Anaconda may work well as a virtual environment for some ml projects, but it is by no means a solution for getting a gpu-working installation of tensorflow on Windows.
As a software engineer and non-data scientist, I hate Anaconda because it feels like it's a tool that tries to be the be-all, end-all package management tool for everyone in the data science field, yet it feels like a sloppily built, bloated whale. It's even managed to overwrite PATH on some of my Linux machines, which is where I drew the line.
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...
> 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.
