from multiprocessing import Process

def func(s):
print(s)

if __name__ == '__main__':
p = Process(target=func, args=('autofelix', ))
p.start()
p.join()

from multiprocessing import Process

def func(s):
print(s)

if __name__ == '__main__':
process = [
Process(target=func, args=('1', ))
Process(target=func, args=('2', ))
]

[p.start() for p in process]
[p.join() for p in process]

from multiprocessing import Process, Pipe

def func(conn):
print('send a list object ot other side...')
# 從管道對(duì)象的一端發(fā)送數(shù)據(jù)對(duì)象
conn.send(['33', 44, None])
conn.close()

if __name__ == '__main__':
# 默認(rèn)創(chuàng)建一個(gè)雙工管道對(duì)象，返回的兩個(gè)對(duì)象代表管道的兩端，
# 雙工表示兩端的對(duì)象都可以發(fā)送和接收數(shù)據(jù)，但是需要注意，
# 需要避免多個(gè)進(jìn)程或線程從一端同時(shí)讀或?qū)憯?shù)據(jù)
parent_conn, child_conn = Pipe()
p = Process(target=func, args=(child_conn, ))
p.start()
# 從管道的另一端接收數(shù)據(jù)對(duì)象
print(parent_conn.recv())
p.join()

from multiprocessing import Process, Queue

def func(q):
  print('put a list object to queue...')
  # 向Queue對(duì)象中添加一個(gè)對(duì)象
  q.put(['33', 44, None])

if __name__ == '__main__':
  # 創(chuàng)建一個(gè)隊(duì)列
  q = Queue()
  p = Process(target=func, args=(q, ))
  p.start()
  # 從Queue對(duì)象中獲取一個(gè)對(duì)象
  print(q.get())
  p.join()
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者autofelix的原創(chuàng)作品，謝絕轉(zhuǎn)載，否則將追究法律責(zé)任
python 包之 multiprocessing 多進(jìn)程教程
https://blog.51cto.com/autofelix/5166197

from multiprocessing import Process, Lock

def func(lc, num):
# 使用鎖保證以下代碼同一時(shí)間只有一個(gè)進(jìn)程在執(zhí)行
lc.acquire()
print('process num: ', num)
lc.release()

if __name__ == '__main__':
lock = Lock()
for i in range(5):
Process(target=func, args=(lock, i)).start()

from multiprocessing import Process, Value, Array

def func(n, a):
n.value = 3.333
for i in range(len(a)):
a[i] = -a[i]

if __name__ == '__main__':
# 第一個(gè)參數(shù)d表示數(shù)據(jù)類型'double'雙精度浮點(diǎn)類型
num = Value('d', 0.0)
# 第一個(gè)參數(shù)i表示數(shù)據(jù)類型'integer'整型
arr = Array('i', range(6))
p = Process(target=func, args=(num, arr))
p.start()
p.join()
print(num.value)
print(arr[:])

from multiprocessing import Pool
import time

def f(x):
return x * x

if __name__ == '__main__':
with Pool(processes=4) as pool: # start 4 worker processes
# 在進(jìn)程池中開(kāi)啟一個(gè)新的進(jìn)程并執(zhí)行 f 函數(shù)
result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process
print(result.get(timeout=1)) # prints "100" unless your computer is *very* slow

# map會(huì)一直阻塞當(dāng)前進(jìn)程直到運(yùn)行完可迭代對(duì)象中的所有元素，并返回結(jié)果。
print(pool.map(f, range(10))) # prints "[0, 1, 4,..., 81]"

# imap是map方法的延遲執(zhí)行版本，對(duì)于比較消耗內(nèi)存的迭代，建議使用這個(gè)方法，
it = pool.imap(f, range(10))
print(next(it)) # prints "0"
print(next(it)) # prints "1"
print(it.next(timeout=1)) # prints "4" unless your computer is *very* slow

result = pool.apply_async(time.sleep, (10,))
print(result.get(timeout=1)) # raises multiprocessing.TimeoutError