// 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"
  //

  // Replace worker1 using a new worker, and update the contexts.
  // Read the variable using `ctx_1`. This read should succeed.
  //
  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var");

  // 2. Wait for `var` to be created and initialized on the worker.
  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.

            // forked phase
            if (md.getExecuteLifecycle() != null && !md.getExecuteLifecycle().isEmpty()) {
                buff.append('[');
                buff.append(md.getExecuteLifecycle());
                buff.append(']');
            }
            buff.append(md.getExecutePhase());
        } else {
            // forked goal
            buff.append(':');

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

			lm.ref = 1
			lm.isWriteLock = true
			locked = true
		}
	} else {
		if !lm.isWriteLock {
			lm.ref++
			locked = true
		}
	}

	return locked
}

const (
	lockRetryInterval = 50 * time.Millisecond
)

// lockLoop will acquire either a read or a write lock
//
// The call will block until the lock is granted using a built-in
// timing randomized back-off algorithm to try again until successful

// open file descriptor.
package lock

import (
	"errors"
	"os"
	"sync"
)

// ErrAlreadyLocked is returned if the underlying fd is already locked.
var ErrAlreadyLocked = errors.New("file already locked")

// RLockedFile represents a read locked file, implements a special
// closer which only closes the associated *os.File when the ref count.
// has reached zero, i.e when all the readers have given up their locks.

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();

	}
}

func TestSimpleWriteLockTimedOut(t *testing.T) {
	locked := testSimpleWriteLock(t, time.Second)

	expected := false
	if locked != expected {
		t.Errorf("TestSimpleWriteLockTimedOut(): \nexpected %#v\ngot      %#v", expected, locked)
	}
}

func testDualWriteLock(t *testing.T, duration time.Duration) (locked bool) {
	ctx := context.Background()

Here are some possible combinations and strategies:

* **Uvicorn** with `--workers`
    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**.
* **Kubernetes** and other distributed **container systems**