Develop Reference

How could I stop the script without having to wait for the time set for interval to pass?

In this script I was looking to launch a given program and monitor it as long as the program exists. Thus, I reached the point where I got to use the threading's module Timer method for controlling a loop that writes to a file and prints out to the console a specific stat of the launched process (for this case, mspaint).
The problem arises when I'm hitting CTRL + C in the console or when I close mspaint, with the script capturing any of the 2 events only after the time defined for the interval has completely ran out. These events make the script stop.
For example, if a 20 seconds time is set for the interval, once the script has started, if at second 5 I either hit CTRL + C or close mspaint, the script will stop only after the remaining 15 seconds will have passed.
I would like for the script to stop right away when I either hit CTRL + C or close mspaint (or any other process launched through this script).
The script can be used with the following command, according to the example:
python.exe mon_tool.py -p "C:\Windows\System32\mspaint.exe" -i 20
I'd really appreciate if you could come up with a working example.
I had used python 3.10.4 and psutil 5.9.0 .
This is the code:
# mon_tool.py
import psutil, sys, os, argparse
from subprocess import Popen
from threading import Timer
debug = False
def parse_args(args):
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--path", type=str, required=True)
parser.add_argument("-i", "--interval", type=float, required=True)
return parser.parse_args(args)
def exceptionHandler(exception_type, exception, traceback, debug_hook=sys.excepthook):
'''Print user friendly error messages normally, full traceback if DEBUG on.
Adapted from http://stackoverflow.com/questions/27674602/hide-traceback-unless-a-debug-flag-is-set
'''
if debug:
print('\n*** Error:')
debug_hook(exception_type, exception, traceback)
else:
print("%s: %s" % (exception_type.__name__, exception))
sys.excepthook = exceptionHandler
def validate(data):
try:
if data.interval < 0:
raise ValueError
except ValueError:
raise ValueError(f"Time has a negative value: {data.interval}. Please use a positive value")
def main():
args = parse_args(sys.argv[1:])
validate(args)
# creates the "Process monitor data" folder in the "Documents" folder
# of the current Windows profile
default_path: str = f"{os.path.expanduser('~')}\\Documents\Process monitor data"
if not os.path.exists(default_path):
os.makedirs(default_path)
abs_path: str = f'{default_path}\data_test.txt'
print("data_test.txt can be found in: " + default_path)
# launches the provided process for the path argument, and
# it checks if the process was indeed launched
p: Popen[bytes] = Popen(args.path)
PID = p.pid
isProcess: bool = True
while isProcess:
for proc in psutil.process_iter():
if(proc.pid == PID):
isProcess = False
process_stats = psutil.Process(PID)
# creates the data_test.txt and it erases its content
with open(abs_path, 'w', newline='', encoding='utf-8') as testfile:
testfile.write("")
# loop for writing the handles count to data_test.txt, and
# for printing out the handles count to the console
def process_monitor_loop():
with open(abs_path, 'a', newline='', encoding='utf-8') as testfile:
testfile.write(f"{process_stats.num_handles()}\n")
print(process_stats.num_handles())
Timer(args.interval, process_monitor_loop).start()
process_monitor_loop()
if __name__ == '__main__':
main()
Thank you!

I think you could use python-worker (link) for the alternatives
import time
from datetime import datetime
from worker import worker, enableKeyboardInterrupt
# make sure to execute this before running the worker to enable keyboard interrupt
enableKeyboardInterrupt()
# your codes
...
# block lines with periodic check
def block_next_lines(duration):
t0 = time.time()
while time.time() - t0 <= duration:
time.sleep(0.05) # to reduce resource consumption
def main():
# your codes
...
#worker(keyboard_interrupt=True)
def process_monitor_loop():
while True:
print("hii", datetime.now().isoformat())
block_next_lines(3)
return process_monitor_loop()
if __name__ == '__main__':
main_worker = main()
main_worker.wait()
here your process_monitor_loop will be able to stop even if it's not exactly 20 sec of interval

You can try registering a signal handler for SIGINT, that way whenever the user presses Ctrl+C you can have a custom handler to clean all of your dependencies, like the interval, and exit gracefully.
See this for a simple implementation.

