PyInstaller VS conda

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

Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.

conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.

The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).

It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.

It took some time but as great Christmas present Miniconda was finally released with Python 3.10!

The September update broke it.

According to this this issue is high on their priority list (whatever that means).