PyInstaller VS fastapi

Not sure if fixed in this patch, but pyinstaller had an issue in 3.12.0 https://github.com/pyinstaller/pyinstaller/issues/7992

You are right. I think I've misremembered the module name - it was uwsgi, not uvicorn.

This is a github issue where I discussed my original issue with PyInstaller devs - the dev explained the situation very well: https://github.com/pyinstaller/pyinstaller/issues/6362

PyInstaller seemed like the most maintained and developed tool to freeze python script into an executable, so I went with it. As expected, the freezed interface with the model was gigabytes large, so I had to figure out how to squeeze this. Fortunately, Onnx worked wonders and packaged the model into an inference only state, so I could throw away the Pytorch and Torchtext dependencies when freezing with Pyinstaller.Now the size of the executable with the model was 43MB instead of 4GB.

You might want to see Pyinstaller and auto_py_to_exe if you want a GUI interface.

It uses the pyinstaller command behind and please read their docs if you want to know more details.

I also found another link on github that may have some solutions to try: https://github.com/pyinstaller/pyinstaller/issues/3600

I made a Django react app. Now I want to make it a desktop application so that the user does not have type python manage.py runserver and also activate the environment every time. I used pyinstaller. I did all the steps mentioned for django

First, instead of creating a VM for windows, you may need to use a software called Wine mentioned in the pyinstaller FAQs

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.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.