I'm running two python threads (import threading). Both of them are blocked on a open() call; in fact they try to open named pipes in order to write in them, so it's a normal behaviour to block until somebody try to read from the named pipe.
In short, it looks like:
import threading
def f():
open('pipe2', 'r')
if __name__ == '__main__':
t = threading.Thread(target=f)
t.start()
open('pipe1', 'r')
When I type a ^C, the open() in the main thread is interrupted (raises IOError with errno == 4).
My problem is: the t threads still waits, and I'd like to propagate the interruption behaviour, in order to make it raise IOError too.
I found this in python docs:
"
... only the main thread can set a new signal handler, and the main thread will be the only one to receive signals (this is enforced by the Python signal module, even if the underlying thread implementation supports sending signals to individual threads). This means that signals can’t be used as a means of inter-thread communication. Use locks instead.
"
Maybe you should also check these docs:
exceptions.KeyboardInterrupt
library/signal.html
One other idea is to use select to read the pipe asynchronously in the threads. This works in Linux, not sure about Windows (it's not the cleanest, nor the best implementation):
#!/usr/bin/python
import threading
import os
import select
def f():
f = os.fdopen(os.open('pipe2', os.O_RDONLY|os.O_NONBLOCK))
finput = [ f ]
foutput = []
# here the pipe is scanned and whatever gets in will be printed out
# ...as long as 'getout' is False
while finput and not getout:
fread, fwrite, fexcep = select.select(finput, foutput, finput)
for q in fread:
if q in finput:
s = q.read()
if len(s) > 0:
print s
if __name__ == '__main__':
getout = False
t = threading.Thread(target=f)
t.start()
try:
open('pipe1', 'r')
except:
getout = True
Related
Right now, I'm using subprocess to run a long-running job in the background. For multiple reasons (PyInstaller + AWS CLI) I can't use subprocess anymore.
Is there an easy way to achieve the same thing as below ? Running a long running python function in a multiprocess pool (or something else) and do real time processing of stdout/stderr ?
import subprocess
process = subprocess.Popen(
["python", "long-job.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
)
while True:
out = process.stdout.read(2000).decode()
if not out:
err = process.stderr.read().decode()
else:
err = ""
if (out == "" or err == "") and process.poll() is not None:
break
live_stdout_process(out)
Thanks
getting it cross platform is messy .... first of all windows implementation of non-blocking pipe is not user friendly or portable.
one option is to just have your application read its command line arguments and conditionally execute a file, and you get to use subprocess since you will be launching yourself with different argument.
but to keep it to multiprocessing :
the output must be logged to queues instead of pipes.
you need the child to execute a python file, this can be done using runpy to execute the file as __main__.
this runpy function should run under a multiprocessing child, this child must first redirect its stdout and stderr in the initializer.
when an error happens, your main application must catch it .... but if it is too busy reading the output it won't be able to wait for the error, so a child thread has to start the multiprocess and wait for the error.
the main process has to create the queues and launch the child thread and read the output.
putting it all together:
import multiprocessing
from multiprocessing import Queue
import sys
import concurrent.futures
import threading
import traceback
import runpy
import time
class StdoutQueueWrapper:
def __init__(self,queue:Queue):
self._queue = queue
def write(self,text):
self._queue.put(text)
def flush(self):
pass
def function_to_run():
# runpy.run_path("long-job.py",run_name="__main__") # run long-job.py
print("hello") # print something
raise ValueError # error out
def initializer(stdout_queue: Queue,stderr_queue: Queue):
sys.stdout = StdoutQueueWrapper(stdout_queue)
sys.stderr = StdoutQueueWrapper(stderr_queue)
def thread_function(child_stdout_queue,child_stderr_queue):
with concurrent.futures.ProcessPoolExecutor(1, initializer=initializer,
initargs=(child_stdout_queue, child_stderr_queue)) as pool:
result = pool.submit(function_to_run)
try:
result.result()
except Exception as e:
child_stderr_queue.put(traceback.format_exc())
if __name__ == "__main__":
child_stdout_queue = multiprocessing.Queue()
child_stderr_queue = multiprocessing.Queue()
child_thread = threading.Thread(target=thread_function,args=(child_stdout_queue,child_stderr_queue),daemon=True)
child_thread.start()
while True:
while not child_stdout_queue.empty():
var = child_stdout_queue.get()
print(var,end='')
while not child_stderr_queue.empty():
var = child_stderr_queue.get()
print(var,end='')
if not child_thread.is_alive():
break
time.sleep(0.01) # check output every 0.01 seconds
Note that a direct consequence of running as a multiprocess is that if the child runs into a segmentation fault or some unrecoverable error the parent will also die, hencing running yourself under subprocess might seem a better option if segfaults are expected.
