Related
There is a socket related function call in my code, that function is from another module thus out of my control, the problem is that it blocks for hours occasionally, which is totally unacceptable, How can I limit the function execution time from my code? I guess the solution must utilize another thread.
An improvement on #rik.the.vik's answer would be to use the with statement to give the timeout function some syntactic sugar:
import signal
from contextlib import contextmanager
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds)
try:
yield
finally:
signal.alarm(0)
try:
with time_limit(10):
long_function_call()
except TimeoutException as e:
print("Timed out!")
I'm not sure how cross-platform this might be, but using signals and alarm might be a good way of looking at this. With a little work you could make this completely generic as well and usable in any situation.
http://docs.python.org/library/signal.html
So your code is going to look something like this.
import signal
def signal_handler(signum, frame):
raise Exception("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(10) # Ten seconds
try:
long_function_call()
except Exception, msg:
print "Timed out!"
Here's a Linux/OSX way to limit a function's running time. This is in case you don't want to use threads, and want your program to wait until the function ends, or the time limit expires.
from multiprocessing import Process
from time import sleep
def f(time):
sleep(time)
def run_with_limited_time(func, args, kwargs, time):
"""Runs a function with time limit
:param func: The function to run
:param args: The functions args, given as tuple
:param kwargs: The functions keywords, given as dict
:param time: The time limit in seconds
:return: True if the function ended successfully. False if it was terminated.
"""
p = Process(target=func, args=args, kwargs=kwargs)
p.start()
p.join(time)
if p.is_alive():
p.terminate()
return False
return True
if __name__ == '__main__':
print run_with_limited_time(f, (1.5, ), {}, 2.5) # True
print run_with_limited_time(f, (3.5, ), {}, 2.5) # False
I prefer a context manager approach because it allows the execution of multiple python statements within a with time_limit statement. Because windows system does not have SIGALARM, a more portable and perhaps more straightforward method could be using a Timer
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception):
def __init__(self, msg=''):
self.msg = msg
#contextmanager
def time_limit(seconds, msg=''):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
raise TimeoutException("Timed out for operation {}".format(msg))
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
import time
# ends after 5 seconds
with time_limit(5, 'sleep'):
for i in range(10):
time.sleep(1)
# this will actually end after 10 seconds
with time_limit(5, 'sleep'):
time.sleep(10)
The key technique here is the use of _thread.interrupt_main to interrupt the main thread from the timer thread. One caveat is that the main thread does not always respond to the KeyboardInterrupt raised by the Timer quickly. For example, time.sleep() calls a system function so a KeyboardInterrupt will be handled after the sleep call.
Here: a simple way of getting the desired effect:
https://pypi.org/project/func-timeout
This saved my life.
And now an example on how it works: lets say you have a huge list of items to be processed and you are iterating your function over those items. However, for some strange reason, your function get stuck on item n, without raising an exception. You need to other items to be processed, the more the better. In this case, you can set a timeout for processing each item:
import time
import func_timeout
def my_function(n):
"""Sleep for n seconds and return n squared."""
print(f'Processing {n}')
time.sleep(n)
return n**2
def main_controller(max_wait_time, all_data):
"""
Feed my_function with a list of itens to process (all_data).
However, if max_wait_time is exceeded, return the item and a fail info.
"""
res = []
for data in all_data:
try:
my_square = func_timeout.func_timeout(
max_wait_time, my_function, args=[data]
)
res.append((my_square, 'processed'))
except func_timeout.FunctionTimedOut:
print('error')
res.append((data, 'fail'))
continue
return res
timeout_time = 2.1 # my time limit
all_data = range(1, 10) # the data to be processed
res = main_controller(timeout_time, all_data)
print(res)
Doing this from within a signal handler is dangerous: you might be inside an exception handler at the time the exception is raised, and leave things in a broken state. For example,
def function_with_enforced_timeout():
f = open_temporary_file()
try:
...
finally:
here()
unlink(f.filename)
If your exception is raised here(), the temporary file will never be deleted.
The solution here is for asynchronous exceptions to be postponed until the code is not inside exception-handling code (an except or finally block), but Python doesn't do that.
Note that this won't interrupt anything while executing native code; it'll only interrupt it when the function returns, so this may not help this particular case. (SIGALRM itself might interrupt the call that's blocking--but socket code typically simply retries after an EINTR.)
