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Multiprocessing strange behaviour?

I am using multiprocessing from Python for testing purpose and there are somethings I don't understand.
A priori, processes have their own memory space so we can't share and Python class between them.
But look at my code:
import sys,
import time
from multiprocessing import Queue, Process
class MainClass():
def __init__(self, q):
self.q = q
print("Queue in Main", q)
def start_p(self):
p = Proc(self.q)
p.processing()
def run_p(self):
p = Process(target=self.start_p, args=())
p.start()
return p
class Proc():
def __init__(self, q):
self.q = q
def processing(self):
print("Queue in process", self.q)
n = ''
try:
n = self.q.get(0) # Get first item in the "queue"
except KeyError as e:
print("NOK", e)
print("GET: ", n)
print('Size: ', self.q.qsize())
if __name__ == "__main__":
# Creating a queue
q = Queue()
# Add 10 numbers in the queue
for i in range(0,10):
q.put(i)
# Add the queue to Main Class
s = MainClass(q)
print("Initial queue size", q.qsize())
# Starting 3 process
p1 = s.run_p()
p2 = s.run_p()
p3 = s.run_p()
#time.sleep(2)
print("Final queue size", q.qsize())
I have created a queue on main process, with 10 numbers.
Then, I ran 3 process so each one run a task consting of just getting (and delete) first item in a queue.
What I misunderstand is how can this program work and return a final queue 7 ?
It seems the queue is shared...but the object itself (multiprocessing) is located in different memory place...
But there is no "pointer" mecanism in python ?
The result when I run the programm below:
Behaviour is almost the same on linux except the memory adress is the same for all instances.
Please can someone explain me ?

Your basic premise is not entirely correct. While multiple processes indeed have their own memory space, that does not mean they cannot exchange data.
In fact there are multiple mechanisms for programs to share data. These are generally called "inter process communication" or IPC. For example;
shared memory
pipes
sockets
Under the covers, a multiprocessing.Queue uses a multiprocessing.Pipe.
This in turn is a Python wrapper for an operating system communication pipe. On ms-windows this is an operating system primitive called a named pipe. On many other operating systems pipes are based on file descriptors.
The point is that even when the Queue is inherited in the child processes, they are all connected to the same operating system object.
That is basically why they can communicate with each other.
(Note that this is of necessity a simplification; If you really want to know how it works, read the multiprocessing module Python code.)

How to call method from different class using multiprocess pool python

How do I call a method from a different class (different module) with the use of Multiprocess pool in python?
My aim is to start a process which keep running until some task is provide, and once task is completed it will again go back to waiting mode.
Below is code, which has three module, Reader class is my run time task, I will provide execution of reader method to ProcessExecutor.
Process executor is process pool, it will continue while loop until some task is provided to it.
Main module which initiates everything.
Module 1
class Reader(object):
def __init__(self, message):
self.message = message
def reader(self):
print self.message
Module 2
class ProcessExecutor():
def run(self, queue):
print 'Before while loop'
while True:
print 'Reached Run'
try:
pair = queue.get()
print 'Running process'
print pair
func = pair.get('target')
arguments = pair.get('args', None)
if arguments is None:
func()
else:
func(arguments)
queue.task_done()
except Exception:
print Exception.message
main Module
from process_helper import ProcessExecutor
from reader import Reader
import multiprocessing
import Queue
if __name__=='__main__':
queue = Queue.Queue()
myReader = Reader('Hi')
ps = ProcessExecutor()
pool = multiprocessing.Pool(2)
pool.apply_async(ps.run, args=(queue, ))
param = {'target': myReader.reader}
queue.put(param)
Code executed without any error: C:\Python27\python.exe
C:/Users/PycharmProjects/untitled1/main/main.py
Process finished with exit code 0
Code gets executed but it never reached to run method. I am not sure is it possible to call a method of the different class using multi-processes or not
I tried apply_async, map, apply but none of them are working.
All example searched online are calling target method from the script where the main method is implemented.
I am using python 2.7
Please help.

