I don't disagree that Python environments are a mess. I'm actually a developer on quite a prominent large scale neural network training library and a DL researcher that uses said library. With my developer hat on I like to have minimal dependencies and keep Python scripting as decoupled as possible from the CUDA C++ implementation. With my researcher hat on I don't want to be slowed down by C++ development every time I want to change my model or training pipeline. At least for me, C++ development is slower and more error prone than modifying Python.
Obviously doing any heavy lifting in Python is a bad idea. But as a scripting language I think it's good, especially if you keep the environment simple. I don't think the answer for DL training is to dump Python entirely and start over in pure C/C++/Rust/Julia/whatever. Learning C/C++ is too big of an ask for everyone working on the model design and training side and it would slow down progress significantly - most of that work is actually data munging and targeted model tweaks. But I do think there's still a lot that can be done to decouple Python from the underlying engine and yield networks where inference can be run in a minimal dependency environment. There's lots of great people working on all these things.
Obviously doing any heavy lifting in Python is a bad idea. But as a scripting language I think it's good, especially if you keep the environment simple. I don't think the answer for DL training is to dump Python entirely and start over in pure C/C++/Rust/Julia/whatever. Learning C/C++ is too big of an ask for everyone working on the model design and training side and it would slow down progress significantly - most of that work is actually data munging and targeted model tweaks. But I do think there's still a lot that can be done to decouple Python from the underlying engine and yield networks where inference can be run in a minimal dependency environment. There's lots of great people working on all these things.