Doing this with threads is a better idea, since it's more portable than signals. Since you're starting a worker thread and blocking until it finishes, there are none of the usual concurrency worries. Unfortunately, there's no way to deliver an exception asynchronously to another thread in Python (other thread APIs can do this). It'll also have the same issue with sending an exception during an exception handler, and require the same fix.
You don't have to use threads. You can use another process to do the blocking work, for instance, maybe using the subprocess module. If you want to share data structures between different parts of your program then Twisted is a great library for giving yourself control of this, and I'd recommend it if you care about blocking and expect to have this trouble a lot. The bad news with Twisted is you have to rewrite your code to avoid any blocking, and there is a fair learning curve.
You can use threads to avoid blocking, but I'd regard this as a last resort, since it exposes you to a whole world of pain. Read a good book on concurrency before even thinking about using threads in production, e.g. Jean Bacon's "Concurrent Systems". I work with a bunch of people who do really cool high performance stuff with threads, and we don't introduce threads into projects unless we really need them.
The only "safe" way to do this, in any language, is to use a secondary process to do that timeout-thing, otherwise you need to build your code in such a way that it will time out safely by itself, for instance by checking the time elapsed in a loop or similar. If changing the method isn't an option, a thread will not suffice.
Why? Because you're risking leaving things in a bad state when you do. If the thread is simply killed mid-method, locks being held, etc. will just be held, and cannot be released.
So look at the process way, do not look at the thread way.
I would usually prefer using a contextmanager as suggested by #josh-lee
But in case someone is interested in having this implemented as a decorator, here's an alternative.
Here's how it would look like:
import time
from timeout import timeout
class Test(object):
#timeout(2)
def test_a(self, foo, bar):
print foo
time.sleep(1)
print bar
return 'A Done'
#timeout(2)
def test_b(self, foo, bar):
print foo
time.sleep(3)
print bar
return 'B Done'
t = Test()
print t.test_a('python', 'rocks')
print t.test_b('timing', 'out')
And this is the timeout.py module:
import threading
class TimeoutError(Exception):
pass
class InterruptableThread(threading.Thread):
def __init__(self, func, *args, **kwargs):
threading.Thread.__init__(self)
self._func = func
self._args = args
self._kwargs = kwargs
self._result = None
def run(self):
self._result = self._func(*self._args, **self._kwargs)
#property
def result(self):
return self._result
class timeout(object):
def __init__(self, sec):
self._sec = sec
def __call__(self, f):
def wrapped_f(*args, **kwargs):
it = InterruptableThread(f, *args, **kwargs)
it.start()
it.join(self._sec)
if not it.is_alive():
return it.result
raise TimeoutError('execution expired')
return wrapped_f
The output:
python
rocks
A Done
timing
Traceback (most recent call last):
...
timeout.TimeoutError: execution expired
out
Notice that even if the TimeoutError is thrown, the decorated method will continue to run in a different thread. If you would also want this thread to be "stopped" see: Is there any way to kill a Thread in Python?
Using simple decorator
Here's the version I made after studying above answers. Pretty straight forward.
def function_timeout(seconds: int):
"""Wrapper of Decorator to pass arguments"""
def decorator(func):
#contextmanager
def time_limit(seconds_):
def signal_handler(signum, frame): # noqa
raise TimeoutException(f"Timed out in {seconds_} seconds!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds_)
try:
yield
finally:
signal.alarm(0)
#wraps(func)
def wrapper(*args, **kwargs):
with time_limit(seconds):
return func(*args, **kwargs)
return wrapper
return decorator
How to use?
#function_timeout(seconds=5)
def my_naughty_function():
while True:
print("Try to stop me ;-p")
Well of course, don't forget to import the function if it is in a separate file.
Here's a timeout function I think I found via google and it works for me.
