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

// 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

          "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                                     |

      "coverage" : {
        "per_day" : [ "linux" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.nativeplatform.RealWorldNativePluginPerformanceTest.build with 0 parallel workers",
    "groups" : [ {
      "testProject" : "nativeMonolithic",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    }, {
      "testProject" : "nativeMonolithicOverlapping",

load, number of objects in the object store as well as storage speed. In addition, any bandwidth limits set via `mc admin bucket remote add` could also contribute to replication speed. The number of workers used for replication defaults to 100. Based on network bandwidth and system load, the number of workers used in replication can be configured using `mc admin config set alias api` to set the `replication_workers`. The prometheus metrics exposed by MinIO can be used to plan resource allocation and bandwidth...