I'm working on a Python launcher which should execute a few programs in my list by calling subprocess. The code is correct, but it works very strangely.
In short, it doesn't work without some sleep or input command in main.
Here is the example:
import threading
import subprocess
import time
def executeFile(file_path):
subprocess.call(file_path, shell=True)
def main():
file = None
try:
file = open('./config.ini', 'r');
except:
# TODO: add alert widget
print("cant find a file")
pathes = [ path.strip() for path in file.readlines() ]
try:
for idx in range(len(pathes)):
print(pathes[idx])
file_path = pathes[idx];
newThread = threading.Thread(target=executeFile, args=(file_path,))
newThread.daemon = True
newThread.start()
except:
print("cant start thread")
if __name__ == '__main__':
main()
# IT WORKS WHEN SLEEP EXISTS
time.sleep(10)
# OR
# input("Press enter to exit ;)")
but without input or sleep it doesn't work:
if __name__ == '__main__':
# Doesn't work
main()
Could someone explain me, please, why it happens?
I have some idea but I'm not sure. Maybe it's because subprocess is asynchronyous and the program executes and closes itself BEFORE the subprocess execution.
In case of sleep and input, the program suspends and subprocess has enough time to execute.
Thanks for any help!
As soon as the last thread is started, your main() returns. That in turn will exit your Python program. That stops all your threads.
From the documentation on daemon threads:
Note: Daemon threads are abruptly stopped at shutdown. Their resources (such as open files, database transactions, etc.) may not be released properly. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an Event.
The simple fix would be to not use daemon threads.
As an aside, I would suggest some changes to your loop. First, iterate over pathes directly instead of using indices. Second; catch errors for each thread seperately, so one error doesn't leave remaining files unprocessed.
for path in pathes:
try:
print(path)
newThread = threading.Thread(target=executeFile, args=(path,))
newThread.start()
except:
print("cant start thread for", path)
Another option would be to skip threads entirely, and just maintain a list of running subprocesses:
import os
import subprocess
import time
def manageprocs(proclist):
"""Check a list of subprocesses for processes that have
ended and remove them from the list.
:param proclist: list of Popen objects
"""
for pr in proclist:
if pr.poll() is not None:
proclist.remove(pr)
# since manageprocs is called from a loop,
# keep CPU usage down.
time.sleep(0.5)
def main():
# Read config file
try:
with open('./config.ini', 'r') as f:
pathes = [path.strip() for path in f.readlines()]
except FileNotFoundError:
print("cant find config file")
exit(1)
# List of subprocesses
procs = []
# Do not launch more processes concurrently than your
# CPU has cores. That will only lead to the processes
# fighting over CPU resources.
maxprocs = os.cpu_count()
# Launch all subprocesses.
for path in pathes:
while len(procs) == maxprocs:
manageprocs(procs)
procs.append(subprocess.Popen(path, shell=True))
# Wait for all subprocesses to finish.
while len(procs) > 0:
manageprocs(procs)
if __name__ == '__main__':
main()
I have long running process, that I want to keep track about in which state it currently is in. There is N processes running in same time therefore multiprocessing issue.
I pass Queue into process to report messages about state, and this Queue is then read(if not empty) in thread every couple of second.
I'm using Spider on windows as environment and later described behavior is in its console. I did not try it in different env.
from multiprocessing import Process,Queue,Lock
import time
def test(process_msg: Queue):
try:
process_msg.put('Inside process message')
# process...
return # to have exitstate = 0
except Exception as e:
process_msg.put(e)
callback_msg = Queue()
if __name__ == '__main__':
p = Process(target = test,
args = (callback_msg,))
p.start()
time.sleep(5)
print(p)
while not callback_msg.empty():
msg = callback_msg.get()
if type(msg) != Exception:
tqdm.write(str(msg))
else:
raise msg
Problem is that whatever I do with code, it never reads what is inside the Queue(also because it never puts anything in it). Only when I switch to dummy version, which runs similary to threading on only 1 CPU from multiprocessing.dummy import Process,Queue,Lock
Apparently the test function have to be in separate file.