Your first problem is that you just exit without waiting on anything. You have a Pool, a Queue, and an AsyncResult, but you just ignore all of them and exit as soon as you've created them. You should be able to get away with only waiting on the AsyncResult (after that, there's no more work to do, so who cares what you abandon), except for the fact that you're trying to use Queue.task_done, which doesn't make any sense without a Queue.join on the other side, so you need to wait on that as well.
Your second problem is that you're using the Queue from the Queue module, instead of the one from the multiprocessing module. The Queue module only works across threads in the same process.
Also, you can't call task_done on a plain Queue; that's only a method for the JoinableQueue subclass.
Once you've gotten to the point where the pool tries to actually run a task, you will get the problem that bound methods can't be pickled unless you write a pickler for them. Doing that is a pain, even though it's the right way. The traditional workaround—hacky and cheesy, but everyone did it, and it works—is to wrap each method you want to call in a top-level function. The modern solution is to use the third-party dill or cloudpickle libraries, which know how to pickle bound methods, and how to hook into multiprocessing. You should definitely look into them. But, to keep things simple, I'll show you the workaround.
Notice that, because you've created an extra queue to pass methods onto, in addition to the one built into the pool, you'll need the workaround for both targets.
With these problems fixed, your code looks like this:
from process_helper import ProcessExecutor
from reader import Reader
import multiprocessing
def call_run(ps):
ps.run(queue)
def call_reader(reader):
return reader.reader()
if __name__=='__main__':
queue = multiprocessing.JoinableQueue()
myReader = Reader('Hi')
ps = ProcessExecutor()
pool = multiprocessing.Pool(2)
res = pool.apply_async(call_run, args=(ps,))
param = {'target': call_reader, 'args': myReader}
queue.put(param)
print res.get()
queue.join()
You have additional bugs beyond this in your ProcessReader, but I'm not going to debug everything for you. This gets you past the initial hurdles, and shows the answer to the specific question you were asking about. Also, I'm not sure what the point of all that code is. You seem to be trying to replace what Pool already does on top of Pool, only in a more complicated but less powerful way, but I'm not entirely sure.
Meanwhile, here's a program that does what I think you want, with no problems, by just throwing away that ProcessExecutor and everything that goes with it:
from reader import Reader
import multiprocessing
def call_reader(reader):
return reader.reader()
if __name__=='__main__':
myReader = Reader('Hi')
pool = multiprocessing.Pool(2)
res = pool.apply_async(call_reader, args=(myReader,))
print res.get()

Can I safely use global Queues when using multiprocessing in python?

I have a large codebase to parallelise. I can avoid rewriting the method signatures of hundreds of functions by using a single global queue. I know it's messy; please don't tell me that if I'm using globals I'm doing something wrong in this case it really is the easiest choice. The code below works but i don't understand why. I declare a global multiprocessing.Queue() but don't declare that it should be shared between processes (by passing it as a parameter to the worker). Does python automatically place this queue in shared memory? Is it safe to do this on a larger scale?
Note: You can tell that the queue is shared between the processes: the worker processes start doing work on empty queues and are idle for one second before the main queue pushes some work onto the queues.
import multiprocessing
import time
outqueue = None
class WorkerProcess(multiprocessing.Process):
def __init__(self):
multiprocessing.Process.__init__(self)
self.exit = multiprocessing.Event()
def doWork(self):
global outqueue
ob = outqueue.get()
ob = ob + "!"
print ob
time.sleep(1) #simulate more hard work
outqueue.put(ob)
def run(self):
while not self.exit.is_set():
self.doWork()
def shutdown(self):
self.exit.set()
if __name__ == '__main__':
global outqueue
outqueue = multiprocessing.Queue()
procs = []
for x in range(10):
procs.append(WorkerProcess())
procs[x].start()
time.sleep(1)
for x in range(20):
outqueue.put(str(x))
time.sleep(10)
for p in procs:
p.shutdown()
for p in procs:
p.join()
try:
while True:
x = outqueue.get(False)
print x
except:
print "done"

Assuming you're using Linux, the answer is in the way the OS creates a new process.
When a process spawns a new one in Linux, it actually forks the parent one. The result is a child process with all the properties of the parent one. Basically a clone.
In your example you are instantiating the Queue and then creating the new processes. Therefore the children processes will have a copy of the same queue and will be able to use it.
To see things broken just try to first create the processes and then creating the Queue object. You'll see the children having the global variable still set as None while the parent will have a Queue.
It is safe, yet not recommended, to share a Queue as a global variable on Linux. On Windows, due to the different process creation approach, sharing a queue through a global variable won't work.
As mentioned in the programming guidelines
Explicitly pass resources to child processes
On Unix using the fork start method, a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process.
Apart from making the code (potentially) compatible with Windows and the other start methods this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process.
For more info about Linux forking you can read its man page.

