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Earlier quoted context omitted.
It's not Py3 compatible yet, but from the commit log it seems like they're working on it (we could all help?).
I wonder what is keeping them from Python 3 support. I'll take a look, although I am currently stuck on Python 2.7 for my current project.
Perhaps a strange choice of the word "synchronized" when coming from Java, this typically implies a critical section. Here it seems to initialize a multiprocessing pool for use in the function labelled concurrent (perhaps)?
For simple usage if you are familiar with Go there is this library: https://github.com/pothos/awaitchannel
With Python 3.5 there is native support for concurrency by using the keywords await and async. For simple usage if you are familiar with Go there is this library: https://github.com/pothos/awaitchannel
source looks surprising, for instance the decorators parse the decorated function code and build an AST (still have to find out why)
I've been looking for a way to replace functools.partial and pool.map with something that could cause me to make bad architectural decisions. This could be the ticket.
A couple of things offhand: - It has Python 2 and 3 support - It's a wrapper for the Python built-in "multiprocessing" library - It spreads out work over all cores (so the abstraction hides the ability to control the pool) Seems like a great way to get your feet wet with multiprocessing in Python, but it likely has limited use in production...although certain infrastructures like resource limited containers might be…
this is such a good idea! the @synchronized decorator to collect the parallelized task at the end of a parent call is very very smart & simple.
Multiprocessing gets pretty useless for anything outside of independent CPU bound tasks with little IPC and simple data types that can be stuck into shared memory.
If you're using multiprocessing pools so often that you think you need a decorator to clean up your code, then wow, I'd like to see what you're up to. ;)