upstream/ipython Files · examples/parallel/task1.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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      # <nbformat>2</nbformat>

      # <markdowncell>

      # # Simple task farming example

      # <codecell>

      from __future__ import print_function

      from IPython.parallel import Client

      # <markdowncell>

      # A `Client.load_balanced_view` is used to get the object used for working with load balanced tasks.

      # <codecell>

      rc = Client()

      v = rc.load_balanced_view()

      # <markdowncell>

      # Set the variable `d` on all engines:

      # <codecell>

      rc[:]['d'] = 30

      # <markdowncell>

      # Define a function that will be our task:

      # <codecell>

      def task(a):

          return a, 10*d, a*10*d

      # <markdowncell>

      # Run the task once:

      # <codecell>

      ar = v.apply(task, 5)

      # <markdowncell>

      # Print the results:

      # <codecell>

      print("a, b, c: ", ar.get())

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				# <nbformat>2</nbformat>

				# <markdowncell>

				# # Simple task farming example

				# <codecell>
				from __future__ import print_function

				from IPython.parallel import Client

				# <markdowncell>

				# A `Client.load_balanced_view` is used to get the object used for working with load balanced tasks.

				# <codecell>

				rc = Client()
				v = rc.load_balanced_view()

				# <markdowncell>

				# Set the variable `d` on all engines:

				# <codecell>

				rc[:]['d'] = 30

				# <markdowncell>

				# Define a function that will be our task:

				# <codecell>

				def task(a):
				return a, 10d, a10*d

				# <markdowncell>

				# Run the task once:

				# <codecell>

				ar = v.apply(task, 5)

				# <markdowncell>

				# Print the results:

				# <codecell>

				print("a, b, c: ", ar.get())