dephell VS conda

I've found https://github.com/dephell/dephell but seems to be outdated.

I had a few relatively famous projects (like dephell), and at some point I lost my sleep because I was "fixing bugs" in it in my head in the middle of the night. Archiving it, closing issues in everything else, and starting to just write projects for my own fun only was the best decision I ever made. Don't make my mistakes. Don't ask random people on the internet what you should do. Do what you want to do and enjoy doing.

You jest and yet...

https://github.com/dephell/dephell

Dephell is a converter for python packaging systems. It can turn poetry files into requirements.txt, or setuptools' setup.py into pipenv's Pipfile etc.

Python Packaging: There is More Than One Way to Do It

Not necessarily. You can use Dephell (https://github.com/dephell/dephell) to convert from poetry to the old-fashioned requirements.txt

(& also come across DepHell)

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).