Python Multi-process

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1. Process Class

 from Import Process def func (name):     Print ('hello', name) if __name__ ' __main__ ' :     = Process (Target=func, args= ('bob',))    P.start ()    p.join ()

2. Context and Start method

Start method

ImportMultiprocessing as MPdeffoo (q): Q.put ('Hello')if __name__=='__main__': Mp.set_start_method ('Spawn') Q=MP. Queue () P= MP. Process (Target=foo, args=(q,)) P.start ()Print(Q.get ()) P.join ()

Context

ImportMultiprocessing as MPdeffoo (q): Q.put ('Hello')if __name__=='__main__': CTX= Mp.get_context ('Spawn') Q=CTX. Queue () P= CTX. Process (Target=foo, args=(q,)) P.start ()Print(Q.get ()) P.join ()

3. Inter-Process Exchange objects

Queues

 from  multiprocessing import   Process, Queue  def   func (q): Q.put ([ , None, "  hello   ")  if   = =  " __main__   '   p.join () 

Pipes

 fromMultiprocessingImportProcess, Pipedeffunc (conn): Conn.send ([, None,'Hello']) conn.close ()if __name__=='__main__': Parent_conn, Child_conn=Pipe () p= Process (Target=func, args=(Child_conn,)) P.start ()Print(Parent_conn.recv ())#prints "[All, None, ' hello ']"P.join ()

4. Synchronization between processes

For example, you can use a lock to ensure that only one process prints to the standard output

 fromMultiprocessingImportProcess, Lockdeffunc (lock, I): Lock.acquire ()Try:        Print('Hello World', i)finally: Lock.release ()if __name__=='__main__': Lock=Lock () forNuminchRange (10): Process (Target=func, args= (lock, Num)). Start ()

5. Sharing status between processes

Shared Memory

With Value or Array, data can be stored in a shared memory map. For example, the following code

 fromMultiprocessingImportProcess, Value, Arraydeff (N, a): N.value= 3.1415927 forIinchRange (len (a)): A[i]= -A[i]if __name__=='__main__': Num= Value ('D', 0.0) Arr= Array ('I', Range (10)) P= Process (Target=f, args=(num, arr)) P.start () P.join ()Print(Num.value)Print(arr[:])

The parameter ' d ' represents a double-precision floating-point number, and the parameter "I" represents a signed integer. These shared objects are process-and thread-safe.

Server process

Through Manager (), returns a Manager object that controls the service process that holds the Python object and allows other processes to manipulate them using a proxy.

The types of manager objects that are returned by the manager () are:List,Dict,Namespace,Lock,Rlock,Semaphore,Boundedsemaphore,Condition,Event,Barrier,Queue,ValueandArray.

 fromMultiprocessingImportProcess, ManagerdefF (D, L): d[1] ='1'd['2'] = 2d[0.25] =None l.reverse ()if __name__=='__main__': With Manager () as Manager:d=manager.dict () L= Manager.list (Range (10)) P= Process (Target=f, args=(d, L)) P.start () P.join ( )Print(d)Print(l)

The service process Manager is more flexible than using shared memory objects because they can support arbitrary object types. Similarly, a manager can share the process over the network on different computers. However, they are slower than using shared memory.

6. Using Process Pools

 fromMultiprocessingImportPool fromTimeImportSleepdeff (x):returnx*xif __name__=='__main__':    #Start 4 worker processesWith Pool (processes=4) as Pool:#print "[0, 1, 4,..., Bayi]"        Print(Pool.map (F, Range (10)))        #Print same numbers in arbitrary order         forIinchPool.imap_unordered (F, Range (10)):            Print(i)#evaluate "F (Ten)" asynchronouslyres = Pool.apply_async (f, [10])        Print(Res.get (Timeout=1))#Prints "        #Make worker sleep for ten secsres = Pool.apply_async (sleep, [10])        Print(Res.get (Timeout=1))#raises multiprocessing. Timeouterror    #exiting the ' with '-block have stopped the pool

Python Multi-process