Tips and FAQs

Using MultiprocessIterator

If you are using MultiprocessIterator and communication goes through InfiniBand, you would probably face crashing problems. This is because MultiprocessIterator creates child processes by the fork system call, which has incompatibilities with the design of MPI and InfiniBand. To cope with this issue, use multiprocessing.set_start_method to start child processes, with a process explicitly forked right after, before communicator is created as follows:

p = multiprocessing.Process(target=lambda *x: x, args=())

communicator = chainermn.create_communicator(...)

Either forkserver mode or spawn mode should work. See our ImageNet example script for working sample code of MultiprocessIterator and forkserver. Unfortunately, multiprocessing.set_start_method is only available in Python 3.4+.

Using Your Own Evaluator

Method create_multi_node_evaluator can also be used for customized evaluator classes that inherit from Specifically, it wraps the evaluate method and returns the averaged values over all workers. Please also refer to our ImageNet example, where a customized evaluator is used.

Using MPI4py Communicator

ChainerMN is based on MPI4py. For advanced users (e.g., those who want to parallelize preprocessing, create custom extension, etc.), we encourage you to make use of MPI4py communicators. Let comm be a ChainerMN communicator, then you can obtain MPI4py communicator by comm.mpi_comm. Please refer to MPI4py API reference.

Using FP16

FP16 (16-bit half precision floating point values) is supported in pure_nccl of a ChainerMN communicator.

MPI process hangs after an unhandled Python exception.

An MPI runtime is expected to kill all of its child processes if one of them exits abnormally or without calling MPI_Finalize(). However, when a Python program runs on mpi4py, the MPI runtime often fails to detect the process failure, and the rest of the processes hang infinitely. It is especially problematic when you run your ChainerMN program on a cloud environment, in which you are charged on time basis.

This tiny program demonstrates the issue (note that it is not specific to ChainerMN).:

def func():
  import mpi4py.MPI
  mpi_comm = mpi4py.MPI.COMM_WORLD
  if mpi_comm.rank == 0:
    raise ValueError('failure!')


if __name__ == '__main__':

# mpiexec -n 2 python

mpi4py offers a solution to force all processes to abort if an uncaught exception occurs..

$ mpiexec -n 2 python -m mpi4py ...

This also works well with ChainerMN. See here for more details.

If you cannot apply the solution (i.e. you don’t have a control of how Python interpreter is invoked), you can inject the following code snippet into your script file

import sys

# === begin code snippet
_old_hook = sys.excepthook

# Global error handler
def global_except_hook(exctype, value, traceback):
  import sys
      import mpi4py.MPI

$ mpiexec -n 2 -x CHAINERMN_FORCE_ABORT_ON_EXCEPTION=1 python ...

Alternatively, you can explicitly call chainermn.global_except_hook.add_hook() from your code.:

import chainermn


The handler hooks uncaught exceptions and call MPI_Abort() to ensure that all process are terminated.

You can choose any of these solutions depending on your environment and restrictions.

NOTE: These techniques are effective only for unhandled Python exceptions. If your program crashes due to lower-level issues such as SIGSEGV, the MPI process may still hang.