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use __name__ == "__main__" for the MPIPoolExecutor #260

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

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9d921fd
use __name__ == "__main__" for the MPIPoolExecutor
basnijholt Apr 7, 2020
893154e
bump vmImages
basnijholt Apr 7, 2020
0912e06
use full url to mpi4py docs
basnijholt Apr 7, 2020
d2985fe
split comment over two lines
basnijholt Apr 7, 2020
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12 changes: 6 additions & 6 deletions azure-pipelines.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -12,28 +12,28 @@ jobs:
matrix:
UbuntuPy36:
python.version: '3.6'
vmImage: 'ubuntu-16.04'
vmImage: 'ubuntu-latest'
tox_env: 'py36'
UbuntuPy37:
python.version: '3.7'
vmImage: 'ubuntu-16.04'
vmImage: 'ubuntu-latest'
tox_env: 'py37'
UbuntuPy38:
python.version: '3.8'
vmImage: 'ubuntu-16.04'
vmImage: 'ubuntu-latest'
tox_env: 'py38'

macOSPy36:
python.version: '3.6'
vmImage: 'macOS-10.13'
vmImage: 'macOS-latest'
tox_env: 'py36'
macOSPy37:
python.version: '3.7'
vmImage: 'macOS-10.13'
vmImage: 'macOS-latest'
tox_env: 'py37'
macOSPy38:
python.version: '3.8'
vmImage: 'macOS-10.13'
vmImage: 'macOS-latest'
tox_env: 'py38'

WindowsServerPy36:
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36 changes: 20 additions & 16 deletions docs/source/tutorial/tutorial.parallelism.rst
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Expand Up @@ -65,27 +65,31 @@ For example, you create the following file called ``run_learner.py``:

from mpi4py.futures import MPIPoolExecutor

learner = adaptive.Learner1D(f, bounds=(-1, 1))
# use the idiom below, see the warning at
# https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.html#mpipoolexecutor
if __name__ == "__main__":

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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