## Gunicorn mit Uvicorn-Workern ausführen

Dann können Sie Gunicorn ausführen mit:

<div class="termy">

```console
$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80

[19499] [INFO] Starting gunicorn 20.1.0
[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
[19511] [INFO] Booting worker with pid: 19511

```console
$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80

[19499] [INFO] Starting gunicorn 20.1.0
[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
[19511] [INFO] Booting worker with pid: 19511
[19513] [INFO] Booting worker with pid: 19513
[19514] [INFO] Booting worker with pid: 19514

  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

```console
$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80

[19499] [INFO] Starting gunicorn 20.1.0
[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
[19511] [INFO] Booting worker with pid: 19511
[19513] [INFO] Booting worker with pid: 19513
[19514] [INFO] Booting worker with pid: 19514

* **Uvicorn** managing **Uvicorn 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**

		workers = workers[:len(workers)-1]
		worker <- expiryOp(nil)
		es.stats.workers.Add(-1)
	}
	// Atomically replace workers.
	es.workers.Store(&workers)
}

// Worker handles 4 types of expiration tasks.
// 1. Expiry of objects, includes regular and transitioned objects
// 2. Expiry of noncurrent versions due to NewerNoncurrentVersions

  server_def.set_task_index(1);

  std::unique_ptr<tensorflow::GrpcServer> worker_server;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  /**
   * This counter prevents an ABA issue where a thread may successfully schedule the worker, the
   * worker runs and exhausts the queue, another thread enqueues a task and fails to schedule the
   * worker, and then the first thread's call to delegate.execute() returns. Without this counter,
   * it would observe the QUEUING state and set it to QUEUED, and the worker would never be
   * scheduled again for future submissions.
   */
  @GuardedBy("queue")

  string serialized_server_def_1 = server_def_1.SerializeAsString();

  // Create two worker tasks.
  server_def_0.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def_0, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());
  server_def_0.set_task_index(2);

        def projectDir = 'C:\\tcagent1\\work\\f63322e10dd6b396\\'...