Below is my demo:
Thread xxx:
import xxx
import xxxx
def test_process1(hostname,proxy_param):
# just never run
try: # breakpoint 0
with open("/xxx","w+") as f: # breakpoint 1
f.write("something")
except Exception as e:
pass # just never run breakpoint 3
def test():
try:
a = Process(target=test_process1, args=(hostname,proxy_param))
a.start()
a.join() # you are blocking here. test_process1 not working and never quit
except Exception as e:
pass # breakpoint 4
function test_process1 just never run. No error, No breakpoint.
The test function code is in a big project, here is a demo.
Hope! this piece of code helps.
Workers list will get divided based on the number of processes in use.
Sample Code with ManagerList.
from subprocess import PIPE, Popen
from multiprocessing import Pool,Pipe
from multiprocessing import Process, Queue, Manager
def child_process(child_conn,output_list,messenger):
input_recvd = messenger["input"]
output_list.append(input_recvd)
print(input_recvd)
child_conn.close()
def parent_process(number_of_process=2):
workers_inputs = [{"input":"hello"}, {"input":"world"}]
with Manager() as manager:
processes = []
output_list = manager.list() # <-- can be shared between processes.
parent_conn, child_conn = Pipe()
for single_id_dict in workers_inputs:
pro_obj = Process(target=child_process, args=(child_conn,output_list,single_id_dict)) # Passing the list
pro_obj.start()
processes.append(pro_obj)
for p in processes:
p.join()
output_list = [single_feature for single_feature in output_list]
return output_list
parent_process()
OUTPUT:
hello
world
['hello', 'world']
ManagerList is useful to get the output from various parallel process it's like an inbuilt Queue Mechanism with easy to use and safe from deadlocks.
Related
I have a python script which calls a series of sub-processes. They need to run "for ever" - but they occasionally die, or get killed. When this happens I need to restart the process using the same arguments as the one which died.
This is a very simplified version:
[edit: this is the less simplified version, which includes "restart" code]
import multiprocessing
import time
import random
def printNumber(number):
print("starting :", number)
while random.randint(0, 5) > 0:
print(number)
time.sleep(2)
if __name__ == '__main__':
children = [] # list
args = {} # dictionary
for processNumber in range(10,15):
p = multiprocessing.Process(
target=printNumber,
args=(processNumber,)
)
children.append(p)
p.start()
args[p.pid] = processNumber
while True:
time.sleep(1)
for n, p in enumerate(children):
if not p.is_alive():
#get parameters dead child was started with
pidArgs = args[p.pid]
del(args[p.pid])
print("n,args,p: ",n,pidArgs,p)
children.pop(n)
# start new process with same args
p = multiprocessing.Process(
target=printNumber,
args=(pidArgs,)
)
children.append(p)
p.start()
args[p.pid] = pidArgs
I have updated the example to illustrate how I want the processes to be restarted if one crashes/killed/etc - keeping track of which pid was started with which args.
Is this the "best" way to do this, or is there a more "python" way of doing this?
I think I would create a separate thread for each Process and use a ProcessPoolExecutor. Executors have a useful function, submit, which returns a Future. You can wait on each Future and re-launch the Executor when the Future is done. Arguments to the function are tracked as class variables, so restarting is just a simple loop.
import threading
from concurrent.futures import ProcessPoolExecutor
import time
import random
import traceback
def printNumber(number):
print("starting :", number)
while random.randint(0, 5) > 0:
print(number)
time.sleep(2)
class KeepRunning(threading.Thread):
def __init__(self, func, *args, **kwds):
self.func = func
self.args = args
self.kwds = kwds
super().__init__()
def run(self):
while True:
with ProcessPoolExecutor(max_workers=1) as pool:
future = pool.submit(self.func, *self.args, **self.kwds)
try:
future.result()
except Exception:
traceback.print_exc()
if __name__ == '__main__':
for process_number in range(10, 15):
keep = KeepRunning(printNumber, process_number)
keep.start()
while True:
time.sleep(1)
At the end of the program is a loop to keep the main thread running. Without that, the program will attempt to exit while your Processes are still running.
For the example you provided I would just remove the exit condition from the while loop and change it to True.
As you said though the actual code is more complicated (why didn't you post that?). So if the process gets terminated by lets say an exception just put the code inside a try catch block. You can then put said block in an infinite loop.
