In particular, when running on **Kubernetes** you will probably **not** want to use workers and instead run **a single Uvicorn process per container**, but I'll tell you about it later in that chapter.

///

## Multiple Workers

You can start multiple workers with the `--workers` command line option:

//// tab | `fastapi`

If you use the `fastapi` command:

<div class="termy">

///

## Vários trabalhadores

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
$ <pre> <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:single">main.py</u>
<font color="#3465A4">INFO    </font> Using path <font color="#3465A4">main.py</font>

		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

		// If the channel reaches above half capacity
		// we spawn more workers. The workers spawned
		// from this main worker routine will exit
		// once the channel drops below half capacity
		// and when it's been at least 30 seconds since
		// we launched a new worker.
		if mainWorker && len(h.logCh) > cap(h.logCh)/2 {
			nWorkers := h.workers.Load()
			if nWorkers < h.maxWorkers {
				if time.Since(h.lastStarted).Milliseconds() > 10 {

	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]

And if you need to have multiple workers, you can simply use the `--workers` command line option.

/// note | Technical Details

The Docker image was created when Uvicorn didn't support managing and restarting dead workers, so it was needed to use Gunicorn with Uvicorn, which added quite some complexity, just to have Gunicorn manage and restart the Uvicorn worker processes.

					"problems-api",
					"plugins-java-base",
					"build-cache"
				]
			},
			{
				"parallelizationMethod":{
					"name":"TestDistribution"
				},
				"subprojects":[
					"workers",
					"wrapper-main",
					"testing-base-infrastructure",
					"messaging",
					"war",
					"instrumentation-agent-services",
					"build-profile",
					"resources-gcs",
					"ear",

	current     []jstream.KVS   // current block of results to be returned
	recordsRead int             // number of records read in current slice
	input       chan *queueItem // input for workers
	queue       chan *queueItem // output from workers in order
	err         error           // global error state, only touched by Reader.Read
	bufferPool  sync.Pool       // pool of []byte objects for input

        val gradleStep = CompileAll(buildModel, buildModel.stages[0]).steps.getGradleStep(GRADLE_RUNNER_STEP_NAME)
        assertEquals(
            listOf(
                "-Dorg.gradle.workers.max=%maxParallelForks%",
                "-PmaxParallelForks=%maxParallelForks%",
                "-Dorg.gradle.internal.plugins.portal.url.override=%gradle.plugins.portal.url%",
                "-s",

Here are some possible combinations and strategies:

* **Uvicorn** with `--workers`
    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**.
* **Kubernetes** and other distributed **container systems**