From:
http://code.activestate.com/recipes/473878/
def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'''This function will spwan a thread and run the given function using the args, kwargs and
return the given default value if the timeout_duration is exceeded
'''
import threading
class InterruptableThread(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.result = default
def run(self):
try:
self.result = func(*args, **kwargs)
except:
self.result = default
it = InterruptableThread()
it.start()
it.join(timeout_duration)
if it.isAlive():
return it.result
else:
return it.result
The method from #user2283347 is tested working, but we want to get rid of the traceback messages. Use pass trick from Remove traceback in Python on Ctrl-C, the modified code is:
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
pass
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
def timeout_svm_score(i):
#from sklearn import svm
#import numpy as np
#from IPython.core.display import display
#%store -r names X Y
clf = svm.SVC(kernel='linear', C=1).fit(np.nan_to_num(X[[names[i]]]), Y)
score = clf.score(np.nan_to_num(X[[names[i]]]),Y)
#scoressvm.append((score, names[i]))
display((score, names[i]))
%%time
with time_limit(5):
i=0
timeout_svm_score(i)
#Wall time: 14.2 s
%%time
with time_limit(20):
i=0
timeout_svm_score(i)
#(0.04541284403669725, '计划飞行时间')
#Wall time: 16.1 s
%%time
with time_limit(5):
i=14
timeout_svm_score(i)
#Wall time: 5h 43min 41s
We can see that this method may need far long time to interrupt the calculation, we asked for 5 seconds, but it work out in 5 hours.
This code works for Windows Server Datacenter 2016 with python 3.7.3 and I didn't tested on Unix, after mixing some answers from Google and StackOverflow, it finally worked for me like this:
from multiprocessing import Process, Lock
import time
import os
def f(lock,id,sleepTime):
lock.acquire()
print("I'm P"+str(id)+" Process ID: "+str(os.getpid()))
lock.release()
time.sleep(sleepTime) #sleeps for some time
print("Process: "+str(id)+" took this much time:"+str(sleepTime))
time.sleep(sleepTime)
print("Process: "+str(id)+" took this much time:"+str(sleepTime*2))
if __name__ == '__main__':
timeout_function=float(9) # 9 seconds for max function time
print("Main Process ID: "+str(os.getpid()))
lock=Lock()
p1=Process(target=f, args=(lock,1,6,)) #Here you can change from 6 to 3 for instance, so you can watch the behavior
start=time.time()
print(type(start))
p1.start()
if p1.is_alive():
print("process running a")
else:
print("process not running a")
while p1.is_alive():
timeout=time.time()
if timeout-start > timeout_function:
p1.terminate()
print("process terminated")
print("watching, time passed: "+str(timeout-start) )
time.sleep(1)
if p1.is_alive():
print("process running b")
else:
print("process not running b")
p1.join()
if p1.is_alive():
print("process running c")
else:
print("process not running c")
end=time.time()
print("I am the main process, the two processes are done")
print("Time taken:- "+str(end-start)+" secs") #MainProcess terminates at approx ~ 5 secs.
time.sleep(5) # To see if on Task Manager the child process is really being terminated, and it is
print("finishing")
The main code is from this link:
Create two child process using python(windows)
Then I used .terminate() to kill the child process. You can see that the function f calls 2 prints, one after 5 seconds and another after 10 seconds. However, with a 7 seconds sleep and the terminate(), it does not show the last print.
It worked for me, hope it helps!
Suppose I have a class that will spawn a thread and implements .__enter__ and .__exit__ so I can use it as such:
with SomeAsyncTask(params) as t:
# do stuff with `t`
t.thread.start()
t.thread.join()
.__exit__ might perform certain actions for clean-up purposes (ie. removing temp files, etc.)
That works all fine until I have a list of SomeAsyncTasks that I would like to start all at once.
list_of_async_task_params = [params1, params2, ...]
How should I use with on the list? I'm hoping for something like this:
with [SomeAsyncTask(params) for params in list_of_async_task_params] as tasks:
# do stuff with `tasks`
for task in tasks:
task.thread.start()
for task in tasks:
task.thread.join()
I think contextlib.ExitStack is exactly what you're looking for. It's a way of combining an indeterminate number of context managers into a single one safely (so that an exception while entering one context manager won't cause it to skip exiting the ones it's already entered successfully).
The example from the docs is pretty instructive:
with ExitStack() as stack:
files = [stack.enter_context(open(fname)) for fname in filenames]
# All opened files will automatically be closed at the end of
# the with statement, even if attempts to open files later
# in the list raise an exception
This can adapted to your "hoped for" code pretty easily:
import contextlib
with contextlib.ExitStack() as stack:
tasks = [stack.enter_context(SomeAsyncTask(params))
for params in list_of_async_task_params]
for task in tasks:
task.thread.start()
for task in tasks:
task.thread.join()
Note: Somehow I missed the fact that your Thread subclass was also a context manager itself—so the code below doesn't make that assumption. Nevertheless, it might be helpful when using more "generic" kinds of threads (where using something like contextlib.ExitStack wouldn't be an option).
