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2ac531973e
The main benefit is that they release the memory they were holding onto. llvm-svn: 326127
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import sys
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import multiprocessing
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_current = None
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_total = None
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def _init(current, total):
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global _current
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global _total
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_current = current
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_total = total
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def _wrapped_func(func_and_args):
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func, argument, should_print_progress = func_and_args
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if should_print_progress:
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with _current.get_lock():
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_current.value += 1
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sys.stdout.write('\r\t{} of {}'.format(_current.value, _total.value))
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sys.stdout.flush()
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return func(argument)
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def pmap(func, iterable, processes, should_print_progress, *args, **kwargs):
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"""
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A parallel map function that reports on its progress.
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Applies `func` to every item of `iterable` and return a list of the
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results. If `processes` is greater than one, a process pool is used to run
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the functions in parallel. `should_print_progress` is a boolean value that
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indicates whether a string 'N of M' should be printed to indicate how many
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of the functions have finished being run.
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"""
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global _current
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global _total
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_current = multiprocessing.Value('i', 0)
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_total = multiprocessing.Value('i', len(iterable))
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func_and_args = [(func, arg, should_print_progress,) for arg in iterable]
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if processes == 1:
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result = map(_wrapped_func, func_and_args, *args, **kwargs)
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else:
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pool = multiprocessing.Pool(initializer=_init,
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initargs=(_current, _total,),
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processes=processes)
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result = pool.map(_wrapped_func, func_and_args, *args, **kwargs)
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pool.close()
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pool.join()
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if should_print_progress:
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sys.stdout.write('\r')
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return result
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