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docs/en/docs/deployment/manually.md
## Server Machine and Server Program { #server-machine-and-server-program } There's a small detail about names to keep in mind. đź’ˇ The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Sat Oct 11 17:48:49 UTC 2025 - 6.9K bytes - Viewed (0) -
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 : //// tab | Uvicorn
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docs/en/docs/advanced/events.md
## Use Case { #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. 🤖Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Wed Dec 17 20:41:43 UTC 2025 - 7.9K bytes - Viewed (0) -
docs/pt/docs/advanced/events.md
## Caso de uso { #use-case } Vamos começar 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. 🤖Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Wed Dec 17 20:41:43 UTC 2025 - 8.8K bytes - Viewed (0) -
docs/es/docs/advanced/events.md
## Caso de Uso { #use-case } Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto. Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Wed Dec 17 20:41:43 UTC 2025 - 8.5K bytes - Viewed (0) -
docs/es/docs/async.md
Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).
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docs/en/docs/deployment/index.md
## What Does Deployment Mean { #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.Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Mon Nov 17 19:33:53 UTC 2025 - 1.5K bytes - Viewed (0) -
docs/en/docs/fastapi-cli.md
## `fastapi run` { #fastapi-run } Executing `fastapi run` starts FastAPI in production mode by default.Registered: Sun Dec 28 07:19:09 UTC 2025 - Last Modified: Sat Oct 11 17:48:49 UTC 2025 - 4.8K bytes - Viewed (0) -
docs/pt/llm-prompt.md
* I/O (as in "input and output"): I/O (do not translate to "E/S") * JSON Schema: JSON Schema * library: biblioteca * lifespan: lifespan (do not translate to "vida Ăştil") * list (as in Python list): list * Machine Learning: Aprendizado de Máquina * media type: media type (do not translate to "tipo de mĂdia") * non-Annotated: non-Annotated (do not translate non-Annotated when it comes after a Python version.e.g., “Python 3.10+ non-Annotated”)
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docs/es/llm-prompt.md
* API key: API key (do not translate to "clave API") * 100% test coverage: cobertura de tests del 100% * back and forth: de un lado a otro * I/O (as in "input and output"): I/O (do not translate to "E/S") * Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático") * Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo") * callback hell: callback hell (do not translate to "infierno de callbacks")
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