CPython still needs to update reference counts after the fork even if your python code itself doesn't modify any data, making Copy on Write less helpful than it would first appear. Monolithic memory buffers like numpy arrays can be shared efficiently via CoW, but rich data structures with many PyObjects suffer from the refcounting issue.
True, but running the %debug magic command in IPython immediately after an unhandled exception drops you into the debugger at the point of the raised exception, complete with the full interpreter state including the stack. It doesn’t permit edit-and-continue but you can still evaluate expressions (including calling any function) to explore what went wrong without having to recreate the situation inside an explicit pdb session.
Honest question: how do you make github search work for you? It often fails to find stuff that I know exists in a given repo. Nothing fancy, even single-word full symbol names.
> The key insight is that file operations in separate directories don’t (for the most part) interfere with each other, enabling parallel execution.
It really is magically fast.
EDIT: Sorry, that tool is only for local copies. I just remembered you're doing remote copies. Still worth keeping in mind.