play ground through the UI. However, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LynwoodBolling) to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
-
Run inference using [guardrails](http://47.94.142.23510230) with the [released](http://8.136.197.2303000) DeepSeek-R1 endpoint
-
The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](http://123.207.206.1358048). You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, [configures inference](http://8.136.199.333000) specifications, and sends out a demand to create text based on a user timely.
+At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
+
The model detail page supplies vital details about the design's abilities, rates structure, and application standards. You can discover detailed use instructions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation tasks, [including](https://golz.tv) content production, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:AlexandraRaney9) code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. +The page also consists of implementation choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose 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, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and [facilities](https://lepostecanada.com) settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might want to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
+
When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change model criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for inference.
+
This is an outstanding way to explore the design's thinking and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, helping you understand [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:MyronNowell73) how the model responds to different inputs and letting you fine-tune your prompts for optimal outcomes.
+
You can quickly check the model in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
+
Run inference using guardrails with the [released](http://94.224.160.697990) DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a released 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 produce the guardrail, see the GitHub repo. After you have created the guardrail, [utilize](http://git.jaxc.cn) the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a request to [generate text](https://rrallytv.com) based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart
-
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 uses 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the technique that best matches your needs.
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, [built-in](http://www5f.biglobe.ne.jp) algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.
+
[Deploying](http://64.227.136.170) DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker [Python SDK](http://h2kelim.com). Let's explore both methods to help you choose the approach that finest matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI
-
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
-
1. On the SageMaker console, pick Studio in the navigation pane. -2. First-time users will be triggered to produce a domain. -3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
-
The design web browser shows available models, with details like the service provider name and model abilities.
-
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. -Each model card shows key details, consisting of:
+
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
+
The model [web browser](https://www.valeriarp.com.tr) displays available models, with details like the company name and model capabilities.
+
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, including:

- Model name -- [Provider](http://globalchristianjobs.com) name -- Task category (for instance, Text Generation). -Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
-
5. Choose the model card to view the model details page.
-
The model details page includes the following details:
-
- The design name and service provider details. +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
+
5. Choose the model card to view the design details page.
+
The design details page consists of 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 consists of important details, such as:
+
The About tab includes crucial details, such as:

- Model description. - License details. -- Technical specs. +- Technical specifications. - Usage guidelines
-
Before you release the model, it's recommended to evaluate the design details and license terms to confirm compatibility with your usage case.
-
6. Choose Deploy to continue with implementation.
-
7. For Endpoint name, utilize the automatically generated name or create a customized one. -8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge). -9. For Initial circumstances count, get in the number of instances (default: 1). -Selecting proper circumstances types and counts is vital for cost and performance optimization. Monitor your implementation to change these settings as needed.Under [Inference](https://blazblue.wiki) type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. -10. Review all setups for [accuracy](https://dev-members.writeappreviews.com). For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. -11. Choose Deploy to deploy the model.
-
The implementation process can take numerous minutes to finish.
-
When release is total, your endpoint status will alter to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can invoke the design using a SageMaker runtime customer and integrate it with your applications.
-
Deploy DeepSeek-R1 using the [SageMaker Python](http://8.217.113.413000) SDK
-
To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
+
Before you release the model, it's suggested to review the design details and license terms to validate compatibility with your usage case.
+
6. Choose Deploy to proceed with deployment.
+
7. For Endpoint name, use the instantly produced name or develop a custom one. +8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, [gratisafhalen.be](https://gratisafhalen.be/author/bret0919589/) go into the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [Real-time reasoning](http://rm.runfox.com) is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. [Choose Deploy](https://southernsoulatlfm.com) to release the model.
+
The deployment process can take several minutes to finish.
+
When release is total, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can [conjure](https://maxmeet.ru) up the design using a SageMaker runtime client and integrate it with your applications.
+
Deploy DeepSeek-R1 using 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 consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.

You can run extra 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 . You can [develop](https://www.empireofember.com) a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
+
Similar to Amazon Bedrock, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1384182) you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:

Clean up
-
To avoid undesirable charges, finish the actions in this area to tidy up your resources.
-
Delete the Amazon Bedrock Marketplace deployment
-
If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:
-
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [pick Marketplace](https://jobs.quvah.com) releases. -2. In the Managed implementations area, locate the endpoint you wish to erase. -3. Select the endpoint, and on the Actions menu, pick Delete. -4. Verify the endpoint details to make certain you're erasing the proper release: 1. Endpoint name. +
To avoid undesirable charges, complete the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace implementation
+
If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:ZoraCintron60) select Marketplace releases. +2. In the Managed implementations area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the correct deployment: 1. Endpoint name. 2. Model name. -3. [Endpoint](https://aaalabourhire.com) status
+3. Endpoint status

Delete the SageMaker JumpStart predictor
-
The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
+
The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion
-
In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](http://121.37.166.03000) models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
+
In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and [garagesale.es](https://www.garagesale.es/author/odessapanos/) SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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](https://wiki.team-glisto.com) companies develop ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of large language models. In his free time, Vivek delights in treking, [watching](https://code.miraclezhb.com) films, and trying different foods.
-
Niithiyn Vijeaswaran is a Generative [AI](https://cruzazulfansclub.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://jobshut.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
-
Jonathan Evans is a [Specialist](https://talentmatch.somatik.io) Solutions Architect dealing with generative [AI](http://47.92.218.215:3000) with the Third-Party Model Science group at AWS.
-
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://publiccharters.org) hub. She is passionate about building solutions that help consumers accelerate their [AI](https://picturegram.app) journey and unlock service worth.
\ No newline at end of file +
Vivek Gangasani is a Lead Specialist Solutions [Architect](http://64.227.136.170) for Inference at AWS. He helps emerging generative [AI](https://jobstoapply.com) business construct innovative options using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his downtime, Vivek enjoys hiking, watching movies, and attempting different [cuisines](http://www.jedge.top3000).
+
Niithiyn Vijeaswaran is a Generative [AI](http://sdongha.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://bebebi.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://careers.indianschoolsoman.com) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon [SageMaker](https://lepostecanada.com) JumpStart, SageMaker's artificial intelligence and generative [AI](https://movie.nanuly.kr) center. She is enthusiastic about building options that help customers accelerate their [AI](https://code.52abp.com) journey and unlock organization value.
\ No newline at end of file