Your question is a little light on details—so I made some up—however this might be close to what you want. It defines a AsyncTaskListContextManager class that has the necessary __enter__() and __exit__() methods required to support the context manager protocol (and associated with statements).
import threading
from time import sleep
class SomeAsyncTask(threading.Thread):
def __init__(self, name, *args, **kwargs):
super().__init__(*args, **kwargs)
self.name = name
self.status_lock = threading.Lock()
self.running = False
def run(self):
with self.status_lock:
self.running = True
while True:
with self.status_lock:
if not self.running:
break
print('task {!r} running'.format(self.name))
sleep(.1)
print('task {!r} stopped'.format(self.name))
def stop(self):
with self.status_lock:
self.running = False
class AsyncTaskListContextManager:
def __init__(self, params_list):
self.threads = [SomeAsyncTask(params) for params in params_list]
def __enter__(self):
for thread in self.threads:
thread.start()
return self
def __exit__(self, *args):
for thread in self.threads:
if thread.is_alive():
thread.stop()
thread.join() # wait for it to terminate
return None # allows exceptions to be processed normally
params = ['Fee', 'Fie', 'Foe']
with AsyncTaskListContextManager(params) as task_list:
for _ in range(5):
sleep(1)
print('leaving task list context')
print('end-of-script')
Output:
task 'Fee' running
task 'Fie' running
task 'Foe' running
task 'Foe' running
task 'Fee' running
task 'Fie' running
... etc
task 'Fie' running
task 'Fee' running
task 'Foe' running
leaving task list context
task 'Foe' stopped
task 'Fie' stopped
task 'Fee' stopped
end-of-script
#martineau answer should work. Here's a more generic method that should work for other cases. Note that exceptions are not handled in __exit__(). If one __exit__() function fails, the rest won't be called. A generic solution would probably throw an aggregate exception and allow you to handle it. Another corner case is when you your second manager's __enter__() method throws an exception. The first manager's __exit__() will not be called in that case.
class list_context_manager:
def __init__(self, managers):
this.managers = managers
def __enter__(self):
for m in self.managers:
m.__enter__()
return self.managers
def __exit__(self):
for m in self.managers:
m.__exit__()
It can then be used like in your question:
with list_context_manager([SomeAsyncTask(params) for params in list_of_async_task_params]) as tasks:
# do stuff with `tasks`
for task in tasks:
task.thread.start()
for task in tasks:
task.thread.join()
I have a project in Python 3.5 without any usage of asynchronous features. I have to implement the folowing logic:
def should_return_in_3_sec(some_serious_job, arguments, finished_callback):
# Start some_serious_job(*arguments) in a task
# if it finishes within 3 sec:
# return result immediately
# otherwise return None, but do not terminate task.
# If the task finishes in 1 minute:
# call finished_callback(result)
# else:
# call finished_callback(None)
pass
The function should_return_in_3_sec() should remain synchronous, but it is up to me to write any new asynchronous code (including some_serious_job()).
What is the most elegant and pythonic way to do it?
Fork off a thread doing the serious job, let it write its result into a queue and then terminate. Read in your main thread from that queue with a timeout of three seconds. If the timeout occurs, start another thread and return None. Let the second thread read from the queue with a timeout of one minute; if that timeouts also, call finished_callback(None); otherwise call finished_callback(result).
I sketched it like this:
import threading, queue
def should_return_in_3_sec(some_serious_job, arguments, finished_callback):
result_queue = queue.Queue(1)
def do_serious_job_and_deliver_result():
result = some_serious_job(arguments)
result_queue.put(result)
threading.Thread(target=do_serious_job_and_deliver_result).start()
try:
result = result_queue.get(timeout=3)
except queue.Empty: # timeout?
def expect_and_handle_late_result():
try:
result = result_queue.get(timeout=60)
except queue.Empty:
finished_callback(None)
else:
finished_callback(result)
threading.Thread(target=expect_and_handle_late_result).start()
return None
else:
return result
The threading module has some simple timeout options, see Thread.join(timeout) for example.