I'm trying to complete 100 model runs on my 8-processor 64-bit Windows 7 machine. I'd like to run 7 instances of the model concurrently to decrease my total run time (approx. 9.5 min per model run). I've looked at several threads pertaining to the Multiprocessing module of Python, but am still missing something.
Using the multiprocessing module
How to spawn parallel child processes on a multi-processor system?
Python Multiprocessing queue
My Process:
I have 100 different parameter sets I'd like to run through SEAWAT/MODFLOW to compare the results. I have pre-built the model input files for each model run and stored them in their own directories. What I'd like to be able to do is have 7 models running at a time until all realizations have been completed. There needn't be communication between processes or display of results. So far I have only been able to spawn the models sequentially:
import os,subprocess
import multiprocessing as mp
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
files = []
for f in os.listdir(ws + r'\fieldgen\reals'):
if f.endswith('.npy'):
files.append(f)
## def work(cmd):
## return subprocess.call(cmd, shell=False)
def run(f,def_param=ws):
real = f.split('_')[2].split('.')[0]
print 'Realization %s' % real
mf2k = r'c:\modflow\mf2k.1_19\bin\mf2k.exe '
mf2k5 = r'c:\modflow\MF2005_1_8\bin\mf2005.exe '
seawatV4 = r'c:\modflow\swt_v4_00_04\exe\swt_v4.exe '
seawatV4x64 = r'c:\modflow\swt_v4_00_04\exe\swt_v4x64.exe '
exe = seawatV4x64
swt_nam = ws + r'\reals\real%s\ss\ss.nam_swt' % real
os.system( exe + swt_nam )
if __name__ == '__main__':
p = mp.Pool(processes=mp.cpu_count()-1) #-leave 1 processor available for system and other processes
tasks = range(len(files))
results = []
for f in files:
r = p.map_async(run(f), tasks, callback=results.append)
I changed the if __name__ == 'main': to the following in hopes it would fix the lack of parallelism I feel is being imparted on the above script by the for loop. However, the model fails to even run (no Python error):
if __name__ == '__main__':
p = mp.Pool(processes=mp.cpu_count()-1) #-leave 1 processor available for system and other processes
p.map_async(run,((files[f],) for f in range(len(files))))
Any and all help is greatly appreciated!
EDIT 3/26/2012 13:31 EST
Using the "Manual Pool" method in #J.F. Sebastian's answer below I get parallel execution of my external .exe. Model realizations are called up in batches of 8 at a time, but it doesn't wait for those 8 runs to complete before calling up the next batch and so on:
from __future__ import print_function
import os,subprocess,sys
import multiprocessing as mp
from Queue import Queue
from threading import Thread
def run(f,ws):
real = f.split('_')[-1].split('.')[0]
print('Realization %s' % real)
seawatV4x64 = r'c:\modflow\swt_v4_00_04\exe\swt_v4x64.exe '
swt_nam = ws + r'\reals\real%s\ss\ss.nam_swt' % real
subprocess.check_call([seawatV4x64, swt_nam])
def worker(queue):
"""Process files from the queue."""
for args in iter(queue.get, None):
try:
run(*args)
except Exception as e: # catch exceptions to avoid exiting the
# thread prematurely
print('%r failed: %s' % (args, e,), file=sys.stderr)
def main():
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
wdir = os.path.join(ws, r'fieldgen\reals')
q = Queue()
for f in os.listdir(wdir):
if f.endswith('.npy'):
q.put_nowait((os.path.join(wdir, f), ws))
# start threads
threads = [Thread(target=worker, args=(q,)) for _ in range(8)]
for t in threads:
t.daemon = True # threads die if the program dies
t.start()
for _ in threads: q.put_nowait(None) # signal no more files
for t in threads: t.join() # wait for completion
if __name__ == '__main__':
mp.freeze_support() # optional if the program is not frozen
main()
No error traceback is available. The run() function performs its duty when called upon a single model realization file as with mutiple files. The only difference is that with multiple files, it is called len(files) times though each of the instances immediately closes and only one model run is allowed to finish at which time the script exits gracefully (exit code 0).
