While your millage (clearly) varies from mine, Anaconda is in practice the standard environment for deep learning (and, generally, in most of the Python data sc

While your millage (clearly) varies from mine, Anaconda is in practice the standard environment for deep learning (and, generally, in most of the Python data science ecosystem). For example, when you go to the front page of PyTorch (https://pytorch.org/ https://pytorch.org/), the default way to go is with Anaconda. It precisely makes it easy to install things regardless of the system and with matching versions. For example, out of box, it gives GPU support for Apple Silicon - not extra installation instructions. Pip installers don't work with non-Python dependencies. Of course, you can manually install things any way you like (including inside Docker), but it is up to you to make sure that all dependencies are compatible. And it is a non-trivial task, given frequent updates of all things involved (including CUDA kernels, Python versions, PyTorch/TF versions, and all libraries related to them one way or the other). I know times before Anaconda matured. Docker was often necessary to make code reusable - and I am grateful I don't need to go back to these times. Of course, now you CAN use Docker. Quite a lot of decent installation scripts place conda (usually miniconda) instructions in a Dockerfile.