public static final String UNSAFE_URL = "unsafe-url";
  }

  /**
   * The HTTP <a href="https://www.w3.org/TR/service-workers/#update-algorithm">{@code
   * Service-Worker}</a> header field name.
   *
   * @since 20.0
   */
  public static final String SERVICE_WORKER = "Service-Worker";
  /** The HTTP {@code TE} header field name. */
  public static final String TE = "TE";

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

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因此，在这种情况下，你**不会**希望拥有像 Gunicorn 和 Uvicorn worker一样的进程管理器，或者 Uvicorn 使用自己的 Uvicorn worker。 你可能希望每个容器（但可能有多个容器）只有一个**单独的 Uvicorn 进程**。

在容器内拥有另一个进程管理器（就像使用 Gunicorn 或 Uvicorn 管理 Uvicorn 工作线程一样）只会增加**不必要的复杂性**，而你很可能已经在集群系统中处理这些复杂性了。

### 具有多个进程的容器

当然，在某些**特殊情况**，你可能希望拥有 **一个容器**，其中包含 **Gunicorn 进程管理器**，并在其中启动多个 **Uvicorn worker进程**。

			Description: "manage namespace scanning for usage calculation, lifecycle, healing and more",
		},
		config.HelpKV{
			Key:         config.BatchSubSys,
			Description: "manage batch job workers and wait times",
		},
		config.HelpKV{
			Key:         config.CompressionSubSys,
			Description: "enable server side compression of objects",
		},
		config.HelpKV{

In diesem Fall möchten Sie also **nicht** einen Prozessmanager wie Gunicorn mit Uvicorn-Workern oder Uvicorn mit seinen eigenen Uvicorn-Workern haben. Sie möchten nur einen **einzelnen Uvicorn-Prozess** pro Container haben (wahrscheinlich aber mehrere Container).

	if source.noWait {
		select {
		case globalBackgroundHealRoutine.tasks <- task:
			if serverDebugLog {
				fmt.Printf("Task in the queue: %#v\n", task)
			}
		default:
			// task queue is full, no more workers, we shall move on and heal later.
			return nil
		}
		// Don't wait for result
		return nil
	}

	// respCh must be set to wait for result.
	// We make it size 1, so a result can always be written

  //  Worker0:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14526" } }
  //            job { name: "worker" tasks { key: 0 value: "localhost:14522" } }
  //          } job_name: "worker" protocol: "grpc"
  //

  // Create `worker_1` using single host server def `worker_1_server_def`.
  tensorflow::ServerDef worker_1_server_def =
      CreateSingleHostServerDef(cluster_server_def, 1);

            // all the tasks, then the thread will block waiting for all
            // those subtasks to finish.
            // This ensures the number of running workers is no more than
            // the defined parallism, while making sure the pool will not
            // be exhausted
            //
            return new ThreadPoolExecutor(

Я подготовил для вас Docker-образ, в который включён Gunicorn управляющий процессами (воркерами) Uvicorn, в соответствии с концепциями рассмотренными в предыдущей главе: [Рабочие процессы сервера (воркеры) - Gunicorn совместно с Uvicorn](server-workers.md){.internal-link target=_blank}.

Этот образ может быть полезен для ситуаций описанных тут: [Множество процессов внутри контейнера для особых случаев](#_11).