func (m *rateMeasurement) getExpMovingAvgBytesPerSecond() float64 {
	m.lock.Lock()
	defer m.lock.Unlock()
	return m.expMovingAvg
}

// ActiveWorkerStat is stat for active replication workers
type ActiveWorkerStat struct {
	Curr int     `json:"curr"`
	Avg  float32 `json:"avg"`
	Max  int     `json:"max"`
	hist metrics.Histogram
}

func newActiveWorkerStat(r metrics.Registry) *ActiveWorkerStat {

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.

  "durations" : [ {
    "testProject" : "nativeMonolithic",
    "linux" : 1380
  }, {
    "testProject" : "nativeMonolithicOverlapping",
    "linux" : 1275
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.nativeplatform.RealWorldNativePluginPerformanceTest.build with 12 parallel workers",
  "durations" : [ {
    "testProject" : "nativeMonolithic",

그런 경우에는 `--workers` 커맨드 라인 옵션을 사용해 실행할 워커 수를 설정할 수 있습니다:

```{ .dockerfile .annotate }
FROM python:3.14

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

# (1)!
CMD ["fastapi", "run", "app/main.py", "--port", "80", "--workers", "4"]
```

func (opts Config) Clone() (int, time.Duration, string) {
	configMutex.RLock()
	defer configMutex.RUnlock()
	return opts.IOCount, opts.Sleep, opts.Bitrot
}

// GetWorkers returns the number of workers, -1 is none configured
func (opts Config) GetWorkers() int {
	configMutex.RLock()
	defer configMutex.RUnlock()
	return opts.DriveWorkers
}

// Update updates opts with nopts

                toStringable(() -> Boolean.toString(VersionProperties.isElasticsearchSnapshot()))
            );
        });

        project.getPluginManager().apply("nebula.info-broker");
        project.getPluginManager().apply("nebula.info-basic");
        project.getPluginManager().apply("nebula.info-java");
        project.getPluginManager().apply("nebula.info-jar");
    }

// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed
// on both the main function side and the component function side, an error will

			Optional:    true,
			Type:        "on|off",
		},
		config.HelpKV{
			Key:         target.AmqpDurable,
			Description: "persist queue across broker restarts when set to 'on', default is 'off'",
			Optional:    true,
			Type:        "on|off",
		},
		config.HelpKV{
			Key:         target.AmqpNoWait,

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

| `minio_node_replication_average_active_workers`         | Average number of active replication workers                                                             |
| `minio_node_replication_max_active_workers`             | Maximum number of active replication workers seen since server start                                     |