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Multiprocessing package - torch.multiprocessing

torch.multiprocessing is a wrapper around the native multiprocessing module. It registers custom reducers, that use shared memory to provide shared views on the same data in different processes. Once the tensor/storage is moved to shared_memory (see share_memory_()), it will be possible to send it to other processes without making any copies.

The API is 100% compatible with the original module - it’s enough to change import multiprocessing to import torch.multiprocessing to have all the tensors sent through the queues or shared via other mechanisms, moved to shared memory.

Because of the similarity of APIs we do not document most of this package contents, and we recommend referring to very good docs of the original module.


If the main process exits abruptly (e.g. because of an incoming signal), Python’s multiprocessing sometimes fails to clean up its children. It’s a known caveat, so if you’re seeing any resource leaks after interrupting the interpreter, it probably means that this has just happened to you.

Strategy management


Returns a set of sharing strategies supported on a current system.


Returns the current strategy for sharing CPU tensors.


Sets the strategy for sharing CPU tensors.

Parameters:new_strategy (str) – Name of the selected strategy. Should be one of the values returned by get_all_sharing_strategies().

Sharing CUDA tensors

Sharing CUDA tensors between processes is supported only in Python 3, using a spawn or forkserver start methods. multiprocessing in Python 2 can only create subprocesses using fork, and it’s not supported by the CUDA runtime.

Unlike CPU tensors, the sending process is required to keep the original tensor as long as the receiving process retains a copy of the tensor. This shouldn’t be a problem for sharing model parameters (which stay live for the entire execution of the model), but passing other kinds of data should be done with care.

Here is an example program which handles these requirements correctly:


import torch import torch.multiprocessing as mp


def sender(q, e):
for i in range(10):
s_sample = [torch.zeros(1), torch.ones(1)] q.put(s_sample) e.wait() del s_sample e.clear()
if __name__ == “__main__”:

ctx = mp.get_context(“spawn”) q = ctx.Queue() e = ctx.Event() p = ctx.Process(target=sender, args=(q, e)) p.start()

for i in range(10):
print(‘=== ITER {} ===”.format(i)) r_sample = q.get() del r_sample e.set()


In the example above, calling e.wait() on sender side ensures tensor s_sample doesn’t get deleted while receiver is working on it. The receiver signals when it is done with the tensor using e.set(), being careful to del its reference to the received tensor first. It is INSUFFICIENT to promise never to call r_sample again; while r_sample is live, it may be confused with any subsequent tensors allocated by the source process at the same address.

If a receiver wants to save the data of r_sample for future use while letting the source process deallocate the original, it must clone() it.

This behavior is very confusing, and we are tracking a fix for it at https://github.com/pytorch/pytorch/issues/16141

Sharing strategies

This section provides a brief overview into how different sharing strategies work. Note that it applies only to CPU tensor - CUDA tensors will always use the CUDA API, as that’s the only way they can be shared.

File descriptor - file_descriptor


This is the default strategy (except for macOS and OS X where it’s not supported).

This strategy will use file descriptors as shared memory handles. Whenever a storage is moved to shared memory, a file descriptor obtained from shm_open is cached with the object, and when it’s going to be sent to other processes, the file descriptor will be transferred (e.g. via UNIX sockets) to it. The receiver will also cache the file descriptor and mmap it, to obtain a shared view onto the storage data.

Note that if there will be a lot of tensors shared, this strategy will keep a large number of file descriptors open most of the time. If your system has low limits for the number of open file descriptors, and you can’t raise them, you should use the file_system strategy.

File system - file_system

This strategy will use file names given to shm_open to identify the shared memory regions. This has a benefit of not requiring the implementation to cache the file descriptors obtained from it, but at the same time is prone to shared memory leaks. The file can’t be deleted right after its creation, because other processes need to access it to open their views. If the processes fatally crash, or are killed, and don’t call the storage destructors, the files will remain in the system. This is very serious, because they keep using up the memory until the system is restarted, or they’re freed manually.

To counter the problem of shared memory file leaks, torch.multiprocessing will spawn a daemon named torch_shm_manager that will isolate itself from the current process group, and will keep track of all shared memory allocations. Once all processes connected to it exit, it will wait a moment to ensure there will be no new connections, and will iterate over all shared memory files allocated by the group. If it finds that any of them still exist, they will be deallocated. We’ve tested this method and it proved to be robust to various failures. Still, if your system has high enough limits, and file_descriptor is a supported strategy, we do not recommend switching to this one.

Spawning subprocesses


Available for Python >= 3.4.

This depends on the spawn start method in Python’s multiprocessing package.

Spawning a number of subprocesses to perform some function can be done by creating Process instances and calling join to wait for their completion. This approach works fine when dealing with a single subprocess but presents potential issues when dealing with multiple processes.

Namely, joining processes sequentially implies they will terminate sequentially. If they don’t, and the first process does not terminate, the process termination will go unnoticed. Also, there are no native facilities for error propagation.

The spawn function below addresses these concerns and takes care of error propagation, out of order termination, and will actively terminate processes upon detecting an error in one of them.

torch.multiprocessing.spawn(fn, args=(), nprocs=1, join=True, daemon=False)[source]

Spawns nprocs processes that run fn with args.

If one of the processes exits with a non-zero exit status, the remaining processes are killed and an exception is raised with the cause of termination. In the case an exception was caught in the child process, it is forwarded and its traceback is included in the exception raised in the parent process.

  • fn (function) –

    Function is called as the entrypoint of the spawned process. This function must be defined at the top level of a module so it can be pickled and spawned. This is a requirement imposed by multiprocessing.

    The function is called as fn(i, *args), where i is the process index and args is the passed through tuple of arguments.

  • args (tuple) – Arguments passed to fn.
  • nprocs (int) – Number of processes to spawn.
  • join (bool) – Perform a blocking join on all processes.
  • daemon (bool) – The spawned processes’ daemon flag. If set to True, daemonic processes will be created.

None if join is True, SpawnContext if join is False

class torch.multiprocessing.SpawnContext[source]

Returned by spawn() when called with join=False.


Tries to join one or more processes in this spawn context. If one of them exited with a non-zero exit status, this function kills the remaining processes and raises an exception with the cause of the first process exiting.

Returns True if all processes have been joined successfully, False if there are more processes that need to be joined.

Parameters:timeout (float) – Wait this long before giving up on waiting.


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