Adding some print statements to main() reveals some information about active thread-counts as well as thread status (note that this is a test on only 8 of the realization files to make the screenshot more manageable, theoretically all 8 files should be run concurrently, however the behavior continues where they are spawn and immediately die except one):
def main():
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
wdir = os.path.join(ws, r'fieldgen\test')
q = Queue()
for f in os.listdir(wdir):
if f.endswith('.npy'):
q.put_nowait((os.path.join(wdir, f), ws))
# start threads
threads = [Thread(target=worker, args=(q,)) for _ in range(mp.cpu_count())]
for t in threads:
t.daemon = True # threads die if the program dies
t.start()
print('Active Count a',threading.activeCount())
for _ in threads:
print(_)
q.put_nowait(None) # signal no more files
for t in threads:
print(t)
t.join() # wait for completion
print('Active Count b',threading.activeCount())
**The line which reads "D:\\Data\\Users..." is the error information thrown when I manually stop the model from running to completion. Once I stop the model running, the remaining thread status lines get reported and the script exits.
EDIT 3/26/2012 16:24 EST
SEAWAT does allow concurrent execution as I've done this in the past, spawning instances manually using iPython and launching from each model file folder. This time around, I'm launching all model runs from a single location, namely the directory where my script resides. It looks like the culprit may be in the way SEAWAT is saving some of the output. When SEAWAT is run, it immediately creates files pertaining to the model run. One of these files is not being saved to the directory in which the model realization is located, but in the top directory where the script is located. This is preventing any subsequent threads from saving the same file name in the same location (which they all want to do since these filenames are generic and non-specific to each realization). The SEAWAT windows were not staying open long enough for me to read or even see that there was an error message, I only realized this when I went back and tried to run the code using iPython which directly displays the printout from SEAWAT instead of opening a new window to run the program.
I am accepting #J.F. Sebastian's answer as it is likely that once I resolve this model-executable issue, the threading code he has provided will get me where I need to be.
FINAL CODE
Added cwd argument in subprocess.check_call to start each instance of SEAWAT in its own directory. Very key.
from __future__ import print_function
import os,subprocess,sys
import multiprocessing as mp
from Queue import Queue
from threading import Thread
import threading
def run(f,ws):
real = f.split('_')[-1].split('.')[0]
print('Realization %s' % real)
seawatV4x64 = r'c:\modflow\swt_v4_00_04\exe\swt_v4x64.exe '
cwd = ws + r'\reals\real%s\ss' % real
swt_nam = ws + r'\reals\real%s\ss\ss.nam_swt' % real
subprocess.check_call([seawatV4x64, swt_nam],cwd=cwd)
def worker(queue):
"""Process files from the queue."""
for args in iter(queue.get, None):
try:
run(*args)
except Exception as e: # catch exceptions to avoid exiting the
# thread prematurely
print('%r failed: %s' % (args, e,), file=sys.stderr)
def main():
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
wdir = os.path.join(ws, r'fieldgen\reals')
q = Queue()
for f in os.listdir(wdir):
if f.endswith('.npy'):
q.put_nowait((os.path.join(wdir, f), ws))
# start threads
threads = [Thread(target=worker, args=(q,)) for _ in range(mp.cpu_count()-1)]
for t in threads:
t.daemon = True # threads die if the program dies
t.start()
for _ in threads: q.put_nowait(None) # signal no more files
for t in threads: t.join() # wait for completion
if __name__ == '__main__':
mp.freeze_support() # optional if the program is not frozen
main()
I don't see any computations in the Python code. If you just need to execute several external programs in parallel it is sufficient to use subprocess to run the programs and threading module to maintain constant number of processes running, but the simplest code is using multiprocessing.Pool:
#!/usr/bin/env python
import os
import multiprocessing as mp
def run(filename_def_param):
filename, def_param = filename_def_param # unpack arguments
... # call external program on `filename`
def safe_run(*args, **kwargs):
"""Call run(), catch exceptions."""
try: run(*args, **kwargs)
except Exception as e:
print("error: %s run(*%r, **%r)" % (e, args, kwargs))
def main():
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
workdir = os.path.join(ws, r'fieldgen\reals')
files = ((os.path.join(workdir, f), ws)
for f in os.listdir(workdir) if f.endswith('.npy'))
# start processes
pool = mp.Pool() # use all available CPUs
pool.map(safe_run, files)
if __name__=="__main__":
mp.freeze_support() # optional if the program is not frozen
main()
If there are many files then pool.map() could be replaced by for _ in pool.imap_unordered(safe_run, files): pass.
There is also mutiprocessing.dummy.Pool that provides the same interface as multiprocessing.Pool but uses threads instead of processes that might be more appropriate in this case.
