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docs/en/docs/deployment/concepts.md
* A bash script that runs the previous steps and then starts your application * You would still need a way to start/restart *that* bash script, detect errors, etc. !!! tip I'll give you more concrete examples for doing this with containers in a future chapter: [FastAPI in Containers - Docker](docker.md){.internal-link target=_blank}. ## Resource Utilization
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docs/en/docs/release-notes.md
### Migration
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docs/en/docs/features.md
You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request. No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`. ### Short
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README.md
* Automatic and clear errors when the data is invalid. * Validation even for deeply nested JSON objects. * <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from: * JSON. * Path parameters. * Query parameters. * Cookies. * Headers. * Forms. * Files.
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docs/en/docs/advanced/settings.md
## Pydantic `Settings` Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>. ### Install `pydantic-settings`
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docs/en/docs/index.md
* Automatic and clear errors when the data is invalid. * Validation even for deeply nested JSON objects. * <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from: * JSON. * Path parameters. * Query parameters. * Cookies. * Headers. * Forms. * Files.
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docs/en/docs/deployment/docker.md
### Multiple Containers If you have **multiple containers**, probably each one running a **single process** (for example, in a **Kubernetes** cluster), then you would probably want to have a **separate container** doing the work of the **previous steps** in a single container, running a single process, **before** running the replicated worker containers. !!! info
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