## Testing locally with Traefik

You can easily run the experiment locally with a stripped path prefix using <a href="https://docs.traefik.io/" class="external-link" target="_blank">Traefik</a>.

<a href="https://github.com/containous/traefik/releases" class="external-link" target="_blank">Download Traefik</a>, it's a single binary, you can extract the compressed file and run it directly from the terminal.

# First Steps

The simplest FastAPI file could look like this:

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

Copy that to a file `main.py`.

Run the live server:

<div class="termy">

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

  "devDependencies": {
    "@hey-api/openapi-ts": "^0.27.38",
    "typescript": "^4.6.2"
  }
}
```

After having that NPM `generate-client` script there, you can run it with:

<div class="termy">

```console
$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

### Separate Program

To achieve this, you will normally have a **separate program** that would make sure your application is run on startup. And in many cases, it would also make sure other components or applications are also run, for example, a database.

### Example Tools to Run at Startup

Some examples of the tools that can do this job are:

* Docker
* Kubernetes
* Docker Compose
* Docker in Swarm Mode

    logging.info("Setting up GitHub Actions git user")
    subprocess.run(["git", "config", "user.name", "github-actions"], check=True)
    subprocess.run(
        ["git", "config", "user.email", "******@****.***"], check=True
    )
    branch_name = "fastapi-people"
    logging.info(f"Creating a new branch {branch_name}")
    subprocess.run(["git", "checkout", "-b", branch_name], check=True)

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

# (9)

It is the recommended server for Starlette and **FastAPI**.

!!! check "**FastAPI** recommends it as"
    The main web server to run **FastAPI** applications.

    You can combine it with Gunicorn, to have an asynchronous multi-process server.

    Check more details in the [Deployment](deployment/index.md){.internal-link target=_blank} section.

The important thing to keep in mind is that the `sub` key should have a unique identifier across the entire application, and it should be a string.

## Check it

Run the server and go to the docs: <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

You'll see the user interface like:

<img src="/img/tutorial/security/image07.png">

    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`:

<div class="termy">

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

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

# (9)