* mount (verb): montar
* statement (as in code statement): statement (do not translate to "declaración" or "sentencia")
* worker process: worker process (do not translate to "proceso trabajador" or "proceso de trabajo")
* worker processes: worker processes (do not translate to "procesos trabajadores" or "procesos de trabajo")
* worker: worker (do not translate to "trabajador")
* load balancer: load balancer (do not translate to "balanceador de carga")

	}
	// allow overriding this value as well..
	if v := globalHealConfig.GetWorkers(); v > 0 {
		numHealers = uint64(v)
	}

	healingLogEvent(ctx, "Healing drive '%s' - use %d parallel workers.", tracker.disk.String(), numHealers)

	jt, _ := workers.New(int(numHealers))

	healEntryDone := func(name string) healEntryResult {
		return healEntryResult{
			entryDone: true,
			name:      name,
		}
	}

Hier sind einige mögliche Kombinationen und Strategien:

* **Gunicorn**, welches **Uvicorn-Worker** managt
    * Gunicorn wäre der **Prozessmanager**, der die **IP** und den **Port** überwacht, die Replikation würde durch **mehrere Uvicorn-Workerprozesse** erfolgen
* **Uvicorn**, welches **Uvicorn-Worker** managt

namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "tpuserver:0"
    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Запуск с Trio

Далее запустите Hypercorn с опцией `--worker-class` и аргументом `trio`:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

Hypercorn, в свою очередь, запустит ваше приложение использующее Trio.

<div class="termy">

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### 🏃 ⏮️ 🎻

⤴️ 👆 💪 🚶‍♀️ 📋 ⏸ 🎛 `--worker-class` ⏮️ 💲 `trio`:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

&amp; 👈 🔜 ▶️ Hypercorn ⏮️ 👆 📱 ⚙️ 🎻 👩‍💻.

🔜 👆 💪 ⚙️ 🎻 🔘 👆 📱. ⚖️ 👍, 👆 💪 ⚙️ AnyIO, 🚧 👆 📟 🔗 ⏮️ 👯‍♂️ 🎻 &amp; ✳. 👶

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Exécuter avec Trio

Ensuite, vous pouvez passer l'option de ligne de commande `--worker-class` avec la valeur `trio` :

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

Et cela démarrera Hypercorn avec votre application en utilisant Trio comme backend.

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Mit Trio ausführen

Dann können Sie die Befehlszeilenoption `--worker-class` mit dem Wert `trio` übergeben:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

Und das startet Hypercorn mit Ihrer Anwendung und verwendet Trio als Backend.

    <img src="https://github.com/fastapi/fastapi/actions/workflows/test.yml/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/fastapi/fastapi" target="_blank">
    <img src="https://coverage-badge.samuelcolvin.workers.dev/fastapi/fastapi.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">

func (m *rateMeasurement) getExpMovingAvgBytesPerSecond() float64 {
	m.lock.Lock()
	defer m.lock.Unlock()
	return m.expMovingAvg
}

// ActiveWorkerStat is stat for active replication workers
type ActiveWorkerStat struct {
	Curr int     `json:"curr"`
	Avg  float32 `json:"avg"`
	Max  int     `json:"max"`
	hist metrics.Histogram
}

func newActiveWorkerStat(r metrics.Registry) *ActiveWorkerStat {