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"""An example for handling results in a way that AsyncMapResult doesn't provide
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Specifically, out-of-order results with some special handing of metadata.
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This just submits a bunch of jobs, waits on the results, and prints the stdout
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and results of each as they finish.
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Authors
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-------
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* MinRK
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"""
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import time
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import random
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from IPython import parallel
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# create client & views
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rc = parallel.Client()
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dv = rc[:]
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v = rc.load_balanced_view()
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# scatter 'id', so id=0,1,2 on engines 0,1,2
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dv.scatter('id', rc.ids, flatten=True)
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print(dv['id'])
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def sleep_here(count, t):
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"""simple function that takes args, prints a short message, sleeps for a time, and returns the same args"""
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import time,sys
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print("hi from engine %i" % id)
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sys.stdout.flush()
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time.sleep(t)
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return count,t
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amr = v.map(sleep_here, range(100), [ random.random() for i in range(100) ], chunksize=2)
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pending = set(amr.msg_ids)
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while pending:
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try:
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rc.wait(pending, 1e-3)
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except parallel.TimeoutError:
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# ignore timeouterrors, since they only mean that at least one isn't done
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pass
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# finished is the set of msg_ids that are complete
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finished = pending.difference(rc.outstanding)
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# update pending to exclude those that just finished
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pending = pending.difference(finished)
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for msg_id in finished:
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# we know these are done, so don't worry about blocking
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ar = rc.get_result(msg_id)
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print("job id %s finished on engine %i" % (msg_id, ar.engine_id))
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print("with stdout:")
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print(' ' + ar.stdout.replace('\n', '\n ').rstrip())
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print("and results:")
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# note that each job in a map always returns a list of length chunksize
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# even if chunksize == 1
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for (count,t) in ar.result:
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print(" item %i: slept for %.2fs" % (count, t))
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