submitting cpu-bound tasks to processes and io-bound tasks to threads
Convert a classic sequential program into a parallel one.
It runs faster.
What if not?
Don’t use it.
for image in images: create_thumbnail(image) # original for image in images: fork(create_thumbnail, image) # parallelized explicitly for image in images: create_thumbnail(image) # parallelized implictly (Caution: Magic)
What about return values?
result = fork(my_func, *args, **kwargs)
And what is this result?
A future that behaves almost exactly as if it were the return value of my_func. That in turn means, as soon as you access the result and it is not ready yet, the main thread blocks.
Speaking of threads …
and processes? That depends on whether your function is @io_bound or @cpu_bound. Not decorated means @cpu_bound.
If necessary, decorate your functions:
@io_bound def call_remote_webservice(): # implementation @cpu_bound def fib(n): # naive implementation of Fibonacci numbers @unsafe # don't fork; run sequentially def weird_side_effects(*args, **kwargs): # implementation
Sure. If you don’t like the fork calling syntax, try those decorators:
@io_bound_fork def create_thumbnail_by_webservice(image): # implementation @cpu_bound_fork def create_thumbnail_by_bare_processing_power(image): # implementation # the following two lines spawn two forks create_thumbnail_by_webservice() create_thumbnail_by_bare_processing_power()
- easy way back and forth (from sequential to parallel and vice versa)
- cascading possible (thread-safe)
- Python 3 (out of the box)
- Python 2 (via pip install futures)
- weird calling syntax (no syntax support)
- type(result) == BlockingFuture
- not working with coroutines (asyncio) yet
- future is not contagious yet
- not working with lambdas due to PickleError
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