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Ok. I think that’s all handled by my workflow, but it does involve taking responsibility for requirements files.

If I want to install something, I pip install and then add the explicit version to the base. I can then freeze the current state to requirements to lock in all the sub dependencies.

It’s a bit manual (though you only need a couple of cli commands) but it’s simple and robust.

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This is my workflow too. And it works fine. I think the disconnect here is that I grew up fighting dependencies when compiling other programs from source on Linux. I know how painful it can be and I’ve accepted the pain and when I came to python/venv I thought “This isn’t so bad!”

But if someone is coming from data science and not dev-ops then no matter how much we say “all you have to do”. The response will be why do I have to do any of this?

I don't think that manual handling of requirement.txt in a collaborative environment is a robust process. It will be a waste of time and resources to handle it like that. And I don't know about your workflow but it is obviously not standard and it does not address the first and forth points.

Haha. Ok. I think that’s where we’re just going to have to agree to disagree.

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