pytorch/docs/source/multiprocessing.rst

Multiprocessing package - torch.multiprocessing
===============================================

.. automodule:: torch.multiprocessing
.. currentmodule:: torch.multiprocessing

.. warning::

    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
-------------------

.. autofunction:: get_all_sharing_strategies
.. autofunction:: get_sharing_strategy
.. autofunction:: set_sharing_strategy

Sharing CUDA tensors
--------------------

Sharing CUDA tensors between processes is supported only in Python 3, using
a ``spawn`` or ``forkserver`` start methods. :mod:`python: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

    torch.set_default_tensor_type(torch.cuda.FloatTensor)

    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()

        p.join()

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``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


.. note::

    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, :mod:`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
---------------------

.. note::

   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.

.. autofunction:: spawn

.. class:: SpawnContext

   Returned by :func:`~spawn` when called with ``join=False``.

   .. automethod:: join