You don't need to keep some CPUs free. Just use a command that starts your executables with a low priority (on Linux it is a nice program).
ThreadPoolExecutor example
concurrent.futures.ThreadPoolExecutor would be both simple and sufficient but it requires 3rd-party dependency on Python 2.x (it is in the stdlib since Python 3.2).
#!/usr/bin/env python
import os
import concurrent.futures
def run(filename, def_param):
... # call external program on `filename`
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
wdir = os.path.join(ws, r'fieldgen\reals')
files = (os.path.join(wdir, f) for f in os.listdir(wdir) if f.endswith('.npy'))
# start threads
with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
future_to_file = dict((executor.submit(run, f, ws), f) for f in files)
for future in concurrent.futures.as_completed(future_to_file):
f = future_to_file[future]
if future.exception() is not None:
print('%r generated an exception: %s' % (f, future.exception()))
# run() doesn't return anything so `future.result()` is always `None`
Or if we ignore exceptions raised by run():
from itertools import repeat
... # the same
# start threads
with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
executor.map(run, files, repeat(ws))
# run() doesn't return anything so `map()` results can be ignored
subprocess + threading (manual pool) solution
#!/usr/bin/env python
from __future__ import print_function
import os
import subprocess
import sys
from Queue import Queue
from threading import Thread
def run(filename, def_param):
... # define exe, swt_nam
subprocess.check_call([exe, swt_nam]) # run external program
def worker(queue):
"""Process files from the queue."""
for args in iter(queue.get, None):
try:
run(*args)
except Exception as e: # catch exceptions to avoid exiting the
# thread prematurely
print('%r failed: %s' % (args, e,), file=sys.stderr)
# start threads
q = Queue()
threads = [Thread(target=worker, args=(q,)) for _ in range(8)]
for t in threads:
t.daemon = True # threads die if the program dies
t.start()
# populate files
ws = r'D:\Data\Users\jbellino\Project\stJohnsDeepening\model\xsec_a'
wdir = os.path.join(ws, r'fieldgen\reals')
for f in os.listdir(wdir):
if f.endswith('.npy'):
q.put_nowait((os.path.join(wdir, f), ws))
for _ in threads: q.put_nowait(None) # signal no more files
for t in threads: t.join() # wait for completion
Here is my way to maintain the minimum x number of threads in the memory. Its an combination of threading and multiprocessing modules. It may be unusual to other techniques like respected fellow members have explained above BUT may be worth considerable. For the sake of explanation, I am taking a scenario of crawling a minimum of 5 websites at a time.
so here it is:-
#importing dependencies.
from multiprocessing import Process
from threading import Thread
import threading
# Crawler function
def crawler(domain):
# define crawler technique here.
output.write(scrapeddata + "\n")
pass
Next is threadController function. This function will control the flow of threads to the main memory. It will keep activating the threads to maintain the threadNum "minimum" limit ie. 5. Also it won't exit until, all Active threads(acitveCount) are finished up.
It will maintain a minimum of threadNum(5) startProcess function threads (these threads will eventually start the Processes from the processList while joining them with a time out of 60 seconds). After staring threadController, there would be 2 threads which are not included in the above limit of 5 ie. the Main thread and the threadController thread itself. thats why threading.activeCount() != 2 has been used.
def threadController():
print "Thread count before child thread starts is:-", threading.activeCount(), len(processList)
# staring first thread. This will make the activeCount=3
Thread(target = startProcess).start()
# loop while thread List is not empty OR active threads have not finished up.
while len(processList) != 0 or threading.activeCount() != 2:
if (threading.activeCount() < (threadNum + 2) and # if count of active threads are less than the Minimum AND
len(processList) != 0): # processList is not empty
Thread(target = startProcess).start() # This line would start startThreads function as a seperate thread **
startProcess function, as a separate thread, would start Processes from the processlist. The purpose of this function (**started as a different thread) is that It would become a parent thread for Processes. So when It will join them with a timeout of 60 seconds, this would stop the startProcess thread to move ahead but this won't stop threadController to perform. So this way, threadController will work as required.
def startProcess():
pr = processList.pop(0)
pr.start()
pr.join(60.00) # joining the thread with time out of 60 seconds as a float.
if __name__ == '__main__':
# a file holding a list of domains
domains = open("Domains.txt", "r").read().split("\n")
output = open("test.txt", "a")
processList = [] # thread list
threadNum = 5 # number of thread initiated processes to be run at one time
# making process List
for r in range(0, len(domains), 1):
domain = domains[r].strip()
p = Process(target = crawler, args = (domain,))
processList.append(p) # making a list of performer threads.
