kedro-environment

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Avoid serious version conflict issues, and use a virtual environment anytime you are running python, here are three ways you can setup a kedro virtual environment.

conda

I prefer to use conda as my virtual environment manager of choice as it give me both the interpreter and the packages I install. I don't have to rely on the system version of python or another tool to maintain python versions at all, I get everything in one tool.

conda create -n my-project python=3.8 -y
conda activate my-project
python  -m pip install --upgrade pip
pip install -e src
conda info --envs

virtualenv

Virtual env (venv) is another very respectable option that is built right into python, and requires no additional installs or using a different distribution of pytyhon.

python -m venv .venv
source ./.venv/bin/activate
python  -m pip install --upgrade pip
pip install -e src

pipenv

Pipenv is another virtual enviroment tool that comes with its own system for managing dependencies using a pipfile . It's main benefit is that it creates a lockfile that will allow users to replicate the exact version of all their packages. The typical requirements.txt workflow can easily break as new version of dependecies are released between testing and deplpoyment.

pipx run pipenv shell
python  -m pip install --upgrade pip
pip install -e src
* stores environment in a root directory i.e. ~/.local/share/virtualenvs/ * pipenv can use its own way to manage environments pipfile * does not package the interpreter

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