};

  tensorflow::mutex execution_mutex_;
  ExecutionState execution_state_ TF_GUARDED_BY(execution_mutex_) =
      ExecutionState::kIdle;
  // Tells the worker thread that there is new work.
  tensorflow::condition_variable start_execute_;
  // The worker thread notifies that work has finished.
  tensorflow::condition_variable finished_execute_;
  // Notifies a StartExecute that the previous Join has finished.

```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.

         * predictable what work might be done. (e.g., close a file and flush buffers to disk). To
         * protect ourselves from this, we park ourselves and tell our interrupter that we did so.
         */
        if (state == PARKED || compareAndSet(state, PARKED)) {
          // Interrupting Cow Says:
          //  ______
          // < Park >

          # (see actions/stale documentation for the behavior)
          days-before-issue-stale: -1
          stale-issue-label: stale
          stale-issue-message: >
            **BUG!** This issue should not be marked stale by the "stale" workflow.
            Please report it to @gradle/bt-support team
          days-before-issue-close: -1
          close-issue-message: >

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**。

      int64_t init_timeout_in_ms, int retries,
      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created
  // for remote workers. Currently this only works for resetting context.
  // TODO(b/289445025): Consider removing this when we find a proper fix.
  virtual absl::Status InitializeLocalOnlyContext(const ServerDef& server_def,

        decides which workers to read from. If `"ANY"`, TF workers read from any
        tf.data service workers. If `"LOCAL"`, TF workers will only read from
        local in-processs tf.data service workers. `"AUTO"` works well for most
        cases, while users can specify other targets. For example, `"LOCAL"`
        would help avoid RPCs and data copy if every TF worker colocates with a

	if (checkOld > 0 && len(p.workers) != checkOld) || n == len(p.workers) || n < 1 {
		// Either already satisfied or worker count changed while we waited for the lock.
		return
	}
	for len(p.workers) < n {
		input := make(chan ReplicationWorkerOperation, 10000)
		p.workers = append(p.workers, input)

		go p.AddWorker(input, &p.activeWorkers)
	}
	for len(p.workers) > n {
		worker := p.workers[len(p.workers)-1]

    )

    failureConditions {
        // JavaExecDebugIntegrationTest.debug session fails without debugger might cause JVM crash
        // Some soak tests produce OOM exceptions
        // There are also random worker crashes for some tests.
        // We have test-retry to handle the crash in tests
        javaCrash = false
    }
})

private fun determineFlakyTestStrategy(stage: Stage): String {

private val TYPE_SUBTYPE = Regex("$TOKEN/$TOKEN")
private val PARAMETER = Regex(";\\s*(?:$TOKEN=(?:$TOKEN|$QUOTED))?")

/**
 * Returns a media type for this string.
 *
 * @throws IllegalArgumentException if this is not a well-formed media type.
 */
internal fun String.commonToMediaType(): MediaType {
  val typeSubtype: MatchResult =
    TYPE_SUBTYPE.matchAtPolyfill(this, 0)
      ?: throw IllegalArgumentException("No subtype found for: \"$this\"")