```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

		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

default   details-v1-698d88b-krdw7       10.244.2.55 ambient-worker2 None     HBONE
default   httpbin-7447985f87-t8hv7       10.244.1.40 ambient-worker  None     TCP
default   productpage-v1-675fc69cf-kkrm2 10.244.2.56 ambient-worker2 None     HBONE
default   ratings-v1-6484c4d9bb-8xc2r    10.244.2.57 ambient-worker2 None     HBONE
default   reviews-v1-5b5d6494f4-c7z5w    10.244.1.41 ambient-worker  None     HBONE

  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\\'...

const (
	ReplicationWorkersWait = "replication_workers_wait"
	KeyRotationWorkersWait = "keyrotation_workers_wait"
	ExpirationWorkersWait  = "expiration_workers_wait"

	EnvReplicationWorkersWait   = "MINIO_BATCH_REPLICATION_WORKERS_WAIT"
	EnvKeyRotationWorkersWait   = "MINIO_BATCH_KEYROTATION_WORKERS_WAIT"
	EnvKeyExpirationWorkersWait = "MINIO_BATCH_EXPIRATION_WORKERS_WAIT"
)

var configMu sync.RWMutex

      "serviceAccount": "httpbin",
      "workloadName": "httpbin",
      "workloadType": "deployment",
      "canonicalName": "httpbin",
      "canonicalRevision": "v1",
      "node": "ambient-worker",
      "status": "Healthy",
      "clusterId": "Kubernetes"
    },
    "/10.244.2.8": {
      "workloadIps": [
        "10.244.2.8"
      ],
      "protocol": "TCP",

In those cases, you can use the **official Docker image** that includes **Gunicorn** as a process manager running multiple **Uvicorn worker processes**, and some default settings to adjust the number of workers based on the current CPU cores automatically. I'll tell you more about it below in [Official Docker Image with Gunicorn - Uvicorn](#official-docker-image-with-gunicorn-uvicorn).