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[英]How to access shared memory from SharedMemoryManager in python
[英]How can I access a python shared memory from cython?
我有兩個進程,一個創建共享 memory,第二個訪問它。 我正在使用 Python 3.8。
第二個進程可以選擇僅使用 python 函數或通過 cython 訪問共享的 memory。 cython 選項失敗 - 我得到一個 SIGSEGV。
是否有一些特殊的方法可以在 cython 中獲取共享的 memory? 文檔似乎沒有顯示如何實際獲得 python 共享 memory。
過程 1 (setup_shm.py):
from multiprocessing import shared_memory
import numpy as np
import argparse
from package.get_shm import main as get_shm
from multiprocessing import Process
def main(cython=None):
arr_share = np.array([[1.3424, 23.24324], [1.4234, .08682]], dtype='f')
shape, dtype = arr_share.shape, arr_share.dtype
# Create a shared memory of size arr_share.nbytes
shm = shared_memory.SharedMemory(create=True, size=arr_share.nbytes)
# Create array using the buffer of shm
shm_np_array = np.ndarray(shape=shape, dtype=dtype, buffer=shm.buf)
# Copy the data into the shared memory
np.copyto(shm_np_array, arr_share)
print('shared name {}'.format(shm.name))
print('shared size {}'.format(arr_share.nbytes))
print(arr_share)
p = Process(target=get_shm, args=(shm.name,), kwargs={'cython': cython})
p.start()
p.join()
shm.close()
shm.unlink()
def setup():
parser = argparse.ArgumentParser(description='Shared mem getter')
parser.add_argument('--cython', action='store_true', help='Use cython to get shared mem')
args = parser.parse_args()
main(cython=args.cython)
if __name__ == "__main__":
setup()
過程 2 (get_shm.py):
from multiprocessing.shared_memory import SharedMemory
import numpy as np
import argparse
import sys
from c_get_shm import Vectors
def main(name, cython=None):
val = None
if cython:
print('getting vector using cython - {}'.format(name))
vectors = Vectors(name)
try:
val = vectors.data.base[0]
except Exception as e:
raise (e)
else:
shm = SharedMemory(name=name)
np_array = np.ndarray(shape=(2, 2), dtype='f', buffer=shm.buf)
print('getting vector using python - {}'.format(name))
val = np_array[0]
shm.close()
print('val is {}'.format(val))
sys.stdout.flush()
return
def setup():
parser = argparse.ArgumentParser(description='Shared mem getter')
parser.add_argument('name', type=str, help='Shared memory name')
parser.add_argument('--cython', action='store_true', help='Use cython to get shared mem')
args = parser.parse_args()
main(name=args.name, cython=args.cython)
if __name__ == "__main__":
setup()
Cython class (c_get_shm.pyx)
#!python
#cython: language_level=3, boundscheck=False
from multiprocessing import shared_memory
cimport numpy as np
np.import_array()
cdef class Vectors:
cdef public object shm_name
cdef public float[:, :] data
def __init__(self, shm_name):
print('Vectors Class for getting shared memory - {}'.format(shm_name))
self.__get_shared_array(shm_name)
print('data is {}, {}'.format(self.data, self.data.shape))
def __get_shared_array(self, shm_name: str):
print('get_shared_array {}'.format(shm_name))
shm = shared_memory.SharedMemory(name=shm_name)
print('shm buf is {} size {}'.format(shm.buf, shm.buf.nbytes))
self.data = np.ndarray(shape=(2, 2), dtype='f', buffer=shm.buf)
安裝程序.py
from setuptools import setup, Extension
from Cython.Build import cythonize
extensions = [
Extension("c_get_shm", ["./c_get_shm.pyx"],
libraries=['rt'])
]
setup(
ext_modules=cythonize(extensions, gdb_debug=True)
)
我正在運行腳本
我設法看到 SIGSEGV 的唯一方法是使用 strace:
Vectors Class for getting shared memory - psm_23cd04df
get_shared_array psm_23cd04df
shm buf is <memory at 0x7f8b4d66df40> size 16
data is <MemoryView of 'ndarray' object>, [2, 2, 0, 0, 0, 0, 0, 0]
[{WIFSIGNALED(s) && WTERMSIG(s) == SIGSEGV && WCOREDUMP(s)}], 0, NULL) = 868
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_DUMPED, si_pid=868, si_uid=1000, si_status=SIGSEGV, si_utime=0, si_stime=0} ---
close(5) = 0
munmap(0x7f8b65698000, 16) = 0
close(4) = 0
unlink("/dev/shm/psm_23cd04df") = 0
我試過 gdb python 並從那里運行腳本,但沒有堆棧跟蹤。
對於遇到類似問題的任何人,請確保在 Cython 中聲明共享的 memory。 我假設 SIGSEGV 是因為它不允許訪問 memory 除非你這樣做。
cdef class Vectors:
cdef public object shm_name
cdef public float[:, :] data
# declare the shared memory in Cython!!
cdef public object shm
def __init__(self, shm_name):
self.__get_shared_array(shm_name)
def __get_shared_array(self, shm_name: str):
self.shm = shared_memory.SharedMemory(name=shm_name)
self.data = np.ndarray(shape=(2, 2), dtype='f', buffer=self.shm.buf)
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