# starting the threadController as a seperate thread.
mt = Thread(target = threadController)
mt.start()
mt.join() # won't let go next until threadController thread finishes.
output.close()
print "Done"
Besides maintaining a minimum number of threads in the memory, my aim was to also have something which could avoid stuck threads or processes in the memory. I did this using the time out function.
My apologies for any typing mistake.
I hope this construction would help anyone in this world.
Regards,
Vikas Gautam
I have this Python based service daemon which is doing a lot of multiplexed IO (select).
From another script (also Python) I want to query this service daemon about status/information and/or control the processing (e.g. pause it, shut it down, change some parameters, etc).
What is the best way to send control messages ("from now on you process like this!") and query processed data ("what was the result of that?") using python?
I read somewhere that named pipes might work, but don't know that much about named pipes, especially in python - and whether there are any better alternatives.
Both the background service daemon AND the frontend will be programmed by me, so all options are open :)
I am using Linux.
Pipes and Named pipes are good solution to communicate between different processes.
Pipes work like shared memory buffer but has an interface that mimics a simple file on each of two ends. One process writes data on one end of the pipe, and another reads that data on the other end.
Named pipes are similar to above , except that this pipe is actually associated with a real file in your computer.
More details at
http://www.softpanorama.org/Scripting/pipes.shtml
In Python, named pipe files are created with the os.mkfifo call
x = os.mkfifo(filename)
In child and parent open this pipe as file
out = os.open(filename, os.O_WRONLY)
in = open(filename, 'r')
To write
os.write(out, 'xxxx')
To read
lines = in.readline( )
Edit: Adding links from SO
Create a temporary FIFO (named pipe) in Python?
https://stackoverflow.com/search?q=python+named+pipes
You may want to read more on "IPC and Python"
http://www.freenetpages.co.uk/hp/alan.gauld/tutipc.htm
The best way to do IPC is using message Queue in python as bellow
server process server.py (run this before running client.py and interact.py)
from multiprocessing.managers import BaseManager
import Queue
queue1 = Queue.Queue()
queue2 = Queue.Queue()
class QueueManager(BaseManager): pass
QueueManager.register('get_queue1', callable=lambda:queue1)
QueueManager.register('get_queue2', callable=lambda:queue2)
m = QueueManager(address=('', 50000), authkey='abracadabra')
s = m.get_server()
s.serve_forever()
The inter-actor which is for I/O interact.py
from multiprocessing.managers import BaseManager
import threading
import sys
class QueueManager(BaseManager): pass
QueueManager.register('get_queue1')
QueueManager.register('get_queue2')
m = QueueManager(address=('localhost', 50000),authkey='abracadabra')
m.connect()
queue1 = m.get_queue1()
queue2 = m.get_queue2()
def read():
while True:
sys.stdout.write(queue2.get())
def write():
while True:
queue1.put(sys.stdin.readline())
threads = []
threadr = threading.Thread(target=read)
threadr.start()
threads.append(threadr)
threadw = threading.Thread(target=write)
threadw.start()
threads.append(threadw)
for thread in threads:
thread.join()
The client program Client.py
from multiprocessing.managers import BaseManager
import sys
import string
import os
class QueueManager(BaseManager): pass
QueueManager.register('get_queue1')
QueueManager.register('get_queue2')
m = QueueManager(address=('localhost', 50000), authkey='abracadabra')
m.connect()
queue1 = m.get_queue1()
queue2 = m.get_queue2()
class RedirectOutput:
def __init__(self, stdout):
self.stdout = stdout
def write(self, s):
queue2.put(s)
class RedirectInput:
def __init__(self, stdin):
self.stdin = stdin
def readline(self):
return queue1.get()
# redirect standard output
sys.stdout = RedirectOutput(sys.stdout)
sys.stdin = RedirectInput(sys.stdin)
# The test program which will take input and produce output
Text=raw_input("Enter Text:")
print "you have entered:",Text
def x():
while True:
x= raw_input("Enter 'exit' to end and some thing else to continue")
print x
if 'exit' in x:
break
x()
this can be used to communicate between two process in network or on same machine
remember that inter-actor and server process will not terminate until you manually kill it.