Merge pull request #260 from python-adaptive/mpi-doc-fix

use __name__ == "__main__" for the MPIPoolExecutor

Author: basnijholt

azure-pipelines.yml

@@ -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:

docs/source/tutorial/tutorial.parallelism.rst

@@ -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: