upstream/ipython Files · docs/examples/parallel/task1.py

Merge pull request from dopplershift/pylab-switch...

Merge pull request from dopplershift/pylab-switch Enable switching %pylab mode between inline and a single gui mode in a single notebook. With this merge, `%pylab` can be called interactively to toggle inline/GUI (matplotlib floating windows) mode. After initializing `%pylab inline`, now one can call `%pylab` without arguments to activate the default GUI or ask for a specific one as usual. IPython will detect if a different GUI is requested if one was already activated and will refuse to do so (to prevent multiple event loops from running concurrently, which often leads to problems).

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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())