///

## Vários trabalhadores { #multiple-workers }

Você pode iniciar vários trabalhadores com a opção de linha de comando `--workers`:

//// tab | `fastapi`

Se você usar o comando `fastapi`:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>

///

## 여러 워커 { #multiple-workers }

`--workers` 명령어 옵션으로 여러 워커를 시작할 수 있습니다:

//// tab | `fastapi`

`fastapi` 명령어를 사용한다면:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>

# Server Workers - ワーカー付きUvicorn { #server-workers-uvicorn-with-workers }

前回のデプロイメントのコンセプトを振り返ってみましょう：

* セキュリティ - HTTPS
* 起動時の実行
* 再起動
* **レプリケーション（実行中のプロセス数）**
* メモリ
* 開始前の事前ステップ

ここまでのドキュメントのチュートリアルでは、おそらく `fastapi` コマンドなど（Uvicornを実行するもの）を使って、**単一のプロセス**として動作する**サーバープログラム**を実行してきたはずです。

アプリケーションをデプロイする際には、**複数のコア**を利用し、そしてより多くのリクエストを処理できるようにするために、プロセスの**レプリケーション**を持つことを望むでしょう。

import org.gradle.api.tasks.PathSensitive;
import org.gradle.api.tasks.PathSensitivity;
import org.gradle.api.tasks.TaskAction;
import org.gradle.process.ExecOperations;
import org.gradle.workers.WorkAction;
import org.gradle.workers.WorkParameters;
import org.gradle.workers.WorkerExecutor;

import javax.inject.Inject;
import java.io.IOException;
import java.util.Arrays;

public class DockerBuildTask extends DefaultTask {

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...

	if (checkOld > 0 && len(p.workers) != checkOld) || n == len(p.workers) || n < 1 {
		// Either already satisfied or worker count changed while we waited for the lock.
		return
	}
	for len(p.workers) < n {
		input := make(chan ReplicationWorkerOperation, 10000)
		p.workers = append(p.workers, input)

		go p.AddWorker(input, &p.activeWorkers)
	}
	for len(p.workers) > n {
		worker := p.workers[len(p.workers)-1]

import org.gradle.api.tasks.PathSensitive;
import org.gradle.api.tasks.PathSensitivity;
import org.gradle.api.tasks.SkipWhenEmpty;
import org.gradle.api.tasks.TaskAction;
import org.gradle.workers.WorkAction;
import org.gradle.workers.WorkParameters;
import org.gradle.workers.WorkerExecutor;

import javax.inject.Inject;
import java.io.File;
import java.io.IOException;
import java.io.UncheckedIOException;
import java.nio.file.FileSystem;

TF_CAPI_EXPORT extern TFE_ContextDevicePlacementPolicy
TFE_ContextGetDevicePlacementPolicy(TFE_Context* ctx);

// A tensorflow.ServerDef specifies remote workers (in addition to the current
// workers name). Operations created in this context can then be executed on
// any of these remote workers by setting an appropriate device.
//
// If the following is set, all servers identified by the
// ServerDef must be up when the context is created.

	oi, ok := oiCache[fmt.Sprintf("%s-%s", toDel.ObjectName, toDel.VersionID)]
	return oi, ok
}

func batchObjsForDelete(ctx context.Context, r *BatchJobExpire, ri *batchJobInfo, job BatchJobRequest, api ObjectLayer, wk *workers.Workers, expireCh <-chan []expireObjInfo) {
	vc, _ := globalBucketVersioningSys.Get(r.Bucket)
	retryAttempts := job.Expire.Retry.Attempts
	if retryAttempts <= 0 {
		retryAttempts = batchExpireJobDefaultRetries
	}

| `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                                     |