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  1. 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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  2. docs/pt/docs/advanced/events.md

    ## Caso de uso
    
    Vamos iniciar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.
    
    Vamos imaginar que você tem alguns **modelos de _machine learning_** que deseja usar para lidar com as requisições. 🤖
    
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  3. docs/pt/docs/async.md

    * **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.
    
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  4. docs/fr/docs/async.md

    * L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.
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  5. docs/en/docs/deployment/https.md

        * The contents are **encrypted**, even though they are being sent with the **HTTP protocol**.
    
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  6. docs/en/docs/how-to/nosql-databases-couchbase.md

    In the code, a `Bucket` represents the main entrypoint of communication with the database.
    
    This utility function will:
    
    * Connect to a **Couchbase** cluster (that might be a single machine).
        * Set defaults for timeouts.
    * Authenticate in the cluster.
    * Get a `Bucket` instance.
        * Set defaults for timeouts.
    * Return it.
    
    ```Python hl_lines="12-21"
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  7. docs/en/docs/release-notes.md

    * 📝 Add external link: Building simple E-Commerce with NuxtJS and FastAPI. PR [#3271](https://github.com/tiangolo/fastapi/pull/3271) by [@ShahriyarR](https://github.com/ShahriyarR).
    * 📝 Add external link: Serve a machine learning model using Sklearn, FastAPI and Docker. PR [#2974](https://github.com/tiangolo/fastapi/pull/2974) by [@rodrigo-arenas](https://github.com/rodrigo-arenas).
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  8. 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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  9. 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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  10. 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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