Pipenv is only as good as the wheels that get installed. And the wheels are only as good as the assumption that your runtime/deployment environment matches the

Pipenv is only as good as the wheels that get installed. And the wheels are only as good as the assumption that your runtime/deployment environment matches the buildtime environment of the person who made those wheels. Oftentimes with ML and compute-intensive workloads, performance is an aspect of correctness. It is nontrivial to get the right accelerated libraries, and the right GPU libraries linking against the right Numpy version for the particular notebook you're running. If you are doing work just for yourself, or if you have total control over the deployment environment (and your collaborator's environments), then you might be able to get away with just using pip for these things.