examples/Parallel Computing/customresults.py

"""An example for handling results in a way that AsyncMapResult doesn't provide

Specifically, out-of-order results with some special handing of metadata.

This just submits a bunch of jobs, waits on the results, and prints the stdout
and results of each as they finish.

Authors
-------
* MinRK
"""
import time
import random

from IPython import parallel

# create client & views
rc = parallel.Client()
dv = rc[:]
v = rc.load_balanced_view()


# scatter 'id', so id=0,1,2 on engines 0,1,2
dv.scatter('id', rc.ids, flatten=True)
print(dv['id'])


def sleep_here(count, t):
    """simple function that takes args, prints a short message, sleeps for a time, and returns the same args"""
    import time,sys
    print("hi from engine %i" % id)
    sys.stdout.flush()
    time.sleep(t)
    return count,t

amr = v.map(sleep_here, range(100), [ random.random() for i in range(100) ], chunksize=2)

pending = set(amr.msg_ids)
while pending:
    try:
        rc.wait(pending, 1e-3)
    except parallel.TimeoutError:
        # ignore timeouterrors, since they only mean that at least one isn't done
        pass
    # finished is the set of msg_ids that are complete
    finished = pending.difference(rc.outstanding)
    # update pending to exclude those that just finished
    pending = pending.difference(finished)
    for msg_id in finished:
        # we know these are done, so don't worry about blocking
        ar = rc.get_result(msg_id)
        print("job id %s finished on engine %i" % (msg_id, ar.engine_id))
        print("with stdout:")
        print('    ' + ar.stdout.replace('\n', '\n    ').rstrip())
        print("and results:")
        
        # note that each job in a map always returns a list of length chunksize
        # even if chunksize == 1
        for (count,t) in ar.result:
            print("  item %i: slept for %.2fs" % (count, t))