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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
- venv
- pipenv
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
- stores environment in a root directory i.e.
~/miniconda3 - conda can use its own way to manage environments
environment.yml - the python interpreter is packaged with the environment
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
- environments are typically stored in the project directory
- does not package the interpreter
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