upstream/ipython Files · examples/parallel/demo/map.py

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      from __future__ import print_function

      from IPython.parallel import *

      client = Client()

      view = client.load_balanced_view()

      @view.remote(block=True)

      def square(a):

          """return square of a number"""

          return a*a

      squares = map(square, range(42))

      # but that blocked between each result; not exactly useful

      square.block = False

      arlist = map(square, range(42))

      # submitted very fast

      # wait for the results:

      squares2 = [ r.get() for r in arlist ]

      # now the more convenient @parallel decorator, which has a map method:

      view2 = client[:]

      @view2.parallel(block=False)

      def psquare(a):

          """return square of a number"""

          return a*a

      # this chunks the data into n-negines jobs, not 42 jobs:

      ar = psquare.map(range(42))

      # wait for the results to be done:

      squares3 = ar.get()

      print(squares == squares2, squares3==squares)

      # True

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				from __future__ import print_function

				from IPython.parallel import *

				client = Client()
				view = client.load_balanced_view()

				@view.remote(block=True)
				def square(a):
				"""return square of a number"""
				return a*a

				squares = map(square, range(42))

				# but that blocked between each result; not exactly useful

				square.block = False

				arlist = map(square, range(42))
				# submitted very fast

				# wait for the results:
				squares2 = [ r.get() for r in arlist ]

				# now the more convenient @parallel decorator, which has a map method:
				view2 = client[:]
				@view2.parallel(block=False)
				def psquare(a):
				"""return square of a number"""
				return a*a

				# this chunks the data into n-negines jobs, not 42 jobs:
				ar = psquare.map(range(42))

				# wait for the results to be done:
				squares3 = ar.get()
				print(squares == squares2, squares3==squares)
				# True