How to list Processes started by multiprocessing Pool?

While attempting to store multiprocessing's process instance in multiprocessing list-variable 'poolList` I am getting a following exception:
SimpleQueue objects should only be shared between processes through inheritance
The reason why I would like to store the PROCESS instances in a variable is to be able to terminate all or just some of them later (if for example a PROCESS freezes). If storing a PROCESS in variable is not an option I would like to know how to get or to list all the PROCESSES started by mutliprocessing POOL. That would be very similar to what .current_process() method does. Except .current_process gets only a single process while I need all the processes started or all the processes currently running.
Two questions:
Is it even possible to store an instance of the Process (as a result of mp.current_process()
Currently I am only able to get a single process from inside of the function that the process is running (from inside of myFunct() using .current_process() method).
Instead I would like to to list all the processes currently running by multiprocessing. How to achieve it?
import multiprocessing as mp
poolList=mp.Manager().list()
def myFunct(arg):
print 'myFunct(): current process:', mp.current_process()
try: poolList.append(mp.current_process())
except Exception, e: print e
for i in range(110):
for n in range(500000):
pass
poolDict[arg]=i
print 'myFunct(): completed', arg, poolDict
from multiprocessing import Pool
pool = Pool(processes=2)
myArgsList=['arg1','arg2','arg3']
pool=Pool(processes=2)
pool.map_async(myFunct, myArgsList)
pool.close()
pool.join()

To list the processes started by a Pool()-instance(which is what you mean if I understand you correctly), there is the pool._pool-list. And it contains the instances of the processes.
However, it is not part of the documented interface and hence, really should not be used.
BUT...it seems a little bit unlikely that it would change just like that anyway. I mean, should they stop having an internal list of processes in the pool? And not call that _pool?
And also, it annoys me that there at least isn't a get processes-method. Or something.
And handling it breaking due to some name change should not be that difficult.
But still, use at your own risk:
from multiprocessing import pool
# Have to run in main
if __name__ == '__main__':
# Create 3 worker processes
_my_pool = pool.Pool(3)
# Loop, terminate, and remove from the process list
# Use a copy [:] of the list to remove items correctly
for _curr_process in _my_pool._pool[:]:
print("Terminating process "+ str(_curr_process.pid))
_curr_process.terminate()
_my_pool._pool.remove(_curr_process)
# If you call _repopulate, the pool will again contain 3 worker processes.
_my_pool._repopulate_pool()
for _curr_process in _my_pool._pool[:]:
print("After repopulation "+ str(_curr_process.pid))
The example creates a pool and manually terminates all processes.
It is important that you remember to delete the process you terminate from the pool yourself i you want Pool() to continue working as usual.
_my_pool._repopulate increases the number of working processes to 3 again, not needed to answer the question, but gives a little bit of behind-the-scenes insight.

Yes you can get all active process and perform action based on name of process
e.g
multiprocessing.Process(target=foo, name="refresh-reports")
and then
for p in multiprocessing.active_children():
if p.name == "foo":
p.terminate()

You're creating a managed List object, but then letting the associated Manager object expire.
Process objects are shareable because they aren't pickle-able; that is, they aren't simple.
Oddly the multiprocessing module doesn't have the equivalent of threading.enumerate() -- that is, you can't list all outstanding processes. As a workaround, I just store procs in a list. I never terminate() a process, but do sys.exit(0) in the parent. It's rough, because the workers will leave things in an inconsistent state, but it's okay for smaller programs
To kill a frozen worker, I suggest: 1) worker receives "heartbeat" jobs in a queue every now and then, 2) if parent notices worker A hasn't responded to a heartbeat in a certain amount of time, then p.terminate(). Consider restating the problem in another SO question, as it's interesting.
To be honest the map stuff is much easier than using a Manager.
Here's a Manager example I've used. A worker adds stuff to a shared list. Another worker occasionally wakes up, processes everything on the list, then goes back to sleep. The code also has verbose logs, which are essential for ease in debugging.
source
# producer adds to fixed-sized list; scanner uses them
import logging, multiprocessing, sys, time
def producer(objlist):
'''
add an item to list every sec; ensure fixed size list
'''
logger = multiprocessing.get_logger()
logger.info('start')
while True:
try:
time.sleep(1)
except KeyboardInterrupt:
return
msg = 'ding: {:04d}'.format(int(time.time()) % 10000)
logger.info('put: %s', msg)
del objlist[0]
objlist.append( msg )
def scanner(objlist):
'''
every now and then, run calculation on objlist
'''
logger = multiprocessing.get_logger()
logger.info('start')
while True:
try:
time.sleep(5)
except KeyboardInterrupt:
return
logger.info('items: %s', list(objlist))
def main():
logger = multiprocessing.log_to_stderr(
level=logging.INFO
)
logger.info('setup')
# create fixed-length list, shared between producer & consumer
manager = multiprocessing.Manager()
my_objlist = manager.list( # pylint: disable=E1101
[None] * 10
)
multiprocessing.Process(
target=producer,
args=(my_objlist,),
name='producer',
).start()
multiprocessing.Process(
target=scanner,
args=(my_objlist,),
name='scanner',
).start()
logger.info('running forever')
try:
manager.join() # wait until both workers die
except KeyboardInterrupt:
pass
logger.info('done')
if __name__=='__main__':
main()

