type replicationResyncer struct {
	// map of bucket to their resync status
	statusMap      map[string]BucketReplicationResyncStatus
	workerSize     int
	resyncCancelCh chan struct{}
	workerCh       chan struct{}
	sync.RWMutex
}

const (
	replicationDir      = ".replication"
	resyncFileName      = "resync.bin"
	resyncMetaFormat    = 1
	resyncMetaVersionV1 = 1

Insbesondere wenn die Anwendung auf **Kubernetes** läuft, werden Sie wahrscheinlich **keine** Worker verwenden wollen, und stattdessen **einen einzelnen Uvicorn-Prozess pro Container** ausführen wollen, aber ich werde Ihnen später in diesem Kapitel mehr darüber erzählen.

///

## Mehrere Worker { #multiple-workers }

Sie können mehrere Worker mit der `--workers`-Befehlszeilenoption starten:

//// tab | `fastapi`

///

## Múltiples Workers { #multiple-workers }

Puedes iniciar múltiples workers con la opción de línea de comando `--workers`:

//// tab | `fastapi`

Si usas el comando `fastapi`:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>

# 服务器工作进程（Workers） - 使用 Uvicorn 的多工作进程模式 { #server-workers-uvicorn-with-workers }

让我们回顾一下之前的部署概念：

* 安全性 - HTTPS
* 启动时运行
* 重新启动
* **复制（运行的进程数）**
* 内存
* 启动前的先前步骤

到目前为止，在文档中的所有教程中，您可能一直是在运行一个**服务器程序**，例如使用 `fastapi` 命令来启动 Uvicorn，而它默认运行的是**单进程模式**。

部署应用程序时，您可能希望进行一些**进程复制**，以利用**多核** CPU 并能够处理更多请求。

正如您在上一章有关[部署概念](concepts.md)中看到的，您可以使用多种策略。

在本章节中，我将向您展示如何使用 `fastapi` 命令或直接使用 `uvicorn` 命令以**多工作进程模式**运行 **Uvicorn**。

///

## Кілька працівників { #multiple-workers }

Ви можете запустити кілька працівників за допомогою параметра командного рядка `--workers`:

//// tab | `fastapi`

Якщо ви використовуєте команду `fastapi`:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>

  // Create two worker tasks, using single host server defs.
  // A single host server def contains a client and the remote host.
  // Example:
  //
  //  Worker1:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14525" } }
  //            job { name: "worker" tasks { key: 1 value: "localhost:14523" } }
  //          } job_name: "worker" task_index: 1 protocol: "grpc"
  //

    <!-- These packages are duplicated in workers and daemon-server-worker, don't require a package-info.java in each place -->
    <suppress checks="JavadocPackage"
              files=".*[/\\]platforms[/\\]core-execution[/\\]daemon-server-worker[/\\]src[/\\]main[/\\]java[/\\]org[/\\]gradle[/\\]workers[/\\][^/\\]+"/>

    <!-- These packages are duplicated in code-quality and code-quality-workers, don't require a package-info.java in each place -->

    "crossVersionTests": false
  },
  {
    "name": "scala-compiler-worker",
    "path": "platforms/jvm/scala-compiler-worker",
    "unitTests": false,
    "functionalTests": false,
    "crossVersionTests": false
  },
  {
    "name": "scaladoc-worker",
    "path": "platforms/jvm/scaladoc-worker",
    "unitTests": false,
    "functionalTests": false,
    "crossVersionTests": false

  int port = tensorflow::testing::PickUnusedPortOrDie();
  // 3. Replace worker1 with a new worker in server_def_0 and server_def_1.
  ReplaceTaskInServerDef(&server_def_0, /*task_index=*/1, "localhost", port);
  ReplaceTaskInServerDef(&server_def_1, /*task_index=*/1, "localhost", port);
  // 4. Start a new task to replace worker1.
  server_def_0.set_task_index(1);
  worker_server1.release();

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

# (1)!
CMD ["fastapi", "run", "app/main.py", "--port", "80", "--workers", "4"]
```

1. Burada worker sayısını 4 yapmak için `--workers` komut satırı seçeneğini kullanıyoruz.

Bunun mantıklı olabileceği birkaç örnek:

#### Basit Bir Uygulama { #a-simple-app }