```Python hl_lines="9"
{!../../../docs_src/behind_a_proxy/tutorial004.py!}
```

and then it won't include it in the OpenAPI schema.

## Mounting a sub-application

If you need to mount a sub-application (as described in [Sub Applications - Mounts](sub-applications.md){.internal-link target=_blank}) while also using a proxy with `root_path`, you can do it normally, as you would expect.

    This is because we want to include their *path operations* in the OpenAPI schema and the user interfaces.

    As we cannot just isolate them and "mount" them independently of the rest, the *path operations* are "cloned" (re-created), not included directly.

## Check the automatic API docs

Now, run `uvicorn`, using the module `app.main` and the variable `app`:

    skip_users: Container[str],
    min_count: int = 2,
):
    users = []
    for commenter, count in counter.most_common(50):
        if commenter in skip_users:
            continue
        if count >= min_count:
            author = authors[commenter]
            users.append(
                {
                    "login": commenter,
                    "count": count,

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

### Server Memory

```

Dann wird er nicht in das OpenAPI-Schema aufgenommen.

## Mounten einer Unteranwendung

Wenn Sie gleichzeitig eine Unteranwendung mounten (wie beschrieben in [Unteranwendungen – Mounts](sub-applications.md){.internal-link target=_blank}) und einen Proxy mit `root_path` verwenden wollen, können Sie das normal tun, wie Sie es erwarten würden.

Conversations in the chat systems are also not as easily searchable as in GitHub, so questions and answers might get lost in the conversation. And only the ones in GitHub count to become a [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}, so you will most probably receive more attention in GitHub.

It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.

    """
    update_languages()
    shutil.rmtree(site_path, ignore_errors=True)
    langs = [lang.name for lang in get_lang_paths() if lang.is_dir()]
    cpu_count = os.cpu_count() or 1
    process_pool_size = cpu_count * 4
    typer.echo(f"Using process pool size: {process_pool_size}")
    with Pool(process_pool_size) as p:
        p.map(build_lang, langs)


@app.command()

you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

And then you can set those same memory limits and requirements in your configurations for your container management system (for example in **Kubernetes**). That way it will be able to **replicate the containers** in the **available machines** taking into account the amount of memory needed by them, and the amount available in the machines...