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basnijholt merged 4 commits into from
Apr 7, 2020
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use __name__ == "__main__" for the MPIPoolExecutor #260

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use __name__ == "__main__" for the MPIPoolExecutor
basnijholt Apr 7, 2020
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bump vmImages
basnijholt Apr 7, 2020
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use full url to mpi4py docs
basnijholt Apr 7, 2020
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basnijholt Apr 7, 2020
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34 changes: 18 additions & 16 deletions docs/source/tutorial/tutorial.parallelism.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -65,27 +65,29 @@ For example, you create the following file called ``run_learner.py``:

from mpi4py.futures import MPIPoolExecutor

learner = adaptive.Learner1D(f, bounds=(-1, 1))
if __name__ == "__main__": # ← use this, see warning @ https://bit.ly/2HAk0GG
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learner = adaptive.Learner1D(f, bounds=(-1, 1))

# load the data
learner.load(fname)
# load the data
learner.load(fname)

# run until `goal` is reached with an `MPIPoolExecutor`
runner = adaptive.Runner(
learner,
executor=MPIPoolExecutor(),
shutdown_executor=True,
goal=lambda l: l.loss() < 0.01,
)
# run until `goal` is reached with an `MPIPoolExecutor`
runner = adaptive.Runner(
learner,
executor=MPIPoolExecutor(),
shutdown_executor=True,
goal=lambda l: l.loss() < 0.01,
)

# periodically save the data (in case the job dies)
runner.start_periodic_saving(dict(fname=fname), interval=600)
# periodically save the data (in case the job dies)
runner.start_periodic_saving(dict(fname=fname), interval=600)

# block until runner goal reached
runner.ioloop.run_until_complete(runner.task)
# block until runner goal reached
runner.ioloop.run_until_complete(runner.task)

# save one final time before exiting
learner.save(fname)
# save one final time before exiting
learner.save(fname)


On your laptop/desktop you can run this script like:
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