How to use multiprocessing with class instances in Python?

I am trying to create a class than can run a separate process to go do some work that takes a long time, launch a bunch of these from a main module and then wait for them all to finish. I want to launch the processes once and then keep feeding them things to do rather than creating and destroying processes. For example, maybe I have 10 servers running the dd command, then I want them all to scp a file, etc.
My ultimate goal is to create a class for each system that keeps track of the information for the system in which it is tied to like IP address, logs, runtime, etc. But that class must be able to launch a system command and then return execution back to the caller while that system command runs, to followup with the result of the system command later.
My attempt is failing because I cannot send an instance method of a class over the pipe to the subprocess via pickle. Those are not pickleable. I therefore tried to fix it various ways but I can't figure it out. How can my code be patched to do this? What good is multiprocessing if you can't send over anything useful?
Is there any good documentation of multiprocessing being used with class instances? The only way I can get the multiprocessing module to work is on simple functions. Every attempt to use it within a class instance has failed. Maybe I should pass events instead? I don't understand how to do that yet.
import multiprocessing
import sys
import re
class ProcessWorker(multiprocessing.Process):
"""
This class runs as a separate process to execute worker's commands in parallel
Once launched, it remains running, monitoring the task queue, until "None" is sent
"""
def __init__(self, task_q, result_q):
multiprocessing.Process.__init__(self)
self.task_q = task_q
self.result_q = result_q
return
def run(self):
"""
Overloaded function provided by multiprocessing.Process. Called upon start() signal
"""
proc_name = self.name
print '%s: Launched' % (proc_name)
while True:
next_task_list = self.task_q.get()
if next_task is None:
# Poison pill means shutdown
print '%s: Exiting' % (proc_name)
self.task_q.task_done()
break
next_task = next_task_list[0]
print '%s: %s' % (proc_name, next_task)
args = next_task_list[1]
kwargs = next_task_list[2]
answer = next_task(*args, **kwargs)
self.task_q.task_done()
self.result_q.put(answer)
return
# End of ProcessWorker class
class Worker(object):
"""
Launches a child process to run commands from derived classes in separate processes,
which sit and listen for something to do
This base class is called by each derived worker
"""
def __init__(self, config, index=None):
self.config = config
self.index = index
# Launce the ProcessWorker for anything that has an index value
if self.index is not None:
self.task_q = multiprocessing.JoinableQueue()
self.result_q = multiprocessing.Queue()
self.process_worker = ProcessWorker(self.task_q, self.result_q)
self.process_worker.start()
print "Got here"
# Process should be running and listening for functions to execute
return
def enqueue_process(target): # No self, since it is a decorator
"""
Used to place an command target from this class object into the task_q
NOTE: Any function decorated with this must use fetch_results() to get the
target task's result value
"""
def wrapper(self, *args, **kwargs):
self.task_q.put([target, args, kwargs]) # FAIL: target is a class instance method and can't be pickled!
return wrapper
def fetch_results(self):
"""
After all processes have been spawned by multiple modules, this command
is called on each one to retreive the results of the call.
This blocks until the execution of the item in the queue is complete
"""
self.task_q.join() # Wait for it to to finish
return self.result_q.get() # Return the result
#enqueue_process
def run_long_command(self, command):
print "I am running number % as process "%number, self.name
# In here, I will launch a subprocess to run a long-running system command
# p = Popen(command), etc
# p.wait(), etc
return
def close(self):
self.task_q.put(None)
self.task_q.join()
if __name__ == '__main__':
config = ["some value", "something else"]
index = 7
workers = []
for i in range(5):
worker = Worker(config, index)
worker.run_long_command("ls /")
workers.append(worker)
for worker in workers:
worker.fetch_results()
# Do more work... (this would actually be done in a distributor in another class)
for worker in workers:
worker.close()
Edit: I tried to move the ProcessWorker class and the creation of the multiprocessing queues outside of the Worker class and then tried to manually pickle the worker instance. Even that doesn't work and I get an error
RuntimeError: Queue objects should only be shared between processes
through inheritance
. But I am only passing references of those queues into the worker instance?? I am missing something fundamental. Here is the modified code from the main section:
if __name__ == '__main__':
config = ["some value", "something else"]
index = 7
workers = []
for i in range(1):
task_q = multiprocessing.JoinableQueue()
result_q = multiprocessing.Queue()
process_worker = ProcessWorker(task_q, result_q)
worker = Worker(config, index, process_worker, task_q, result_q)
something_to_look_at = pickle.dumps(worker) # FAIL: Doesn't like queues??
process_worker.start()
worker.run_long_command("ls /")

