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

Merge pull request from minrk/even-more-ways-to-get-ifaces...

Merge pull request from minrk/even-more-ways-to-get-ifaces Add even more ways to populate localinterfaces use netifaces for faster IPython.utils.localinterfaces when availlable, Parse subprocess output from ifconfig / ip addr / ipconfig. Lower priority than netifaces, but still higher priority than socket.gethostbyname. Fallback to gethostname otherwise. Should be much faster in worst case scenario where machine are badly configurred and can wait up to ~30s to start ipython. Slighly slower in other cases.

Brian Granger - - Load All Authors

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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