This is the solution for the second part of the problem, which checks if the launched process exists. If it doesn't exist, it stops the script.
This solution comes on top of the solution, for the first part of the problem, provided above by #danangjoyoo, which deals with stopping the script when CTRL + C is used.
Thank you very much once again, #danangjoyoo! :)
This is the code for the second part of the problem:
import time, psutil, sys, os
from datetime import datetime
from worker import worker, enableKeyboardInterrupt, abort_all_thread, ThreadWorkerManager
from threading import Timer
# make sure to execute this before running the worker to enable keyboard interrupt
enableKeyboardInterrupt()
# block lines with periodic check
def block_next_lines(duration):
t0 = time.time()
while time.time() - t0 <= duration:
time.sleep(0.05) # to reduce resource consumption
def main():
# launches mspaint, gets its PID and checks if it was indeed launched
path = f"C:\Windows\System32\mspaint.exe"
p = psutil.Popen(path)
PID = p.pid
isProcess: bool = True
while isProcess:
for proc in psutil.process_iter():
if(proc.pid == PID):
isProcess = False
interval = 5
global counter
counter = 0
#allows for sub_process to run only once
global run_sub_process_once
run_sub_process_once = 1
#worker(keyboard_interrupt=True)
def process_monitor_loop():
while True:
print("hii", datetime.now().isoformat())
def sub_proccess():
'''
Checks every second if the launched process still exists.
If the process doesn't exist anymore, the script will be stopped.
'''
print("Process online:", psutil.pid_exists(PID))
t = Timer(1, sub_proccess)
t.start()
global counter
counter += 1
print(counter)
# Checks if the worker thread is alive.
# If it is not alive, it will kill the thread spawned by sub_process
# hence, stopping the script.
for _, key in enumerate(ThreadWorkerManager.allWorkers):
w = ThreadWorkerManager.allWorkers[key]
if not w.is_alive:
t.cancel()
if not psutil.pid_exists(PID):
abort_all_thread()
t.cancel()
global run_sub_process_once
if run_sub_process_once:
run_sub_process_once = 0
sub_proccess()
block_next_lines(interval)
return process_monitor_loop()
if __name__ == '__main__':
main_worker = main()
main_worker.wait()
Also, I have to note that #danangjoyoo's solution comes as an alternative to signal.pause() for Windows. This only deals with CTRL + C problem part. signal.pause() works only for Unix systems. This is how it was supposed for its usage, for my case, in case it were a Unix system:
import signal, sys
from threading import Timer
def main():
def signal_handler(sig, frame):
print('\nYou pressed Ctrl+C!')
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)
print('Press Ctrl+C')
def process_monitor_loop():
try:
print("hi")
except KeyboardInterrupt:
signal.pause()
Timer(10, process_monitor_loop).start()
process_monitor_loop()
if __name__ == '__main__':
main()
The code above is based on this.

Terminante threads when main program exits

I am trying to get started with multithreading in Python. I have multiple threads that acquire a lock, perform an operation, release the lock, and output the result to a csv file. The threads should be terminated in case the main thread finishes (e.g. through pressing Ctrl+C). How is this done?
I thought making the threads daemons should do the job, because "the entire Python program exits when only daemon threads are left" (official Python documentation). This is not sufficient. According to python: how to terminate a thread when main program ends, I then tried catching a KeyboardInterrupt and then terminating the threads manually. Also this is not working, it seems that the KeyboardInterrupt is not catched correctly.
It makes me think, there is something about multithreading in Python that I misunderstood... Find attached my code:
import random
import csv
import sys
from datetime import datetime
import threading
lock = threading.Lock()
def observer(obs):
print("In " + str(obs))
sys.stdout.flush()
with open("data/test_" + str(obs) + ".csv", 'a', newline='') as csvfile:
while True:
datenow = datetime.today()
lock.acquire()
print(str(obs) + "acquired")
sum = 0
for i in range(10000):
sum = sum + random.random()
print(str(obs) + "released")
sys.stdout.flush()
lock.release()
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_ALL)
writer.writerow([datenow.isoformat(), sum/10000])
#print(str(obs) + ": " + datenow.isoformat() + " " + str(sum/1000))
sys.stdout.flush()
if __name__ == "__main__":
observe = [1, 2, 3, 4]
processes = []
for obs in observe:
process = threading.Thread(target=observer, args=(obs,), daemon=True)
processes.append(process)
print("Start processes")
for p in processes:
p.start()
print("Started")
try:
for p in processes:
p.join()
except KeyboardInterrupt:
for p in processes:
p.terminate()
print("Keyboard interrupt")
print("Finished")
Thanks!