So, the problem was that I was assuming that Python was doing some sort of magic that is somehow different from the way that C++/fork() works. I somehow thought that Python only copied the class, not the whole program into a separate process. I seriously wasted days trying to get this to work because all of the talk about pickle serialization made me think that it actually sent everything over the pipe. I knew that certain things could not be sent over the pipe, but I thought my problem was that I was not packaging things up properly.
This all could have been avoided if the Python docs gave me a 10,000 ft view of what happens when this module is used. Sure, it tells me what the methods of multiprocess module does and gives me some basic examples, but what I want to know is what is the "Theory of Operation" behind the scenes! Here is the kind of information I could have used. Please chime in if my answer is off. It will help me learn.
When you run start a process using this module, the whole program is copied into another process. But since it is not the "__main__" process and my code was checking for that, it doesn't fire off yet another process infinitely. It just stops and sits out there waiting for something to do, like a zombie. Everything that was initialized in the parent at the time of calling multiprocess.Process() is all set up and ready to go. Once you put something in the multiprocess.Queue or shared memory, or pipe, etc. (however you are communicating), then the separate process receives it and gets to work. It can draw upon all imported modules and setup just as if it was the parent. However, once some internal state variables change in the parent or separate process, those changes are isolated. Once the process is spawned, it now becomes your job to keep them in sync if necessary, either through a queue, pipe, shared memory, etc.
I threw out the code and started over, but now I am only putting one extra function out in the ProcessWorker, an "execute" method that runs a command line. Pretty simple. I don't have to worry about launching and then closing a bunch of processes this way, which has caused me all kinds of instability and performance issues in the past in C++. When I switched to launching processes at the beginning and then passing messages to those waiting processes, my performance improved and it was very stable.
BTW, I looked at this link to get help, which threw me off because the example made me think that methods were being transported across the queues: http://www.doughellmann.com/PyMOTW/multiprocessing/communication.html
The second example of the first section used "next_task()" that appeared (to me) to be executing a task received via the queue.

Instead of attempting to send a method itself (which is impractical), try sending a name of a method to execute.
Provided that each worker runs the same code, it's a matter of a simple getattr(self, task_name).
I'd pass tuples (task_name, task_args), where task_args were a dict to be directly fed to the task method:
next_task_name, next_task_args = self.task_q.get()
if next_task_name:
task = getattr(self, next_task_name)
answer = task(**next_task_args)
...
else:
# poison pill, shut down
break

REF: https://stackoverflow.com/a/14179779
Answer on Jan 6 at 6:03 by David Lynch is not factually correct when he says that he was misled by
http://www.doughellmann.com/PyMOTW/multiprocessing/communication.html.
The code and examples provided are correct and work as advertised. next_task() is executing a task received via the queue -- try and understand what the Task.__call__() method is doing.
In my case what, tripped me up was syntax errors in my implementation of run(). It seems that the sub-process will not report this and just fails silently -- leaving things stuck in weird loops! Make sure you have some kind of syntax checker running e.g. Flymake/Pyflakes in Emacs.
Debugging via multiprocessing.log_to_stderr()F helped me narrow down the problem.