If you do choose to use asyncio, below is a a partial solution to address some of your needs:
import asyncio
import time
async def late_response(task, flag, timeout, callback):
done, pending = await asyncio.wait([task], timeout=timeout)
callback(done.pop().result() if done else None) # will raise an exception if some_serious_job failed
flag[0] = True # signal some_serious_job to stop
return await task
async def launch_job(loop, some_serious_job, arguments, finished_callback,
timeout_1=3, timeout_2=5):
flag = [False]
task = loop.run_in_executor(None, some_serious_job, flag, *arguments)
done, pending = await asyncio.wait([task], timeout=timeout_1)
if done:
return done.pop().result() # will raise an exception if some_serious_job failed
asyncio.ensure_future(
late_response(task, flag, timeout_2, finished_callback))
return None
def f(flag, n):
for i in range(n):
print("serious", i, flag)
if flag[0]:
return "CANCELLED"
time.sleep(1)
return "OK"
def finished(result):
print("FINISHED", result)
loop = asyncio.get_event_loop()
result = loop.run_until_complete(launch_job(loop, f, [1], finished))
print("result:", result)
loop.run_forever()
This will run the job in a separate thread (Use loop.set_executor(ProcessPoolExecutor()) to run a CPU intensive task in a process instead). Keep in mind it is a bad practice to terminate a process/thread - the code above uses a very simple list to signal the thread to stop (See also threading.Event / multiprocessing.Event).
While implementing your solution, you might discover you would want to modify your existing code to use couroutines instead of using threads.
I'm trying to use twisted with greenlets, so I can write synchronous looking code in twisted without using inlineCallbacks.
Here is my code:
import time, functools
from twisted.internet import reactor, threads
from twisted.internet.defer import Deferred
from functools import wraps
import greenlet
def make_async(func):
#wraps(func)
def wrapper(*pos, **kwds):
d = Deferred()
def greenlet_func():
try:
rc = func(*pos, **kwds)
d.callback(rc)
except Exception, ex:
print ex
d.errback(ex)
g = greenlet.greenlet(greenlet_func)
g.switch()
return d
return wrapper
def sleep(t):
print "sleep(): greenelet:", greenlet.getcurrent()
g = greenlet.getcurrent()
reactor.callLater(t, g.switch)
g.parent.switch()
def wait_one(d):
print "wait_one(): greenelet:", greenlet.getcurrent()
g = greenlet.getcurrent()
active = True
def callback(result):
if not active:
g.switch(result)
else:
reactor.callLater(0, g.switch, result)
def errback(failure):
if not active:
g.throw(failure)
else:
reactor.callLater(0, g.throw, failure)
d.addCallback(callback)
d.addErrback(errback)
active = False
rc = g.parent.switch()
return rc
#make_async
def inner():
print "inner(): greenelet:", greenlet.getcurrent()
import random, time
interval = random.random()
print "Sleeping for %s seconds..." % interval
sleep(interval)
print "done"
return interval
#make_async
def outer():
print "outer(): greenelet:", greenlet.getcurrent()
print wait_one(inner())
print "Here"
reactor.callLater(0, outer)
reactor.run()
There are 5 main parts:
A sleep function, that starts a timer, then switches back to the parent greenlet. When the timer goes off, it switches back to the greenlet that is sleeping.
A make_async decorator. This takes some synchronous looking code and runs it in a greenlet. IT also returns a deferred so the caller can register callbacks when the code completes.
A wait_one function, which blocks the greenlet until the deferred being waited on resolves.
The inner function, which (when wrapped) returns a deferred, sleeps for a random time, and then passes the time it slept for to the deferred.
The outer function, which calls inner(), waits for it to return, then prints the return value.
When I run this code I get this output (Note the error on the last two lines):
outer(): greenelet: <greenlet.greenlet object at 0xb729cc5c>
inner(): greenelet: <greenlet.greenlet object at 0xb729ce3c>
Sleeping for 0.545666723422 seconds...
sleep(): greenelet: <greenlet.greenlet object at 0xb729ce3c>
wait_one(): greenelet: <greenlet.greenlet object at 0xb729cc5c>
done
0.545666723422
Here
Exception twisted.python.failure.Failure: <twisted.python.failure.Failure <class 'greenlet.GreenletExit'>> in <greenlet.greenlet object at 0xb729ce3c> ignored
GreenletExit did not kill <greenlet.greenlet object at 0xb729ce3c>
Doing a bit of research I've found that:
The last line is logged by greenlet.c
The previous line is logged by python itself, as it's ignoring an exception raised in a del method.
