@User Did some testing there, and found the following: PyPI and Conda have different naming conventions between some packages (

jupyter-client

vs

jupyter_client

, etc), meaning that the resulting

requirements.txt

from

kedro build-reqs

almost certainly can't be installed by Conda. Of course, one can manually manage a

requirements.txt

or

environment.yml

without any help from Kedro that can be parsed by Conda, but you'll run into more problems with

setup.py

as

install_requires

will parse the

.txt

and search PyPI for the contents, resulting in the same problem. As of right now, it seems like doing everything with

pip

is the "correct" way (I don't know anything about poetry), or at least the path of least resistance if you want to work with the existing starters out-of-the-box.

This is the best combo to avoid conda weird dependencies check/install/resolve and time lost by waiting conda to resolve things + you just need to poetry install or pip install you requirements file whenever you need to update package inside the conda env.

I use pip with kedro, and poetry outside when it's a project without kedro or I initialize myself without any requirements file created

It resolves also a lot of boring time dealing with conda packages not updated like pip ones

In case useful there is a conda related discussion going on here https://github.com/kedro-org/kedro/discussions/1220