kivy VS Numba

We will create this complete Python registration form using Kivy. We get started by installing Kivy, a powerful Python framework for building interactive applications.

For reference, YouTube runs on Python[1,2,3]:

> 1. Python and Django: YouTube’s backend is predominantly written in Python, offering a balance of performance and readability.

> 2. Google Cloud Platform...

> 3. Java and C++: YouTube also utilizes Java and C++ for specific backend services, as they provide better performance for certain tasks.

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A long time ago, I looked into these Python frameworks:

- http://www.web2py.com/

- https://kivy.org/

[1]: https://blogopost.com/youtube-tech-stack/#backend

[2]: https://medium.com/@shanthanaroja99/technology-stack-behind-...

[3]: https://youtu.be/G-lGCC4KKok

I have a multi-threaded app using Kivy for the GUI and sometimes need to update the GUI from a background thread. This app is a few years old and I recently updated to Kivy 2.2 and am now getting an exception with the message "Cannot create graphics instruction outside the main Kivy thread" -- which is related to this change in Kivy 2.1: https://github.com/kivy/kivy/pull/7270

I suggest you use kivy which is suitable for the desktop but also has the advantage of being one of few options for creating Python based native(ish) mobile apps (for IoS and Android app stores).

I think the best one right now for python is "beeware": https://beeware.org/

You also have Kivy which is prety good: https://kivy.org/

Anyone has some experience with Kivy [1]? It seems that it checks off some of my requirements, like cross-platform, supporting touch interfaces, ease of development, allows complex/fancy UIs as well, etc.

[1] https://kivy.org/

There are hot reload tools like kivy.tools.kviewer and KivyMD HotReload and I think others... but maybe they used something else, hard to tell

Notable Projects: Kivy ( https://kivy.org/ )

My name is Mathieu Virbel, and I am a consultant on system and software architecture with over 12 years of experience as a freelance and 20 years in the field. I have a passion for creating innovative and user-friendly applications. I specialize in using the open-source Kivy framework to develop desktop and mobile applications, but I am also skilled in other technologies and frameworks.

SEEKING WORK | France (UTC-1) | Remote | Full stack developer

My name is Mathieu Virbel, and I am a consultant on system and software architecture with over 12 years of experience as a freelance and 20 years in the field. I have a passion for creating innovative and user-friendly applications. I specialize in using the open-source Kivy framework to develop desktop and mobile applications, but I am also skilled in other technologies and frameworks.

I worked on a variety of subjects, from Interactives and mobile application for Museum and Public Institutions, Embedded system in security company, telecommunication, and Startups environments from scratch. Recently playing with Python, Golang, VueJS 3/Typescript, InfluxDB, Docker Swarm, as well as writing specifications and reviewing code of others contractors.

Technologies: Python (django, flask, tensorflow, Kivy, …), Golang, TypeScript, VueJS, Quasar, Docker, Docker Swarm, Terraform, Ansible, MongoDB, InfluxDB, MariaDB, SQLite, Kafka, Prometheus/Graphana

Résumé/CV: https://meltingrocks.com/cv

LinkedIn: https://www.linkedin.com/in/mathieuvirbel/

Website: https://meltingrocks.com

Email: [email protected]

Notable Projects: Kivy ( https://kivy.org/ )

Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.

Simulations are, at least in my experience, numba’s [0] wheelhouse.

[0]: https://numba.pydata.org/

That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.

A super-fast backtesting engine built in NumPy and accelerated with Numba.

Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.

Just for reference,

* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

For the benefit of future readers: https://numba.pydata.org/

Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.