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, we can use multiprocessing.set_start_method
to change the way to start child processes:
multiprocessing.set_start_method('forkserver')
Both forkserver
mode and spawn
mode should work.
Please also refer to our ImageNet example, where MultiprocessIterator
is used.
Unfortunately, multiprocessing.set_start_method
is only available in Python 3.4+.
Therefore you need those recent Python versions to use MultiprocessIterator
.
Using Your Own Evaluator¶
Method create_multi_node_evaluator
can also be used for customized evaluator classes
that inherit from chainer.training.extensions.Evaluator
.
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 processes don’t exit when an error occurs in a process¶
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.:
# test.py
def func():
import mpi4py.MPI
mpi_comm = mpi4py.MPI.COMM_WORLD
if mpi_comm.rank == 0:
raise ValueError('failure!')
mpi4py.MPI.COMM_WORLD.Barrier()
if __name__ == '__main__':
func()
# mpiexec -n 2 python test.py
mpi4py offers a solution to force all processes to abort if an uncaught exception occurs..
$ mpiexec -n 2 python -m mpi4py yourscript.py ...
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
try:
import mpi4py.MPI
_old_hook(exctype, value, traceback)
rank = mpi4py.MPI.COMM_WORLD.Get_rank()
sys.stderr.write("\n")
sys.stderr.write("********************************************************\n")
sys.stderr.write("ChainerMN: Uncaught exception was detected on rank {}. \n".format(rank))
sys.stderr.write(" Calling MPI_Abort() to shut down MPI processes...\n")
sys.stderr.write("********************************************************\n\n\n")
sys.stderr.flush()
finally:
try:
import mpi4py.MPI
mpi4py.MPI.COMM_WORLD.Abort(1)
except Exception as e:
sys.stderr.write("Sorry, we failed to stop MPI, this MPI process may hang.\n")
sys.stderr.flush()
raise e
sys.excepthook = global_except_hook
# === end code snippet
def func():
"A sample function to cause the problem"
import mpi4py.MPI
mpi_comm = mpi4py.MPI.COMM_WORLD
if mpi_comm.rank == 0:
raise ValueError('failure!')
mpi4py.MPI.COMM_WORLD.Barrier()
if __name__ == '__main__':
func()
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.