![]() You then just use a new container for every project. You spin up a container from an image that has python2 or python3 tools already installed, and then install your requirements in that container. ![]() Worse yet, many projects haven’t fully moved to python3 yet! So you may find yourself juggling systemwide requirements across python2 and python3.Ī lot of folks use docker for this type of workflow issue. ![]() If one of your projects has requirements that conflicts with another, switching to that project and running pip install will effectively break your other project by modifying the systemwide python libraries it needs to run. By default, pip install puts libraries in your systemwide libraries folder. If you jump between a lot of different machine learning projects, you probably find yourself running something like pip install -r requirements.txt quite often. If you’re doing machine learning and tired of python dependency hell, use pyenv! ![]()
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