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Organizing the contents/outputs of Python's multiprocessing queue

I'm writing a script that processes several different instances of a Class object, which contains a number of attributes and methods. The objects are all placed in a single list (myobjects = [myClass(IDnumber=1), myClass(IDnumber=2), myClass(IDnumber=3)], and then modified by fairly simplistic for loops that call specific functions from the objects, of the form
for x in myobjects:
x.myfunction()
This script utilizes logging, to forward all output to a logfile that I can check later. I'm attempting to parallelize this script, because it's fairly straightforward to do so (example below), and need to utilize a queue in order to organize all the logging outputs from each Process. This aspect works flawlessly- I can define a new logfile for each process, and then pass the object-specific logfile back to my main script, which can then organize the main logfile by appending each minor logfile in turn.
from multiprocessing import Process, Queue
q = Queue()
threads = []
mainlog = 'mylogs.log' #this is set up in my __init__.py but included here as demonstration
for x in myobjects:
logfile = x.IDnumber+'.log'
thread = Process(target=x.myfunction(), args=(logfile, queue))
threads.append(thread)
thread.start()
for thread in threads:
if thread.is_alive():
thread.join()
while not queue.empty():
minilog = queue.get()
minilog_open = open(minilog, 'r')
mainlog_open = open(mainlog, 'a+')
mainlog_open.write(minilog_open.read())
My problem, now, is that I also need these objects to update a specific attribute, x.success, as True or False. Normally, in serial, x.success is updated at the end of x.myfunction() and is sent down the script where it needs to go, and everything works great. However, in this parallel implementation, x.myfunction populates x.success in the Process, but that information never makes it back to the main script- so if I add print(success) inside myfunction(), I see True or False, but if I add for x in myobjects: print(x.success) after the queue.get() block, I just see None. I realize that I can just use queue.put(success) in myfunction() the same way I use queue.put(logfile), but what happens when two or more processes finish simultaneously? There's no guarantee (that I know of) that my queue will be organized like
logfile (for myobjects[0])
success = True (for myobjects[0])
logfile (for myobjects[1])
success = False (for myobjects[1]) (etc etc)
How can I organize object-specific outputs from a queue, if this queue contains both logfiles and variables? I need to know the content of x.success for each x.myfunction(), so that information has to come back to the main process somehow.

OP has request an example to demonstrate concepts mentioned in my comment. Explanation follows the code:-
import concurrent.futures
class MyObject:
def __init__(self):
self._ID = str(id(self))
self._status = None
#property
def ID(self):
return self._ID
#property
def status(self):
return self._status
#status.setter
def status(self, status):
self._status = status
def MyFunction(self):
# do the real work here
self.status = True
def MyThreadFunc(args):
myObject = args[0]
myObject.MyFunction()
# note that the wrapper function returns a tuple
return myObject.status, myObject.ID
if __name__ == '__main__':
N = 10 # number of instances of MyObject
myObjects = [MyObject() for _ in range(N)]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = {executor.submit(MyThreadFunc, [o]): o for o in myObjects}
for future in concurrent.futures.as_completed(futures):
_status, _id = future.result()
print(f'Status is {_status} for ID {_id}')
The class MyObject obviously doesn't do very much. The key features are that it has a string version of its id, a status and a function that does something but implicitly returns None.
We write a wrapper function that takes a reference to an instance of MyObject (first element in the iterable args), executes MyFunction() on that particular class instance then return that class's ID and status as a tuple.
The main loop uses a pattern that I use a lot and I'm sure many others do too. Using a dictionary comprehension, we build the so-called "futures". Remember that the second argument to submit() must be an iterable even though MyThreadFunc only needs one value.
We then wait for the threads to complete and get their return values.

Use multiprocessing to run functions inside a while loop in a class method

I have a method which calculates a final result using multiple other methods. It has a while loop inside which continuously checks for new data, and if new data is received, it runs the other methods and calculates the results. This main method is the only one which is called by the user, and it stays active until the program is closed. the basic structure is as follows:
class sample:
def __init__(self):
results = []
def main_calculation(self):
while True:
#code to get data
if newdata != olddata:
#insert code to prepare data for analysis
res1 = self.calc1(prepped_data)
res2 = self.calc2(prepped_data)
final = res1 + res2
self.results.append(final)
I want to run calc1 and calc2 in parallel, so that I can get the final result faster. However, I am unsure of how to implement multiprocessing in this way, since I'm not using a __main__ guard. Is there any way to run these processes in parallel?
This is likely not the best organization for this code, but it is what is easiest for the actual calculations I am running, since it is necessary that this code be imported and run from a different file. However, I can restructure the code if this is not a salvageable structure.

