This section describes some known problems that can arise when using MPIRE.

Progress bar issues with Jupyter notebooks

When using the progress bar in a Jupyter notebook you might encounter some issues. A few of these are described below, together with possible solutions.

IProgress not found

When you something like ImportError: IProgress not found. Please update jupyter and ipywidgets., this means ipywidgets is not installed. You can install it using pip:

pip install ipywidgets

or conda:

conda install -c conda-forge ipywidgets

Have a look at the ipywidgets documentation for more information.

Widget Javascript not detected

When you see something like Widget Javascript not detected. It may not be enabled properly., this means the Javascript extension is not enabled. You can enable it using the following command before starting your notebook:

jupyter nbextension enable --py --sys-prefix widgetsnbextension

Note that you have to restart your notebook server after enabling the extension, simply restarting the kernel won’t be enough.

Unit tests

When using the 'spawn' or 'forkserver' method you’ll probably run into one or two issues when running unittests in your own package. One problem that might occur is that your unittests will restart whenever the piece of code containing such a start method is called, leading to very funky terminal output. To remedy this problem make sure your setup call in is surrounded by an if __name__ == '__main__': clause:

from setuptools import setup

if __name__ == '__main__':

    # Call setup and install any dependencies you have inside the if-clause

See the ‘Safe importing of main module’ section at caveats.

The second problem you might encounter is that the semaphore tracker of multiprocessing will complain when you run individual (or a selection of) unittests using python test -s tests.some_test. At the end of the tests you will see errors like:

Traceback (most recent call last):
  File ".../site-packages/multiprocess/", line 132, in main
KeyError: b'/mp-d3i13qd5'
.../site-packages/multiprocess/ UserWarning: semaphore_tracker: There appear to be 58
                                                         leaked semaphores to clean up at shutdown
.../site-packages/multiprocess/ UserWarning: semaphore_tracker: '/mp-f45dt4d6': [Errno 2]
                                                         No such file or directory
  warnings.warn('semaphore_tracker: %r: %s' % (name, e))

Your unittests will still succeed and run OK. Unfortunately, I’ve not found a remedy to this problem using python test yet. What you can use instead is something like the following:

python -m unittest tests.some_test

This will work just fine. See the unittest documentation for more information.

Shutting down takes a long time on error

When you issue a KeyboardInterrupt or when an error occured in the function that’s run in parallel, there are situations where MPIRE needs a few seconds to gracefully shutdown. This has to do with the fact that in these situations the task or results queue can be quite full, still. MPIRE drains these queues until they’re completely empty, as to properly shutdown and clean up every communication channel.

To remedy this issue you can use the max_tasks_active parameter and set it to n_jobs * 2, or similar. Aside from the added benefit that the workers can start more quickly, the queues won’t get that full anymore and shutting down will be much quicker. See Maximum number of active tasks for more information.

When you’re using a lazy map function also be sure to iterate through the results, otherwise that queue will be full and draining it will take a longer time.

Unpicklable tasks/results

Sometimes you can encounter deadlocks in your code when using MPIRE. When you encounter this, chances are some tasks or results from your script can’t be pickled. MPIRE makes use of multiprocessing queues for inter-process communication and if your function returns unpicklable results the queue will unfortunately deadlock.

The only way to remedy this problem in MPIRE would be to manually pickle objects before sending it to a queue and quit gracefully when encountering a pickle error. However, this would mean objects would always be pickled twice. This would add a heavy performance penalty and is therefore not an acceptable solution.

Instead, the user should make sure their tasks and results are always picklable (which in most cases won’t be a problem), or resort to setting use_dill=True. The latter is capable of pickling a lot more exotic types. See Dill for more information.

AttributeError: Can’t get attribute ‘<some_function>’ on <module ‘__main__’ (built-in)>

This error can occur when inside an iPython or Jupyter notebook session and the function to parallelize is defined in that session. This is often the result of using spawn as start method (the default on Windows), which starts a new process without copying the function in question.

This error is actually related to the Unpicklable tasks/results problem and can be solved in a similar way. I.e., you can define your function in a file that can be imported by the child process, or you can resort to using dill by setting use_dill=True. See Dill for more information.


  • When using dill and an exception occurs, or when the exception occurs in an exit function, it can print additional OSError messages in the terminal, but they can be safely ignored.

  • The mpire-dashboard script does not work on Windows.


  • When encountering OSError: [Errno 24] Too many open files errors, use ulimit -n <number> to increase the limit of the number of open files. This is required because MPIRE uses file-descriptor based synchronization primitives and macOS has a very low default limit. For example, MPIRE uses about 190 file descriptors when using 10 workers.

  • Pinning of processes to CPU cores is not supported on macOS. This is because macOS does not support the sched_setaffinity system call. A warning will be printed when trying to use this feature.