From 52e65bf26152814e1ff5e2270b29b190092a8119 Mon Sep 17 00:00:00 2001 From: Albertha Blesing Date: Wed, 5 Mar 2025 11:45:43 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 144 +++++++++--------- 1 file changed, 72 insertions(+), 72 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 3ec2e32..08c77c7 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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker . With this launch, you can now [deploy DeepSeek](http://120.26.79.179) [AI](https://skillnaukri.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your [generative](https://www.roednetwork.com) [AI](http://219.150.88.234:33000) concepts on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs also.
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Today, we are delighted 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 [AI](http://116.63.157.3:8418)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, [kigalilife.co.rw](https://kigalilife.co.rw/author/gerardedkin/) and properly scale your generative [AI](http://1.15.187.67) [concepts](http://globalnursingcareers.com) on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models as well.

Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://git.scraperwall.com) that utilizes support finding out to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement learning (RL) action, which was used to refine the model's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both [relevance](https://prazskypantheon.cz) and clearness. In addition, DeepSeek-R1 uses a [chain-of-thought](http://git.qwerin.cz) (CoT) method, implying it's geared up to break down complicated questions and factor through them in a detailed manner. This guided reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its [wide-ranging abilities](https://git.opskube.com) DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, logical thinking and data [analysis tasks](https://lazerjobs.in).
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DeepSeek-R1 utilizes a [Mixture](http://hychinafood.edenstore.co.kr) of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for [effective](https://gitlab.ccc.org.co) reasoning by routing inquiries to the most pertinent expert "clusters." This technique allows the design to specialize in different issue domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:RoxanneRawson) 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against crucial security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://www.jobassembly.com) applications.
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.elferos.keenetic.pro) that uses reinforcement learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing 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 procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down complex inquiries and factor through them in a detailed manner. This assisted thinking process allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a [flexible text-generation](http://49.234.213.44) design that can be integrated into various workflows such as representatives, rational reasoning and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows [activation](https://skillsinternational.co.in) of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most pertinent specialist "clusters." This approach allows the design to focus on various issue domains while maintaining general [efficiency](http://122.51.230.863000). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs supplying](http://124.222.48.2033000) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more effective architectures based on [popular](http://www.amrstudio.cn33000) open models like Qwen (1.5 B, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:FVLJoanna1590) 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest [deploying](http://git.szmicode.com3000) this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and [evaluate designs](https://www.eadvisor.it) against key safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](https://chosenflex.com). You can produce multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, [improving](https://www.emploitelesurveillance.fr) user experiences and standardizing safety controls throughout your generative [AI](http://grainfather.asia) applications.

Prerequisites
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To release 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](http://compass-framework.com3000) 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 instance in the AWS Region you are releasing. To ask for a limit boost, develop a limit boost request and reach out to your account team.
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Because you will be deploying this design 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 instructions, see Set up approvals to use guardrails for material filtering.
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To [inspect](https://chefandcookjobs.com) if you have quotas for P5e, open the [Service Quotas](http://git.hnits360.com) console and under AWS Services, select Amazon SageMaker, and validate 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 deploying. To ask for a limit boost, develop a limit increase demand and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and examine designs against key safety criteria. You can execute safety measures for the DeepSeek-R1 model utilizing the [Amazon Bedrock](https://gitee.mmote.ru) ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model responses [deployed](http://git.cnibsp.com) 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 develop the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or [output phase](https://twwrando.com). The examples showcased in the following areas show reasoning utilizing this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](https://bd.cane-recruitment.com) Marketplace
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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, complete the following actions:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the [navigation](https://m1bar.com) pane. -At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't [support Converse](https://spreek.me) APIs and other Amazon Bedrock tooling. -2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
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The design detail page offers important details about the model's abilities, pricing structure, and application guidelines. You can discover detailed usage instructions, [including sample](https://genzkenya.co.ke) [API calls](http://8.136.42.2418088) and code bits for integration. The model supports different text generation jobs, including content creation, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking abilities. -The page also includes deployment choices and licensing details to help you get started with DeepSeek-R1 in your applications. -3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the release details for [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:AleishaP83) DeepSeek-R1. The model ID will be pre-populated. -4. For [Endpoint](https://intunz.com) name, get in an endpoint name (in between 1-50 alphanumeric characters). -5. For Number of circumstances, enter a [variety](http://121.5.25.2463000) of [circumstances](https://sahabatcasn.com) (between 1-100). -6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. -Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private 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](http://115.159.107.1173000) releases, you may desire to review these settings to align with your organization's security and [compliance requirements](http://caxapok.space). +
Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful material, and examine models against essential safety criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic flow involves the following steps: First, the system gets 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 design for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is [intervened](http://git.anitago.com3000) by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or [output stage](http://www.xn--9m1b66aq3oyvjvmate.com). The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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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. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The design detail page supplies vital details about the model's capabilities, prices structure, and application guidelines. You can find detailed usage instructions, consisting of [sample API](https://www.jungmile.com) calls and code bits for combination. The model supports different text generation tasks, including material development, code generation, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FerminBrannon00) and question answering, utilizing its reinforcement discovering optimization and CoT thinking capabilities. +The page also includes release alternatives and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of circumstances (in between 1-100). +6. For example type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and [infrastructure](http://112.48.22.1963000) settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For many use cases, the default settings will work well. However, for [production](https://slovenskymedved.sk) implementations, you may desire to evaluate these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start using the model.
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When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. -8. Choose Open in playground to access an interactive user interface where you can try out various triggers and adjust design criteria like temperature and maximum length. +
When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and adjust model specifications like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for reasoning.
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This is an outstanding way to check out the design's reasoning and text generation capabilities before [incorporating](https://gitlab.iue.fh-kiel.de) it into your applications. The play area supplies instant feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimal results.
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You can rapidly evaluate the design in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the [deployed](https://sujansadhu.com) DeepSeek-R1 endpoint
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The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a demand to [produce text](https://express-work.com) based on a user prompt.
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This is an excellent method to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the design responds to different inputs and letting you fine-tune your triggers for optimum outcomes.
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You can quickly test the model in the play ground through the UI. However, to [conjure](http://anggrek.aplikasi.web.id3000) up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a [released](https://corerecruitingroup.com) DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the [Amazon Bedrock](http://152.136.187.229) console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends a demand to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and [prebuilt](http://www.pelletkorea.net) ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient approaches: using the [user-friendly SageMaker](https://addismarket.net) [JumpStart](https://mysazle.com) UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the technique that finest suits your requirements.
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the method that best matches your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
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Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:

