P.S. -- Even if you are on Windows, whole SciPy ecosystem can be easily installed with pipenv, no need of conda for that now.
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.
What benefits does pipenv have beyond storing the hashes as well? In what way is it more sane? For example, does it get around the issues with, e.g., matplotlib being installed in a virtualenv?
If you are happy with your pip and venv workflow you can happily ignore pipenv, I personally prefer it because:
- It combines all the features of pip and venv with a better UI(in my opinion).
- I can specify my environments in more detail using Pipfile [https://docs.pipenv.org/advanced/#specifying-basically-anyth...]
- It is integrated with pyenv [https://docs.pipenv.org/advanced/#automatic-python-installat...]
- The promise of deterministic builds