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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/em/docs/deployment/docker.md

    🍵 ⚙️ 📦, ⚒ 🈸 🏃 🔛 🕴 & ⏮️ ⏏ 💪 ⚠ & ⚠. ✋️ 🕐❔ **👷 ⏮️ 📦** 🌅 💼 👈 🛠️ 🔌 🔢. 👶
    
    ## 🧬 - 🔢 🛠️
    
    🚥 👆 ✔️ <abbr title="A group of machines that are configured to be connected and work together in some way.">🌑</abbr> 🎰 ⏮️ **☁**, ☁ 🐝 📳, 🖖, ⚖️ ➕1️⃣ 🎏 🏗 ⚙️ 🛠️ 📎 📦 🔛 💗 🎰, ⤴️ 👆 🔜 🎲 💚 **🍵 🧬** **🌑 🎚** ↩️ ⚙️ **🛠️ 👨‍💼** (💖 🐁 ⏮️ 👨‍🏭) 🔠 📦.
    
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  3. docs/de/docs/deployment/concepts.md

    ### Serverspeicher
    
    Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.
    
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  4. 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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  5. docs/fr/docs/deployment/manually.md

    Lorsqu'on se réfère à la machine distante, il est courant de l'appeler **serveur**, mais aussi **machine**, **VM** (machine virtuelle), **nœud**. Tout cela fait référence à un type de machine distante, exécutant  Linux, en règle générale, sur laquelle vous exécutez des programmes.
    
    
    ## Installer le programme serveur
    
    Vous pouvez installer un serveur compatible ASGI avec :
    
    === "Uvicorn"
    
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  6. docs/en/docs/advanced/events.md

    ## Use Case
    
    Let's start with an example **use case** and then see how to solve it with this.
    
    Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖
    
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  7. docs/en/docs/deployment/index.md

    ## What Does Deployment Mean
    
    To **deploy** an application means to perform the necessary steps to make it **available to the users**.
    
    For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.
    
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  8. 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
    Others
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  9. 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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  10. 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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