You can use simple tools like `htop` to see the CPU and RAM used in your server or the amount used by each process. Or you can use more complex monitoring tools, which may be distributed across servers, etc.

## Recap

You have been reading here some of the main concepts that you would probably need to keep in mind when deciding how to deploy your application:

* Security - HTTPS
* Running on startup
* Restarts

    {!> ../../../docs_src/body_updates/tutorial002_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="35"
    {!> ../../../docs_src/body_updates/tutorial002.py!}
    ```

### Partial updates recap

In summary, to apply partial updates you would:

* (Optionally) use `PATCH` instead of `PUT`.
* Retrieve the stored data.
* Put that data in a Pydantic model.

    {!> ../../../docs_src/extra_models/tutorial005_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="1  8"
    {!> ../../../docs_src/extra_models/tutorial005.py!}
    ```

## Recap

Use multiple Pydantic models and inherit freely for each case.

## Recap

You can use **Gunicorn** (or also Uvicorn) as a process manager with Uvicorn workers to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

Password: `secret2`

And try to use the operation `GET` with the path `/users/me`.

You will get an "Inactive user" error, like:

```JSON
{
  "detail": "Inactive user"
}
```

## Recap

You now have the tools to implement a complete security system based on `username` and `password` for your API.

    And the `dict` you receive as `weights` will actually have `int` keys and `float` values.

## Recap

With **FastAPI** you have the maximum flexibility provided by Pydantic models, while keeping your code simple, short and elegant.

But with all the benefits:

* Editor support (completion everywhere!)

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="11  18"
    {!> ../../../docs_src/request_files/tutorial003.py!}
    ```

## Recap

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="11"
    {!> ../../../docs_src/path_params_numeric_validations/tutorial006.py!}
    ```

## Recap

With `Query`, `Path` (and others you haven't seen yet) you can declare metadata and string validations in the same ways as with [Query Parameters and String Validations](query-params-str-validations.md){.internal-link target=_blank}.

## Recap

You can use Pydantic Settings to handle the settings or configurations for your application, with all the power of Pydantic models.

* By using a dependency you can simplify testing.
* You can use `.env` files with it.

* The alternative documentation will also reflect the new query parameter and body:

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Recap

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.