I hope this is what you are looking for but that seems to be the right way to do it provided the goal and information you provided.
Instead of just starting the process immediately, you can save the list of processes and their arguments, and create another process that checks they are alive.
For example:
if __name__ == '__main__':
process_list = []
for processNumber in range(5):
process = multiprocessing.Process(
target=printNumber,
args=(processNumber,)
)
process_list.append((process,args))
process.start()
while True:
for running_process, process_args in process_list:
if not running_process.is_alive():
new_process = multiprocessing.Process(target=printNumber, args=(process_args))
process_list.remove(running_process, process_args) # Remove terminated process
process_list.append((new_process, process_args))
I must say that I'm not sure the best way to do it is in python, you may want to look at scheduler services like jenkins or something like that.
here is a simple example:
from collections import deque
from multiprocessing import Process
global_dequeue = deque([])
def push():
global_dequeue.append('message')
p = Process(target=push)
p.start()
def pull():
print(global_dequeue)
pull()
the output is deque([])
if I was to call push function directly, not as a separate process, the output would be deque(['message'])
How can get the message into deque, but still run push function in a separate process?
You can share data by using multiprocessing Queue object which is designed to share data between processes:
from multiprocessing import Process, Queue
import time
def push(q): # send Queue to function as argument
for i in range(10):
q.put(str(i)) # put element in Queue
time.sleep(0.2)
q.put("STOP") # put poison pillow to stop taking elements from Queue in master
if __name__ == "__main__":
q = Queue() # create Queue instance
p = Process(target=push, args=(q,),) # create Process
p.start() # start it
while True:
x = q.get()
if x == "STOP":
break
print(x)
p.join() # join process to our master process and continue master run
print("Finish")
Let me know if it helped, feel free to ask questions.
You can also use Managers to achieve this.
Python 2: https://docs.python.org/2/library/multiprocessing.html#managers
Python 3:https://docs.python.org/3.8/library/multiprocessing.html#managers
Example of usage:
https://pymotw.com/2/multiprocessing/communication.html#managing-shared-state
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 have problems with python multiprocessing
python version 3.6.6
using Spyder IDE on windows 7
1.
queue is not being populated -> everytime I try to read it, its empty. Somewhere I read, that I have to get() it before process join() but it did not solve it.
from multiprocessing import Process,Queue
# define a example function
def fnc(i, output):
output.put(i)
if __name__ == '__main__':
# Define an output queue
output = Queue()
# Setup a list of processes that we want to run
processes = [Process(target=fnc, args=(i, output)) for i in range(4)]
print('created')
# Run processes
for p in processes:
p.start()
print('started')
# Exit the completed processes
for p in processes:
p.join()
print(output.empty())
print('finished')
>>>created
>>>started
>>>True
>>>finished
I would expect output to not be empty.
if I change it from .join() to
for p in processes:
print(output.get())
#p.join()
it freezes
2.
Next problem I have is with pool.map() - it freezes and has no chance to exceed memory limit. I dont even know how to debug such simple pieace of code.
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4)
print('Pool created')
# print "[0, 1, 4,..., 81]"
print(pool.map(f, range(10))) # it freezes here
Hope its not a big deal to have two questions in one topic
Apperently the problem is Spyder's IPython console. When I run both from cmd, its executed properly.
Solution
for debugging in Spyder add .dummy to multiprocessing import
from multiprocessing.dummy import Process,Queue
It will not be executed by more processors, but you will get results and can actualy see the output. When debugging is done simply delete .dummy, place it in another file, import it and call it for example as function
multiprocessing_my.py
from multiprocessing import Process,Queue
# define a example function
def fnc(i, output):
output.put(i)
print(i)
def test():
# Define an output queue
output = Queue()
# Setup a list of processes that we want to run
processes = [Process(target=fnc, args=(i, output)) for i in range(4)]
print('created')
# Run processes
for p in processes:
p.start()
print('started')
# Exit the completed processes
for p in processes:
p.join()
print(output.empty())
print('finished')
# Get process results from the output queue
results = [output.get() for p in processes]
print('get results')
print(results)
test_mp.py
executed by selecting code and pressing ctrl+Enter
import multiprocessing_my
multiprocessing_my.test2()
...
In[9]: test()
created
0
1
2
3
started
False
finished
get results
[0, 1, 2, 3]
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