According to the documentation, the reason you need to use a __main__ guard is that when your program creates a multiprocessing.Process object, it starts up a whole new copy of the Python interpreter which will import a new copy of your program's modules. If importing your module calls multiprocessing.Process() itself, that will create yet another copy of the Python interpreter which interprets yet another copy of your code, and so on until your system crashes (or actually, until Python hits a non-reentrant piece of the multiprocessing code).
In the main module of your program, which usually calls some code at the top level, checking __name__ == '__main__' is the way you can tell whether the program is being run for the first time or is being run as a subprocess. But in a different module, there might not be any code at the top level (other than definitions), and in that case there's no need to use a guard because the module can be safely imported without starting a new process.
In other words, this is dangerous:
import multiprocessing as mp
def f():
...
p = mp.Process(target=f)
p.start()
p.join()
but this is safe:
import multiprocessing as mp
def f():
...
def g():
p = mp.Process(target=f)
p.start()
p.join()
and this is also safe:
import multiprocessing as mp
def f():
...
class H:
def g(self):
p = mp.Process(target=f)
p.start()
p.join()
So in your example, you should be able to directly create Process objects in your function.
However, I'd suggest making it clear in the documentation for the class that that method creates a Process, because whoever uses it (maybe you) needs to know that it's not safe to call that method at the top level of a module. It would be like doing this, which also falls in the "dangerous" category:
import multiprocessing as mp
def f():
...
class H:
def g(self):
p = mp.Process(target=f)
p.start()
p.join()
H().g() # this creates a Process at the top level
You could also consider an alternative approach where you make the caller do all the process creation. In this approach, either your sample class constructor or the main_calculation() method could accept, say, a Pool object, and it can use the processes from that pool to do its calculations. For example:
class sample:
def main_calculation(self, pool):
while True:
if newdata != olddata:
res1_async = pool.apply_async(self.calc1, [prepped_data])
res2_async = pool.apply_async(self.calc2, [prepped_data])
res1 = res1_async.get()
res2 = res2_async.get()
# and so on
This pattern may also allow your program to be more efficient in its use of resources, if there are many different calculations happening, because they can all use the same pool of processes.

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()

Running multiple independent python scripts concurrently

My goal is create one main python script that executes multiple independent python scripts in windows server 2012 at the same time. One of the benefits in my mind is that I can point taskscheduler to one main.py script as opposed to multiple .py scripts. My server has 1 cpu. I have read on multiprocessing,thread & subprocess which only added to my confusion a bit. I am basically running multiple trading scripts for different stock symbols all at the same time after market open at 9:30 EST. Following is my attempt but I have no idea whether this is right. Any direction/feedback is highly appreciated!
import subprocess
subprocess.Popen(["python", '1.py'])
subprocess.Popen(["python", '2.py'])
subprocess.Popen(["python", '3.py'])
subprocess.Popen(["python", '4.py'])

I think I'd try to do this like that:
from multiprocessing import Pool
def do_stuff_with_stock_symbol(symbol):
return _call_api()
if __name__ == '__main__':
symbols = ["GOOG", "APPL", "TSLA"]
p = Pool(len(symbols))
results = p.map(do_stuff_with_stock_symbol, symbols)
print(results)
(Modified example from multiprocessing introduction: https://docs.python.org/3/library/multiprocessing.html#introduction)
Consider using a constant pool size if you deal with a lot of stock symbols, because every python process will use some amount of memory.
Also, please note that using threads might be a lot better if you are dealing with an I/O bound workload (calling an API, writing and reading from disk). Processes really become necessary with python when dealing with compute bound workloads (because of the global interpreter lock).
An example using threads and the concurrent futures library would be:
import concurrent.futures
TIMEOUT = 60
def do_stuff_with_stock_symbol(symbol):
return _call_api()
if __name__ == '__main__':
symbols = ["GOOG", "APPL", "TSLA"]
with concurrent.futures.ThreadPoolExecutor(max_workers=len(symbols)) as executor:
results = {executor.submit(do_stuff_with_stock_symbol, symbol, TIMEOUT): symbol for symbol in symbols}
for future in concurrent.futures.as_completed(results):
symbol = results[future]
try:
data = future.result()
except Exception as exc:
print('{} generated an exception: {}'.format(symbol, exc))
else:
print('stock symbol: {}, result: {}'.format(symbol, data))
(Modified example from: https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example)
Note that threads will still use some memory, but less than processes.
You could use asyncio or green threads if you want to reduce memory consumption per stock symbol to a minimum, but at some point you will run into network bandwidth problems because of all the concurrent API calls :)

While what you're asking might not be the best way to handle what you're doing, I've wanted to do similar things in the past and it took a while to find what I needed so to answer your question:
I'm not promising this to be the "best" way to do it, but it worked in my use case.
I created a class I wanted to use to extend threading.
thread.py
"""
Extends threading.Thread giving access to a Thread object which will accept
A thread_id, thread name, and a function at the time of instantiation. The
function will be called when the threads start() method is called.
"""
import threading
class Thread(threading.Thread):
def __init__(self, thread_id, name, func):
threading.Thread.__init__(self)
self.threadID = thread_id
self.name = name
# the function that should be run in the thread.
self.func = func
def run(self):
return self.func()
I needed some work done that was part of another package
work_module.py
import...
def func_that_does_work():
# do some work
pass
def more_work():
# do some work
pass
Then the main script I wanted to run
main.py
from thread import Thread
import work_module as wm
mythreads = []
mythreads.append(Thread(1, "a_name", wm.func_that_does_work))
mythreads.append(Thread(2, "another_name", wm.more_work))
for t in mythreads:
t.start()
The threads die when the run() is returned. Being this extends a Thread from threading there are several options available in the docs here: https://docs.python.org/3/library/threading.html

If all you're looking to do is automate the startup, creating a .bat file is a great and simple alternative to trying to do it with another python script.
the example linked in the comments shows how to do it with bash on unix based machines, but batch files can do a very similar thing with the START command:
start_py.bat:
START "" /B "path\to\python.exe" "path\to\script_1.py"
START "" /B "path\to\python.exe" "path\to\script_2.py"
START "" /B "path\to\python.exe" "path\to\script_3.py"
the full syntax for START can be found here.

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.