I'm having real trouble debugging this as I can't access the GreenletExit or twisted.python.failure.Failure exceptions to get their stack traces.
Does anyone have any ideas what I'm doing wrong, or how I get debug the exceptions that are being thrown?
One other data point: If I hack wait_one() to just return immediately (and not to register anything on the deferred it is passed), the errors go away. :-/
Rewrite your error callback in wait_one like this:
def errback(failure):
## new code
if g.dead:
return
##
if not active:
g.throw(failure)
else:
reactor.callLater(0, g.throw, failure)
If greenlet is dead (finished running), there is no point throwing exceptions
in it.
mguijarr's answer fixed the problem, but I wanted to write up how I got into this situation.
I have three greenlets:
{main} that's runing the reactor.
{outer} that's running outer().
{inner} that's rrunning inner().
When the sleep finishes the {main} switches to {inner} which switches to {outer}. Outer then returns and raises GreenletExit in {inner}. This propogates back to twisted. It sees an exception being raised from callback(), and so invokes errback(). This tries to throw the exception into {outer} (which has already exited), and I hit the error.
There is a socket related function call in my code, that function is from another module thus out of my control, the problem is that it blocks for hours occasionally, which is totally unacceptable, How can I limit the function execution time from my code? I guess the solution must utilize another thread.
An improvement on #rik.the.vik's answer would be to use the with statement to give the timeout function some syntactic sugar:
import signal
from contextlib import contextmanager
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds)
try:
yield
finally:
signal.alarm(0)
try:
with time_limit(10):
long_function_call()
except TimeoutException as e:
print("Timed out!")
I'm not sure how cross-platform this might be, but using signals and alarm might be a good way of looking at this. With a little work you could make this completely generic as well and usable in any situation.
http://docs.python.org/library/signal.html
So your code is going to look something like this.
import signal
def signal_handler(signum, frame):
raise Exception("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(10) # Ten seconds
try:
long_function_call()
except Exception, msg:
print "Timed out!"
Here's a Linux/OSX way to limit a function's running time. This is in case you don't want to use threads, and want your program to wait until the function ends, or the time limit expires.
from multiprocessing import Process
from time import sleep
def f(time):
sleep(time)
def run_with_limited_time(func, args, kwargs, time):
"""Runs a function with time limit
:param func: The function to run
:param args: The functions args, given as tuple
:param kwargs: The functions keywords, given as dict
:param time: The time limit in seconds
:return: True if the function ended successfully. False if it was terminated.
"""
p = Process(target=func, args=args, kwargs=kwargs)
p.start()
p.join(time)
if p.is_alive():
p.terminate()
return False
return True
if __name__ == '__main__':
print run_with_limited_time(f, (1.5, ), {}, 2.5) # True
print run_with_limited_time(f, (3.5, ), {}, 2.5) # False
I prefer a context manager approach because it allows the execution of multiple python statements within a with time_limit statement. Because windows system does not have SIGALARM, a more portable and perhaps more straightforward method could be using a Timer
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception):
def __init__(self, msg=''):
self.msg = msg
#contextmanager
def time_limit(seconds, msg=''):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
raise TimeoutException("Timed out for operation {}".format(msg))
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
import time
# ends after 5 seconds
with time_limit(5, 'sleep'):
for i in range(10):
time.sleep(1)
# this will actually end after 10 seconds
with time_limit(5, 'sleep'):
time.sleep(10)
The key technique here is the use of _thread.interrupt_main to interrupt the main thread from the timer thread. One caveat is that the main thread does not always respond to the KeyboardInterrupt raised by the Timer quickly. For example, time.sleep() calls a system function so a KeyboardInterrupt will be handled after the sleep call.
Here: a simple way of getting the desired effect:
https://pypi.org/project/func-timeout
This saved my life.