This is wrong
try:
for p in processes:
p.join()
except KeyboardInterrupt:
for p in processes:
p.terminate()
print("Keyboard interrupt")
Your threads never exit the while True: loop, so you will be joining them forever. But that's not the point.
If Ctrl-C (aka KeyboardInterrupt) is not being caught my best bet:
You are running Python under Windows
The shell under which you run your script manipulates the processes and you actually never see the Ctrl-C because your program is being terminated abruptly.
You problem would then be:
Your program doesn't actually know it has terminated and that's why the threads hang.
If the assumptions made above are right, try this code (from Python - Windows - Exiting Child Process when "unrelated" parent dies/crashes)
import sys
def win_wait_for_parent(raise_exceptions=False):
if not sys.platform == 'win32':
return True
# When started under cygwin, the parent process will die leaving a child
# hanging around. The process has to be waited upon
import ctypes
from ctypes.wintypes import DWORD, BOOL, HANDLE
import os
import threading
INFINITE = -1
SYNCHRONIZE = 0x00100000
kernel32 = ctypes.WinDLL('kernel32', use_last_error=True)
kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
kernel32.OpenProcess.restype = HANDLE
kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
kernel32.WaitForSingleObject.restype = DWORD
phandle = kernel32.OpenProcess(SYNCHRONIZE, 0, os.getppid())
def check_parent():
# Get a token with right access to parent and wait for it to be
# signaled (die). Exit ourselves then
kernel32.WaitForSingleObject(phandle, INFINITE)
os._exit(0)
if not phandle:
if raise_exceptions:
raise ctypes.WinError(ctypes.get_last_error())
return False

Python - How to pass global variable to multiprocessing.Process?

I need to terminate some processes after a while, so I've used sleeping another process for the waiting. But the new process doesn't have access to global variables from the main process I guess. How could I solve it please?
Code:
import os
from subprocess import Popen, PIPE
import time
import multiprocessing
log_file = open('stdout.log', 'a')
log_file.flush()
err_file = open('stderr.log', 'a')
err_file.flush()
processes = []
def processing():
print "processing"
global processes
global log_file
global err_file
for i in range(0, 5):
p = Popen(['java', '-jar', 'C:\\Users\\two\\Documents\\test.jar'], stdout=log_file, stderr=err_file) # something long running
processes.append(p)
print len(processes) # returns 5
def waiting_service():
name = multiprocessing.current_process().name
print name, 'Starting'
global processes
print len(processes) # returns 0
time.sleep(2)
for i in range(0, 5):
processes[i].terminate()
print name, 'Exiting'
if __name__ == '__main__':
processing()
service = multiprocessing.Process(name='waiting_service', target=waiting_service)
service.start()

You should be using synchronization primitives.
Possibly you want to set an Event that's triggered after a while by the main (parent) process.
You may also want to wait for the processes to actually complete and join them (like you would a thread).
If you have many similar tasks, you can use a processing pool like multiprocessing.Pool.
Here is a small example of how it's done:
import multiprocessing
import time
kill_event = multiprocessing.Event()
def work(_id):
while not kill_event.is_set():
print "%d is doing stuff" % _id
time.sleep(1)
print "%d quit" % _id
def spawn_processes():
processes = []
# spawn 10 processes
for i in xrange(10):
# spawn process
process = multiprocessing.Process(target=work, args=(i,))
processes.append(process)
process.start()
time.sleep(1)
# kill all processes by setting the kill event
kill_event.set()
# wait for all processes to complete
for process in processes:
process.join()
print "done!"
spawn_processes()

The whole problem was in Windows' Python. Python for Windows is blocking global variables to be seen in functions. I've switched to linux and my script works OK.
Special thanks to #rchang for his comment:
When I tested it, in both cases the print statement came up with 5. Perhaps we have a version mismatch in some way? I tested it with Python 2.7.6 on Linux kernel 3.13.0 (Mint distribution).

Python Daemon Thread Clean Up Logic on Abrupt sys.exit()

