So rather than writing another Python wrapper over c++ they are making a new performant language that can call Python.
To me it makes sense as torch is great and hard to compete with, but everything feeding into it is a mess today (Data loading, distribution logic).
don't forget the control layer/data layer separation principle. Performance mostly only matters at the data layer, and I don't believe that python ML really has a substantial problem with this, aside from not having a real distribution story. So "having a more performant python" doesn't really solve that much.
I'll tell you what could make the control layer better.
- no gil
- better async primitives
- immutability of passed parameters
- better testing story
- better documentation story (python is quite good at documentation, well, when python devs actually do it, which they usually don't).
- project-local dependencies with no shenanigans
Could you explain or give references to what you exactly mean by this? I've heard of separation of concerns, but is this a specific realization of that principle?