And now an example on how it works: lets say you have a huge list of items to be processed and you are iterating your function over those items. However, for some strange reason, your function get stuck on item n, without raising an exception. You need to other items to be processed, the more the better. In this case, you can set a timeout for processing each item:
import time
import func_timeout
def my_function(n):
"""Sleep for n seconds and return n squared."""
print(f'Processing {n}')
time.sleep(n)
return n**2
def main_controller(max_wait_time, all_data):
"""
Feed my_function with a list of itens to process (all_data).
However, if max_wait_time is exceeded, return the item and a fail info.
"""
res = []
for data in all_data:
try:
my_square = func_timeout.func_timeout(
max_wait_time, my_function, args=[data]
)
res.append((my_square, 'processed'))
except func_timeout.FunctionTimedOut:
print('error')
res.append((data, 'fail'))
continue
return res
timeout_time = 2.1 # my time limit
all_data = range(1, 10) # the data to be processed
res = main_controller(timeout_time, all_data)
print(res)
Doing this from within a signal handler is dangerous: you might be inside an exception handler at the time the exception is raised, and leave things in a broken state. For example,
def function_with_enforced_timeout():
f = open_temporary_file()
try:
...
finally:
here()
unlink(f.filename)
If your exception is raised here(), the temporary file will never be deleted.
The solution here is for asynchronous exceptions to be postponed until the code is not inside exception-handling code (an except or finally block), but Python doesn't do that.
Note that this won't interrupt anything while executing native code; it'll only interrupt it when the function returns, so this may not help this particular case. (SIGALRM itself might interrupt the call that's blocking--but socket code typically simply retries after an EINTR.)
Doing this with threads is a better idea, since it's more portable than signals. Since you're starting a worker thread and blocking until it finishes, there are none of the usual concurrency worries. Unfortunately, there's no way to deliver an exception asynchronously to another thread in Python (other thread APIs can do this). It'll also have the same issue with sending an exception during an exception handler, and require the same fix.
You don't have to use threads. You can use another process to do the blocking work, for instance, maybe using the subprocess module. If you want to share data structures between different parts of your program then Twisted is a great library for giving yourself control of this, and I'd recommend it if you care about blocking and expect to have this trouble a lot. The bad news with Twisted is you have to rewrite your code to avoid any blocking, and there is a fair learning curve.
You can use threads to avoid blocking, but I'd regard this as a last resort, since it exposes you to a whole world of pain. Read a good book on concurrency before even thinking about using threads in production, e.g. Jean Bacon's "Concurrent Systems". I work with a bunch of people who do really cool high performance stuff with threads, and we don't introduce threads into projects unless we really need them.
The only "safe" way to do this, in any language, is to use a secondary process to do that timeout-thing, otherwise you need to build your code in such a way that it will time out safely by itself, for instance by checking the time elapsed in a loop or similar. If changing the method isn't an option, a thread will not suffice.
Why? Because you're risking leaving things in a bad state when you do. If the thread is simply killed mid-method, locks being held, etc. will just be held, and cannot be released.
So look at the process way, do not look at the thread way.
I would usually prefer using a contextmanager as suggested by #josh-lee
But in case someone is interested in having this implemented as a decorator, here's an alternative.
Here's how it would look like:
import time
from timeout import timeout
class Test(object):
#timeout(2)
def test_a(self, foo, bar):
print foo
time.sleep(1)
print bar
return 'A Done'
#timeout(2)
def test_b(self, foo, bar):
print foo
time.sleep(3)
print bar
return 'B Done'
t = Test()
print t.test_a('python', 'rocks')
print t.test_b('timing', 'out')
And this is the timeout.py module:
import threading
class TimeoutError(Exception):
pass
class InterruptableThread(threading.Thread):
def __init__(self, func, *args, **kwargs):
threading.Thread.__init__(self)
self._func = func
self._args = args
self._kwargs = kwargs
self._result = None
def run(self):
self._result = self._func(*self._args, **self._kwargs)
#property
def result(self):
return self._result
class timeout(object):
def __init__(self, sec):
self._sec = sec
def __call__(self, f):
def wrapped_f(*args, **kwargs):
it = InterruptableThread(f, *args, **kwargs)
it.start()
it.join(self._sec)
if not it.is_alive():
return it.result
raise TimeoutError('execution expired')
return wrapped_f
The output:
python
rocks
A Done
timing
Traceback (most recent call last):
...
timeout.TimeoutError: execution expired
out
Notice that even if the TimeoutError is thrown, the decorated method will continue to run in a different thread. If you would also want this thread to be "stopped" see: Is there any way to kill a Thread in Python?
