Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

**Typer** is FastAPI's little sibling. And it's intended to be the **FastAPI of CLIs**. ⌨️ 🚀

## Requirements

FastAPI stands on the shoulders of giants:

takes arguments in reversed order and with different keywords. In particular we
now match NumPy order as `tf.concat(values, axis, name)`. *
`tf.image.decode_jpeg` by default uses the faster DCT method, sacrificing a
little fidelity for improved speed. One can revert to the old behavior by
specifying the attribute `dct_method='INTEGER_ACCURATE'`. * `tf.complex_abs` has
been removed from the Python interface. `tf.abs` supports complex tensors and

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:

* **Security - HTTPS**
* **Running on startup**
* ***Restarts***
* Replication (the number of processes running)
* **Memory**

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

If you are paying for 3 servers but you are using only a little bit of their RAM and CPU, you are probably **wasting money** 💸, and probably **wasting server electric power** 🌎, etc.

This is just a **quick tutorial / refresher** about Python type hints. It covers only the minimum necessary to use them with **FastAPI**... which is actually very little.

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

!!! note

```

</div>

That would work for most of the cases. 😎

You could use that command for example to start your **FastAPI** app in a container, in a server, etc.

## ASGI Servers

Let's go a little deeper into the details.

FastAPI uses a standard for building Python web frameworks and servers called <abbr title="Asynchronous Server Gateway Interface">ASGI</abbr>. FastAPI is an ASGI web framework.

# On windows, we still link everything into a single DLL.
build:windows --config=monolithic

# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels

# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN
build:windows --copt=-DNOGDI
build:windows --host_copt=-DNOGDI

It doesn't use a data validation, serialization and documentation third-party library like Pydantic, it has its own. So, these data type definitions would not be reusable as easily.

It requires a little bit more verbose configurations. And as it is based on WSGI (instead of ASGI), it is not designed to take advantage of the high performance provided by tools like Uvicorn, Starlette and Sanic.