> Cross-vendor GPU support: A single codebase runs on AMD, NVIDIA, and CPU via KernelAbstractions.jl
This is why I wish Julia were the language for ML and sci comp in general, but Python is sucking all of the air out of the room.
> Cross-vendor GPU support: A single codebase runs on AMD, NVIDIA, and CPU via KernelAbstractions.jl
This is why I wish Julia were the language for ML and sci comp in general, but Python is sucking all of the air out of the room.
Maybe because Python can reasonably used to make actual applications instead of just notebooks or REPL sessions.
What's stopping Julia from being reasonably usable to make actual applications? It's been awhile since I've touched it, but I ain't seeing a whole lot in the way of obstacles there — just less inertia.
It's actually better suited IMO, being a compiled language. I'm not sure how anyone could consider the current train wreck of getting python code just to run "actual applications." uv is great and all, but many of these "actual applications" don't use it.