Using simple decorator
Here's the version I made after studying above answers. Pretty straight forward.
def function_timeout(seconds: int):
"""Wrapper of Decorator to pass arguments"""
def decorator(func):
#contextmanager
def time_limit(seconds_):
def signal_handler(signum, frame): # noqa
raise TimeoutException(f"Timed out in {seconds_} seconds!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds_)
try:
yield
finally:
signal.alarm(0)
#wraps(func)
def wrapper(*args, **kwargs):
with time_limit(seconds):
return func(*args, **kwargs)
return wrapper
return decorator
How to use?
#function_timeout(seconds=5)
def my_naughty_function():
while True:
print("Try to stop me ;-p")
Well of course, don't forget to import the function if it is in a separate file.
Here's a timeout function I think I found via google and it works for me.
From:
http://code.activestate.com/recipes/473878/
def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'''This function will spwan a thread and run the given function using the args, kwargs and
return the given default value if the timeout_duration is exceeded
'''
import threading
class InterruptableThread(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.result = default
def run(self):
try:
self.result = func(*args, **kwargs)
except:
self.result = default
it = InterruptableThread()
it.start()
it.join(timeout_duration)
if it.isAlive():
return it.result
else:
return it.result
The method from #user2283347 is tested working, but we want to get rid of the traceback messages. Use pass trick from Remove traceback in Python on Ctrl-C, the modified code is:
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
pass
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
def timeout_svm_score(i):
#from sklearn import svm
#import numpy as np
#from IPython.core.display import display
#%store -r names X Y
clf = svm.SVC(kernel='linear', C=1).fit(np.nan_to_num(X[[names[i]]]), Y)
score = clf.score(np.nan_to_num(X[[names[i]]]),Y)
#scoressvm.append((score, names[i]))
display((score, names[i]))
%%time
with time_limit(5):
i=0
timeout_svm_score(i)
#Wall time: 14.2 s
%%time
with time_limit(20):
i=0
timeout_svm_score(i)
#(0.04541284403669725, '计划飞行时间')
#Wall time: 16.1 s
%%time
with time_limit(5):
i=14
timeout_svm_score(i)
#Wall time: 5h 43min 41s
We can see that this method may need far long time to interrupt the calculation, we asked for 5 seconds, but it work out in 5 hours.
This code works for Windows Server Datacenter 2016 with python 3.7.3 and I didn't tested on Unix, after mixing some answers from Google and StackOverflow, it finally worked for me like this:
from multiprocessing import Process, Lock
import time
import os
def f(lock,id,sleepTime):
lock.acquire()
print("I'm P"+str(id)+" Process ID: "+str(os.getpid()))
lock.release()
time.sleep(sleepTime) #sleeps for some time
print("Process: "+str(id)+" took this much time:"+str(sleepTime))
time.sleep(sleepTime)
print("Process: "+str(id)+" took this much time:"+str(sleepTime*2))
if __name__ == '__main__':
timeout_function=float(9) # 9 seconds for max function time
print("Main Process ID: "+str(os.getpid()))
lock=Lock()
p1=Process(target=f, args=(lock,1,6,)) #Here you can change from 6 to 3 for instance, so you can watch the behavior
start=time.time()
print(type(start))
p1.start()
if p1.is_alive():
print("process running a")
else:
print("process not running a")
while p1.is_alive():
timeout=time.time()
if timeout-start > timeout_function:
p1.terminate()
print("process terminated")
print("watching, time passed: "+str(timeout-start) )
time.sleep(1)
if p1.is_alive():
print("process running b")
else:
print("process not running b")
p1.join()
if p1.is_alive():
print("process running c")
else:
print("process not running c")
end=time.time()
print("I am the main process, the two processes are done")
print("Time taken:- "+str(end-start)+" secs") #MainProcess terminates at approx ~ 5 secs.
time.sleep(5) # To see if on Task Manager the child process is really being terminated, and it is
print("finishing")
The main code is from this link:
Create two child process using python(windows)
Then I used .terminate() to kill the child process. You can see that the function f calls 2 prints, one after 5 seconds and another after 10 seconds. However, with a 7 seconds sleep and the terminate(), it does not show the last print.
It worked for me, hope it helps!