Using Linux and Python 2.7.6, I have a script that uploads lots of files at one time. I am using multi-threading with the Queue and Threading modules.
I implemented a handler for SIGINT to stop the script if the user hits ctrl-C. I prefer to use daemon threads so I don't have to clear the queue, which would require alot of re-writing code to make the SIGINT handler have access to the Queue object since the handlers don't take parameters.
To make sure the daemon threads finish and clean up before sys.exit(), I am using threading.Event() and threading.clear() to make threads wait. This code seems to work as print threading.enumerate() only shows the main thread before the script terminates when I did debugging. Just to make sure, I was wondering if there is any kind of insight to this clean up implementation that I might be missing even though it seems to be working for me:
def signal_handler(signal, frame):
global kill_received
kill_received = True
msg = (
"\n\nYou pressed Ctrl+C!"
"\nYour logs and their locations are:"
"\n{}\n{}\n{}\n\n".format(debug, error, info))
logger.info(msg)
threads = threading.Event()
threads.clear()
while True:
time.sleep(3)
threads_remaining = len(threading.enumerate())
print threads_remaining
if threads_remaining == 1:
sys.exit()
def do_the_uploads(file_list, file_quantity,
retry_list, authenticate):
"""The uploading engine"""
value = raw_input(
"\nPlease enter how many concurent "
"uploads you want at one time(example: 200)> ")
value = int(value)
logger.info('{} concurent uploads will be used.'.format(value))
confirm = raw_input(
"\nProceed to upload files? Enter [Y/y] for yes: ").upper()
if confirm == "Y":
kill_received = False
sys.stdout.write("\x1b[2J\x1b[H")
q = CustomQueue()
def worker():
global kill_received
while not kill_received:
item = q.get()
upload_file(item, file_quantity, retry_list, authenticate, q)
q.task_done()
for i in range(value):
t = Thread(target=worker)
t.setDaemon(True)
t.start()
for item in file_list:
q.put(item)
q.join()
print "Finished. Cleaning up processes...",
#Allowing the threads to cleanup
time.sleep(4)
def upload_file(file_obj, file_quantity, retry_list, authenticate, q):
"""Uploads a file. One file per it's own thread. No batch style. This way if one upload
fails no others are effected."""
absolute_path_filename, filename, dir_name, token, url = file_obj
url = url + dir_name + '/' + filename
try:
with open(absolute_path_filename) as f:
r = requests.put(url, data=f, headers=header_collection, timeout=20)
except requests.exceptions.ConnectionError as e:
pass
if src_md5 == r.headers['etag']:
file_quantity.deduct()

If you want to handle Ctrl+C; it is enough to handle KeyboardInterrupt exception in the main thread. Don't use global X in a function unless you do X = some_value in it. Using time.sleep(4) to allow the threads to cleanup is a code smell. You don't need it.
I am using threading.Event() and threading.clear() to make threads wait.
This code has no effect on your threads:
# create local variable
threads = threading.Event()
# clear internal flag in it (that is returned by .is_set/.wait methods)
threads.clear()
Don't call logger.info() from a signal handler in a multithreaded program. It might deadlock your program. Only a limited set of functions can be called from a signal handler. The safe option is to set a global flag in it and exit:
def signal_handler(signal, frame):
global kill_received
kill_received = True
# return (no more code)
The signal might be delayed until q.join() returns. Even if the signal were delivered immediately; q.get() blocks your child threads. They hang until the main thread exits. To fix both issues, you could use a sentinel to signal child processes that there are no more work, drop the signal handler completely in this case:
def worker(stopped, queue, *args):
for item in iter(queue.get, None): # iterate until queue.get() returns None
if not stopped.is_set(): # a simple global flag would also work here
upload_file(item, *args)
else:
break # exit prematurely
# do child specific clean up here
# start threads
q = Queue.Queue()
stopped = threading.Event() # set when threads should exit prematurely
threads = set()
for _ in range(number_of_threads):
t = Thread(target=worker, args=(stopped, q)+other_args)
threads.add(t)
t.daemon = True
t.start()
# provide work
for item in file_list:
q.put(item)
for _ in threads:
q.put(None) # put sentinel to signal the end
while threads: # until there are alive child threads
try:
for t in threads:
t.join(.3) # use a timeout to get KeyboardInterrupt sooner
if not t.is_alive():
threads.remove(t) # remove dead
break
except (KeyboardInterrupt, SystemExit):
print("got Ctrl+C (SIGINT) or exit() is called")
stopped.set() # signal threads to exit gracefully
I've renamed value to number_of_threads. I've used explicit threads set
If an individual upload_file() blocks then the program won't exit on Ctrl-C.
Your case seems to be simple enough for multiprocessing.Pool interface:
from multiprocessing.pool import ThreadPool
from functools import partial
def do_uploads(number_of_threads, file_list, **kwargs_for_upload_file):
process_file = partial(upload_file, **kwargs_for_upload_file)
pool = ThreadPool(number_of_threads) # number of concurrent uploads
try:
for _ in pool.imap_unordered(process_file, file_list):
pass # you could report progress here
finally:
pool.close() # no more additional work
pool.join() # wait until current work is done
It should gracefully exit on Ctrl-C i.e., uploads that are in progress are allowed to finish but new uploads are not started.

Using Python's Multiprocessing module to execute simultaneous and separate SEAWAT/MODFLOW model runs

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