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

Backport PR : RemoteError._render_traceback_ calls self.render_traceback...

Backport PR : RemoteError._render_traceback_ calls self.render_traceback rather than an alias There are two options here: 1. `_render_traceback_` should *call* render_traceback 2. any subclass that redefines render_traceback must also redefine `_render_traceback_` I went with 1., which is more efficient code-wise when subclassing RemoteError (would prevent future cases of this same mistake), but less efficient execution-wise, because it involves an extra function call. closes

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      # <nbformat>2</nbformat>

      # <markdowncell>

      # # Load balanced map and parallel function decorator

      # <codecell>

      from __future__ import print_function

      from IPython.parallel import Client

      # <codecell>

      rc = Client()

      v = rc.load_balanced_view()

      # <codecell>

      result = v.map(lambda x: 2*x, range(10))

      print("Simple, default map: ", list(result))

      # <codecell>

      ar = v.map_async(lambda x: 2*x, range(10))

      print("Submitted tasks, got ids: ", ar.msg_ids)

      result = ar.get()

      print("Using a mapper: ", result)

      # <codecell>

      @v.parallel(block=True)

      def f(x): return 2*x

      result = f.map(range(10))

      print("Using a parallel function: ", result)

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				# <nbformat>2</nbformat>

				# <markdowncell>

				# # Load balanced map and parallel function decorator

				# <codecell>
				from __future__ import print_function

				from IPython.parallel import Client

				# <codecell>

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

				# <codecell>

				result = v.map(lambda x: 2*x, range(10))
				print("Simple, default map: ", list(result))

				# <codecell>

				ar = v.map_async(lambda x: 2*x, range(10))
				print("Submitted tasks, got ids: ", ar.msg_ids)
				result = ar.get()
				print("Using a mapper: ", result)

				# <codecell>

				@v.parallel(block=True)
				def f(x): return 2*x

				result = f.map(range(10))
				print("Using a parallel function: ", result)