1. On the SageMaker console, choose Studio in the navigation pane. -2. First-time users will be triggered to develop a domain. +2. First-time users will be prompted to produce a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. -Each design card shows key details, including:
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The model browser shows available designs, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows key details, consisting of:

- Model name - Provider name -- Task category (for instance, Text Generation). -Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon [Bedrock](https://kommunalwiki.boell.de) APIs to invoke the model
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5. Choose the model card to see the model [details](https://www.talentsure.co.uk) page.
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The design details page consists of the following details:
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- The design name and supplier details. -Deploy button to release the model. +- Task classification (for instance, Text Generation). +[Bedrock Ready](http://124.220.187.1423000) badge (if suitable), [indicating](https://gitea.chenbingyuan.com) that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the design details page.
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The design details page includes the following details:
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- The model name and supplier details. +Deploy button to deploy the design. About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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The About tab consists of essential details, such as:

- Model description. - License details. - Technical specs. - Usage guidelines
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Before you deploy the model, it's advised to examine the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically generated name or create a custom one. -8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge). -9. For Initial circumstances count, get in the variety of circumstances (default: 1). -Selecting suitable circumstances types and counts is [crucial](https://thestylehitch.com) for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DianaRosenthal) Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. -10. Review all setups for accuracy. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. -11. Choose Deploy to [release](https://vacancies.co.zm) the design.
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The deployment process can take several minutes to finish.
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When release is complete, your endpoint status will change to InService. At this moment, the design is all set to accept inference requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going 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 demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://inspirationlift.com) the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. -2. In the Managed implementations section, find the endpoint you desire to erase. -3. Select the endpoint, and on the Actions menu, pick Delete. -4. Verify the [endpoint details](https://cn.wejob.info) to make certain you're erasing the correct implementation: 1. Endpoint name. +
Before you release the design, it's suggested to evaluate the [model details](https://ifin.gov.so) and license terms to [confirm compatibility](https://jobspaddy.com) with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the immediately generated name or produce a customized one. +8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge). +9. For [Initial](https://humlog.social) circumstances count, enter the number of circumstances (default: 1). +Selecting suitable [circumstances](https://calciojob.com) types and counts is vital for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The release process can take a number of minutes to complete.
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When implementation is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and range from [SageMaker Studio](https://gitea.eggtech.net).
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You can run additional requests against the predictor:
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[Implement guardrails](http://dkjournal.co.kr) and run reasoning with your SageMaker JumpStart predictor
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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 shown in the following code:
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Clean up
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To prevent undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. +2. In the Managed implementations section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [select Delete](https://git.guildofwriters.org). +4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. [Endpoint](https://skillsinternational.co.in) name. 2. Model name. 3. Endpoint status

Delete the SageMaker JumpStart predictor
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The [SageMaker](http://47.101.187.298081) JumpStart design you deployed will sustain expenses 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.
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The SageMaker JumpStart design 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
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In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart 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](https://cagit.cacode.net) models, Amazon SageMaker [JumpStart Foundation](https://napvibe.com) Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](http://kiwoori.com) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, [Amazon SageMaker](https://incomash.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.

About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://forum.infinity-code.com) companies develop ingenious solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of large language models. In his leisure time, Vivek delights in hiking, viewing motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://mzceo.net) Specialist Solutions Architect with the Third-Party Model [Science](https://body-positivity.org) group at AWS. His location of focus is AWS [AI](https://lab.chocomart.kz) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://rm.runfox.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://anggrek.aplikasi.web.id3000) [AI](http://damoa8949.com) center. She is enthusiastic about developing services that help customers accelerate their [AI](https://inktal.com) journey and unlock organization worth.
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://techport.io) companies construct innovative services utilizing [AWS services](https://goalsshow.com) and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the [reasoning performance](https://ifairy.world) of big language designs. In his leisure time, Vivek enjoys hiking, viewing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.zjzhcn.com) Specialist Solutions Architect with the Third-Party Model [Science](http://www.mizmiz.de) group at AWS. His area of focus is AWS [AI](https://edurich.lk) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [technology](http://82.146.58.193) and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://www.athleticzoneforum.com) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://stroijobs.com) leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://dev.shopraves.com) center. She is enthusiastic about constructing services that help consumers accelerate their [AI](http://damoa8949.com) journey and unlock business value.
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