## Deployment Concepts { #deployment-concepts }

Here you saw how to use multiple **workers** to **parallelize** the execution of the application, take advantage of **multiple cores** in the CPU, and be able to serve **more requests**.

From the list of deployment concepts from above, using workers would mainly help with the **replication** part, and a little bit with the **restarts**, but you still need to take care of the others:

{* ../../docs_src/query_params_str_validations/tutorial002.py hl[9] *}

Note que substituímos o valor padrão de `None` para `Query(default=None)`, o primeiro parâmetro de `Query` serve para o mesmo propósito: definir o valor padrão do parâmetro.

Então:

```Python
q: Union[str, None] = Query(default=None)
```

...Torna o parâmetro opcional, da mesma maneira que:

```Python

Here I'm also highlighting contributions from sponsors.

    server.requireClientAuth()
    val call = client.newCall(Request.Builder().url(server.url("/")).build())
    assertFailsWith<IOException> {
      call.execute()
    }

    // Observed Events are variable
    // JDK 14
    // CallStart, ProxySelectStart, ProxySelectEnd, DnsStart, DnsEnd, ConnectStart, SecureConnectStart,

# Run a Server Manually { #run-a-server-manually }

## Use the `fastapi run` Command { #use-the-fastapi-run-command }

In short, use `fastapi run` to serve your FastAPI application:

<div class="termy">

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

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

# Object Lambda

            "description": "Entorno de producción"
        }
    ],
    "paths": {
            // Más cosas aquí
    }
}
```

/// tip | Consejo

Observa el server auto-generado con un valor `url` de `/api/v1`, tomado del `root_path`.

///

    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

We'll see how they would affect **deployments**.

In the end, the ultimate objective is to be able to **serve your API clients** in a way that is **secure**, to **avoid disruptions**, and to use the **compute resources** (for example remote servers/virtual machines) as efficiently as possible. 🚀

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