> I vastly prefer creating hermetic environments with either venv or Docker. They're much cleaner and easier to work with. I wish data scientists would adopt these tools instead.
I suspect you have a lot of time on your hands. But for me the 'batteries included' approach really nails it, why repeat the headache over and over again when a single entity can take care of that in such a way that incompatibilities are almost impossible to create? The hardest time I've had was to re-create an environment that ran some python code from a while ago, with Anaconda it was super easy.
I'm sure it has its limitations and just like every other tool there are situations where it is best to avoid it but for now it suits me very well.
I would suggest you try out Miniconda (https://docs.conda.io/en/latest/miniconda.html). It comes with just the basics, and let's you install TF with GPU support by simply doing:
conda install -c anaconda tensorflow-gpu
I vastly prefer creating hermetic environments with either venv or Docker. They're much cleaner and easier to work with. I wish data scientists would adopt these tools instead.
Sadly, many of the ML models I investigate on Github don't even have their package requirements frozen. It's an uphill battle...