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  1. docs/en/docs/deployment/docker.md

    Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).
    
    This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).
    
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  2. docs/en/docs/deployment/concepts.md

    ### Server Memory
    
    For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.
    
    And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨
    
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  3. docs/en/data/external_links.yml

    Articles:
      English:
      - author: Kurtis Pykes - NVIDIA
        link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
        title: Building a Machine Learning Microservice with FastAPI
      - author: Ravgeet Dhillon - Twilio
        link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
        title: Booking Appointments with Twilio, Notion, and FastAPI
      - author: Abhinav Tripathi - Microsoft Blogs
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  4. docs/tr/docs/project-generation.md

    ## Full Stack FastAPI MongoDB
    
    ... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉
    
    ## Machine Learning modelleri, spaCy ve FastAPI
    
    GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>
    
    ### Machine Learning modelleri, spaCy ve FastAPI - Features
    
    * **spaCy** NER model entegrasyonu.
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  5. docs/en/docs/async.md

    * **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
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  6. docs/zh/docs/deployment/manually.md

    ## 服务器主机和服务器程序
    
    关于名称,有一个小细节需要记住。 💡
    
    “**服务器**”一词通常用于指远程/云计算机(物理机或虚拟机)以及在该计算机上运行的程序(例如 Uvicorn)。
    
    请记住,当您一般读到“服务器”这个名词时,它可能指的是这两者之一。
    
    当提到远程主机时,通常将其称为**服务器**,但也称为**机器**(machine)、**VM**(虚拟机)、**节点**。 这些都是指某种类型的远程计算机,通常运行 Linux,您可以在其中运行程序。
    
    
    ## 安装服务器程序
    
    您可以使用以下命令安装 ASGI 兼容服务器:
    
    === "Uvicorn"
    
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  7. docs/fr/docs/tutorial/first-steps.md

    ```hl_lines="4"
    INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
    ```
    
    Cette ligne montre l'URL par laquelle l'app est actuellement accessible, sur votre machine locale.
    
    ### Allez voir le résultat
    
    Ouvrez votre navigateur à l'adresse <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.
    
    Vous obtiendrez cette réponse JSON :
    
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  8. docs/tr/docs/index.md

    ## Görüşler
    
    "_[...] Bugünlerde **FastAPI**'ı çok fazla kullanıyorum. [...] Aslında bunu ekibimin **Microsoft'taki Machine Learning servislerinin** tamamında kullanmayı planlıyorum. Bunlardan bazıları **Windows**'un ana ürünlerine ve **Office** ürünlerine entegre ediliyor._"
    
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  9. docs/ko/docs/deployment/docker.md

    컨테이너를 사용하지 않고서는, 어플리케이션을 구동하고 재시작하는 것이 매우 번거롭고 어려울 수 있습니다. 하지만 **컨테이너를 사용한다면** 대부분의 경우에 이런 기능은 기본적으로 포함되어 있습니다. ✨
    
    ## 복제 - 프로세스 개수
    
    만약 여러분이 **쿠버네티스**와 머신 <abbr title="A group of machines that are configured to be connected and work together in some way.">클러스터</abbr>, 도커 스왐 모드, 노마드, 또는 다른 여러 머신 위에 분산 컨테이너를 관리하는 복잡한 시스템을 다루고 있다면, 여러분은 각 컨테이너에서 (워커와 함께 사용하는 Gunicorn 같은) **프로세스 매니저** 대신 **클러스터 레벨**에서 **복제를 다루**고 싶을 것입니다.
    
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  10. docs/de/docs/deployment/docker.md

    Linux-Container werden mit demselben Linux-Kernel des Hosts (Maschine, virtuellen Maschine, Cloud-Servers, usw.) ausgeführt. Das bedeutet einfach, dass sie sehr leichtgewichtig sind (im Vergleich zu vollständigen virtuellen Maschinen, die ein gesamtes Betriebssystem emulieren).
    
    Auf diese Weise verbrauchen Container **wenig Ressourcen**, eine Menge vergleichbar mit der direkten Ausführung der Prozesse (eine virtuelle Maschine würde viel mehr verbrauchen).
    
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