<option value="$PROJECT_DIR$/platforms/core-execution/scoped-persistent-cache" />
            <option value="$PROJECT_DIR$/platforms/core-execution/snapshots" />
            <option value="$PROJECT_DIR$/platforms/core-execution/worker-main" />
            <option value="$PROJECT_DIR$/platforms/core-execution/workers" />

	if err != nil {
		rebalanceLogIf(ctx, fmt.Errorf("invalid workers value err: %v, defaulting to %d", err, len(pool.sets)))
		workerSize = len(pool.sets)
	}

	// Each decom worker needs one List() goroutine/worker
	// add that many extra workers.
	workerSize += len(pool.sets)

	wk, err := workers.New(workerSize)
	if err != nil {
		return err
	}

E se você precisar ter múltiplos workers, você pode simplesmente usar a opção de linha de comando `--workers`.

/// note | Detalhes Técnicos

A imagem Docker foi criada quando o Uvicorn não suportava gerenciar e reiniciar workers mortos, então era necessário usar o Gunicorn com o Uvicorn, o que adicionava bastante complexidade, apenas para que o Gunicorn gerenciasse e reiniciasse os processos workers do Uvicorn.

// newBatchJobPool creates a pool of job manifest workers of specified size
func newBatchJobPool(ctx context.Context, o ObjectLayer, workers int) *BatchJobPool {
	jpool := &BatchJobPool{
		ctx:          ctx,
		objLayer:     o,
		jobCh:        make(chan *BatchJobRequest, 10000),
		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}

* Modèles **SQLAlchemy** (indépendants des extensions Flask, afin qu'ils puissent être utilisés directement avec des *workers* Celery).
* Modèle de démarrages basiques pour les utilisateurs (à modifier et supprimer au besoin).
* Migrations **Alembic**.
* **CORS** (partage des ressources entre origines multiples, ou *Cross Origin Resource Sharing*).
* *Worker* **Celery** pouvant importer et utiliser les modèles et le code du reste du backend.

	}

	workerSize, err := strconv.Atoi(env.Get("_MINIO_BATCH_KEYROTATION_WORKERS", strconv.Itoa(runtime.GOMAXPROCS(0)/2)))
	if err != nil {
		return err
	}

	wk, err := workers.New(workerSize)
	if err != nil {
		// invalid worker size.
		return err
	}

	ctx, cancel := context.WithCancel(ctx)

	results := make(chan itemOrErr[ObjectInfo], 100)

	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
	}

	if err != nil {
		decomLogIf(ctx, fmt.Errorf("invalid workers value err: %v, defaulting to %d", err, len(pool.sets)))
		workerSize = len(pool.sets)
	}

	// Each decom worker needs one List() goroutine/worker
	// add that many extra workers.
	workerSize += len(pool.sets)

	wk, err := workers.New(workerSize)
	if err != nil {
		return err
	}

	var vc *versioning.Versioning

}

func newHealRoutine() *healRoutine {
	workers := runtime.GOMAXPROCS(0) / 2

	if envHealWorkers := env.Get("_MINIO_HEAL_WORKERS", ""); envHealWorkers != "" {
		if numHealers, err := strconv.Atoi(envHealWorkers); err != nil {
			bugLogIf(context.Background(), fmt.Errorf("invalid _MINIO_HEAL_WORKERS value: %w", err))
		} else {
			workers = numHealers
		}
	}

	if workers == 0 {
		workers = 4
	}

因此，在这种情况下，你**不会**希望拥有像 Gunicorn 和 Uvicorn worker一样的进程管理器，或者 Uvicorn 使用自己的 Uvicorn worker。 你可能希望每个容器（但可能有多个容器）只有一个**单独的 Uvicorn 进程**。

在容器内拥有另一个进程管理器（就像使用 Gunicorn 或 Uvicorn 管理 Uvicorn 工作线程一样）只会增加**不必要的复杂性**，而你很可能已经在集群系统中处理这些复杂性了。

### 具有多个进程的容器

当然，在某些**特殊情况**，你可能希望拥有 **一个容器**，其中包含 **Gunicorn 进程管理器**，并在其中启动多个 **Uvicorn worker进程**。