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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Union
from .. import py_graphlearn_torch as pywrap
from ..utils import parse_size
from .base import SampleMessage, ChannelBase
[docs]class ShmChannel(ChannelBase):
r""" A communication channel for sample messages based on a shared-memory
queue, which is implemented in the underlying c++ lib.
Note that the underlying shared-memory buffer of this channel is pinnable,
which will achieve better performance when the consumer needs to copy
data from channel to gpu device.
Args:
capacity: The max bufferd number of sample messages in channel.
shm_size: The allocated size (bytes) for underlying shared-memory.
When the producer send sample message to the channel, it will be limited by
both `capacity` and `shm_size`. E.g, if current number of buffered
messages in channel reaches the `capacity` limit, or current used
buffer memory reaches the `shm_size` limit, the current `send` operation
will be blocked until some messages in channel are consumed and related
resource are released.
"""
def __init__(self,
capacity: int=128,
shm_size: Union[str, int]='256MB'):
assert capacity > 0
shm_size = parse_size(shm_size)
self._queue = pywrap.SampleQueue(capacity, shm_size)
[docs] def pin_memory(self):
r""" Pin underlying shared-memory.
"""
self._queue.pin_memory()
[docs] def send(self, msg: SampleMessage, **kwargs):
self._queue.send(msg)
[docs] def recv(self, **kwargs) -> SampleMessage:
return self._queue.receive()