/// tip | Dica

    }

    private void addUtilityTasks(TaskContainer tasks, GradleDocumentationExtension extension) {
        tasks.register("serveDocs", ServeDocs.class, task -> {
            task.setDescription("Runs a local webserver to serve generated documentation.");
            task.setGroup("documentation");

            int webserverPort = 8000;
            task.getJavaLauncher().set(

Lets start the lambda handler.

```
python lambda_handler.py
 * Serving Flask app 'webhook'
 * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5000
Press CTRL+C to quit
```

## Start MinIO with Lambda target

## Install the Server Program { #install-the-server-program }

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

But you can also install an ASGI server manually.

Make sure you create a [virtual environment](../virtual-environments.md){.internal-link target=_blank}, activate it, and then you can install the server application.

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static
    ├── redoc.standalone.js
    ├── swagger-ui-bundle.js
    └── swagger-ui.css
```

### Die statischen Dateien bereitstellen { #serve-the-static-files }

* Importieren Sie `StaticFiles`.
* „Mounten“ Sie eine `StaticFiles()`-Instanz in einem bestimmten Pfad.

{* ../../docs_src/custom_docs_ui/tutorial002_py39.py hl[7,11] *}

func (m *Manager) AddToMux(router *mux.Router, authReq func(r *http.Request) error) {
	router.Handle(m.routePath, m.Handler(authReq))
}

// Handler returns a handler that can be used to serve grid requests.
// This should be connected on RoutePath to the main server.
func (m *Manager) Handler(authReq func(r *http.Request) error) http.HandlerFunc {
	return func(w http.ResponseWriter, req *http.Request) {
		defer func() {
			if debugPrint {

read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for
internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any

E esses modelos estão compartilhando muitos dos dados e duplicando nomes e tipos de atributos.

Nós poderíamos fazer melhor.

Podemos declarar um modelo `UserBase` que serve como base para nossos outros modelos. E então podemos fazer subclasses desse modelo que herdam seus atributos (declarações de tipo, validação, etc.).

Toda conversão de dados, validação, documentação, etc. ainda funcionará normalmente.

### In a Remote Server { #in-a-remote-server }

When you set up a remote server (a cloud server, a virtual machine, etc.) the simplest thing you can do is use `fastapi run` (which uses Uvicorn) or something  similar, manually, the same way you do when developing locally.

And it will work and will be useful **during development**.

But if your connection to the server is lost, the **running process** will probably die.

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static
    ├── redoc.standalone.js
    ├── swagger-ui-bundle.js
    └── swagger-ui.css
```

### Предоставьте доступ к статическим файлам { #serve-the-static-files }

* Импортируйте `StaticFiles`.
* Смонтируйте экземпляр `StaticFiles()` в определённый путь.

{* ../../docs_src/custom_docs_ui/tutorial002_py39.py hl[7,11] *}