When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.

As you saw in the previous chapter about [Deployment Concepts](concepts.md){.internal-link target=_blank}, there are multiple strategies you can use.

Então o caixa diz que "seus hambúrgueres estão prontos" colocando seu número no balcão, mas você não corre que nem um maluco imediatamente quando o número exibido é o seu. Você sabe que ninguém irá roubar seus hambúrgueres porquê você tem o número de chamada, e os outros tem os números deles.

# Primeros pasos

Un archivo muy simple de FastAPI podría verse así:

```Python
{!../../../docs_src/first_steps/tutorial001.py!}
```

Copia eso a un archivo `main.py`.

Corre el servidor en vivo:

<div class="termy">

```console
$ uvicorn main:app --reload

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled...

    `float16` for acceleration on Volta and Turing Tensor Cores. This feature
    can be enabled by wrapping an optimizer class with
    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
    forces the selection of deterministic cuDNN convolution and max-pooling

		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]
		uaAppend(" CPU ", fmt.Sprintf("(total_cpus:%d, total_cores:%d; vendor:%s; family:%s; model:%s; stepping:%d; model_name:%s)",
			len(cpuMap), len(coreMap), cpu.VendorID, cpu.Family, cpu.Model, cpu.Stepping, cpu.ModelName))
	}
	uaAppend(")", "")

### Multiple Processes - Workers

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.

</a>
</p>

---

**Dokumentáció**: <a href="https://fastapi.tiangolo.com" target="_blank">https://fastapi.tiangolo.com</a>

**Forrás kód**: <a href="https://github.com/tiangolo/fastapi" target="_blank">https://github.com/tiangolo/fastapi</a>

---

**Nota**:

Si no lo sabes, revisa la sección _"¿Con prisa?"_ sobre <a href="https://fastapi.tiangolo.com/es/async/#con-prisa" target="_blank">`async` y `await` en la documentación</a>.

</details>

### Córrelo

Corre el servidor con:

<div class="termy">

```console
$ uvicorn main:app --reload

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [28720]

* **Robuste** : Obtenez un code prêt pour la production. Avec une documentation interactive automatique.