py2exe VS fastapi

See this GitHub issue to follow the relevant discussion and development.

There are ways to bundle Python with your application using something like py2exe

Then you can turn the python program to an actual executable, a normal .exe file that user just launches and uses. If you chose to use this way, look into PyInstaller or py2exe. The executables will be obviously bigger, I think at least PyInstaller pretty much just packages Python and your script into .exe to make it work, so every program would be quite big (as each program has its own Python packaged). I also heard the process of turning the scripts to .exe is sometimes a bit problematic if you use some fancy imports and you also might run into problems with architecture.

*fyi, you can use a tool like py2exe or PyInstaller to convert Python scripts into a Windows Executable.

py2exe

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.