<span style="background-color:#007166"><font color="#D3D7CF"> tip </font></span>  Running in development mode, for production use:
             <b>fastapi run</b>

             Logs:

     <span style="background-color:#007166"><font color="#D3D7CF"> INFO </font></span>  Will watch for changes in these directories:

      <span style="background-color:#007166"><font color="#D3D7CF"> tip </font></span>  Running in development mode, for production use:
             <b>fastapi run</b>

             Logs:

     <span style="background-color:#007166"><font color="#D3D7CF"> INFO </font></span>  Will watch for changes in these directories:

      <span style="background-color:#007166"><font color="#D3D7CF"> tip </font></span>  Running in development mode, for production use:
             <b>fastapi run</b>

             Logs:

     <span style="background-color:#007166"><font color="#D3D7CF"> INFO </font></span>  Will watch for changes in these directories:

<div class="termy">

```console
$ pip install websockets

---> 100%
```

</div>

## Cliente WebSockets { #websockets-client }

### En producción { #in-production }

En tu sistema de producción, probablemente tengas un frontend creado con un framework moderno como React, Vue.js o Angular.

<div class="termy">

```console
$ pip install websockets

---> 100%
```

</div>

## WebSockets-Client { #websockets-client }

### In Produktion { #in-production }

In Ihrem Produktionssystem haben Sie wahrscheinlich ein Frontend, das mit einem modernen Framework wie React, Vue.js oder Angular erstellt wurde.

 <span style="background-color:#C4A000"><font color="#2E3436">│                                                     │</font></span>
 <span style="background-color:#C4A000"><font color="#2E3436">│  Running in development mode, for production use:   │</font></span>
 <span style="background-color:#C4A000"><font color="#2E3436">│                                                     │</font></span>

[build-system]
requires = ["pdm-backend"]
build-backend = "pdm.backend"

[project]
name = "fastapi"
dynamic = ["version"]
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE"]
requires-python = ">=3.9"
authors = [
    { name = "Sebastián Ramírez", email = "******@****.***" },
]
classifiers = [

* SQLite
* Oracle
* Microsoft SQL Server, etc.

In this example, we'll use **SQLite**, because it uses a single file and Python has integrated support. So, you can copy this example and run it as is.

Later, for your production application, you might want to use a database server like **PostgreSQL**.

/// tip

But you can also provide other alternative `servers`, for example if you want *the same* docs UI to interact with both a staging and a production environment.

If you pass a custom list of `servers` and there's a `root_path` (because your API lives behind a proxy), **FastAPI** will insert a "server" with this `root_path` at the beginning of the list.

For example:

[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)