The better timout decorator
Version v1.4.0 as of 2023-07-14 see Changelog_
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There are several timeout decorators available, but the one mentioned here focuses on ensuring correctness when used with classes, methods, class methods, static methods, etc. It also preserves traceback information for PyCharm debugging.
Additionally, there is a powerful eval function that allows reading the desired timeout value even from class attributes.
Two timeout strategies have been implemented: one using "Signals" and the other using "Multiprocessing".
The "Signals" strategy (for POSIX Systems) is elegant and efficient,
but it has some important caveats which should be reviewed
in the Caveats using Signals_ section.
The default strategy is to use Multiprocessing
When using a subprocess many types from multiprocessing need to be pickable so that child processes can use them. Therefore we use "dill" instead of "pickle" and "multiprocess" instead of "multiprocessing".
dill extends python’s pickle module for serializing and de-serializing python objects to the majority of the built-in python types
Communication with the subprocess is done via "multiprocess.pipe" instead of "queue", which offers improved speed and may also work on Amazon AWS.
automated tests, Github Actions, Documentation, Badges, etc. are managed with PizzaCutter <https://github .com/bitranox/PizzaCutter>_ (cookiecutter on steroids)
Python version required: 3.8.0 or newer
tested on recent linux with python 3.8, 3.9, 3.10, 3.11, 3.12-dev, pypy-3.9, pypy-3.10 - architectures: amd64
100% code coverage <https://codeclimate.com/github/bitranox/wrapt_timeout_decorator/test_coverage>, flake8 style checking ,mypy static type checking ,tested under Linux, macOS, Windows <https://github.com/bitranox/wrapt_timeout_decorator/actions/workflows/python-package.yml>, automatic daily builds and monitoring
Try it Online_Usage_Usage from Commandline_Installation and Upgrade_Requirements_Acknowledgements_Contribute_Report Issues <https://github.com/bitranox/wrapt_timeout_decorator/blob/master/ISSUE_TEMPLATE.md>_Pull Request <https://github.com/bitranox/wrapt_timeout_decorator/blob/master/PULL_REQUEST_TEMPLATE.md>_Code of Conduct <https://github.com/bitranox/wrapt_timeout_decorator/blob/master/CODE_OF_CONDUCT.md>_License_Changelog_You might try it right away in Jupyter Notebook by using the "launch binder" badge, or click here <https://mybinder.org/v2/gh/{{rst_include. repository_slug}}/master?filepath=wrapt_timeout_decorator.ipynb>_
.. code-block:: py
import time
from wrapt_timeout_decorator import *
@timeout(5)
def mytest(message):
# this example does NOT work on windows, please check the section
# "use with Windows" in the README.rst
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed'.format(i))
if __name__ == '__main__':
mytest('starting')
Minimize the excessive use of timeouts in your code and reserve their usage for essential cases.
Implement timeouts at the lowest possible abstraction level to prevent unintended consequences such as exceptions being caught by unrelated code or non-pickable functions and arguments.
Avoid incorporating the Timeout Decorator within a loop that iterates multiple times, as this can result in considerable time overhead, especially on Windows systems, when utilizing multiprocessing.
Whenever feasible, leverage the existing built-in timeouts already provided by the functions and libraries you utilize. These built-in timeouts can handle most situations effectively. Only resort to use this Timeout Decorator as a last resort when all other alternatives have been exhausted.
BAD EXAMPLE (Pseudocode) - lets assume the write to the database fails sometimes for unknown reasons, and "hangs"
.. code-block:: py
# module file_analyzer
import time
from wrapt_timeout_decorator import *
def read_the_file(filename):
...
def analyze_the_file(filename):
...
def write_to_database(file_content):
...
@timeout(5) # try to minimize the scope of the timeout
def import_file(filename):
file_content = read_the_file(filename)
structured_data = analyze_the_file(file_content)
write_to_database(structured_data)
BETTER EXAMPLE (Pseudocode)
.. code-block:: py
# module file_analyzer
import time
from wrapt_timeout_decorator import *
def read_the_file(filename):
...
def analyze_the_file(filename):
...
@timeout(5) # better, because smaller scope
def write_to_database(file_content):
...
def import_file(filename):
file_content = read_the_file(filename)
structured_data = analyze_the_file(file_content)
write_to_database(structured_data)
For the impatient:
All You need to do is to put the decorated function into another Module, NOT in the main program.
For those who want to dive deeper :
On Windows the main module is imported again (but with a name != 'main') because Python is trying to simulate a forking-like behavior on a system that doesn't support forking. multiprocessing tries to create an environment similar to Your main process by importing the main module again with a different name. Thats why You need to shield the entry point of Your program with the famous " if name == 'main': "
.. code-block:: py
import lib_foo
def some_module():
lib_foo.function_foo()
def main():
some_module()
# here the subprocess stops loading, because __name__ is NOT '__main__'
if __name__ = '__main__':
main()
This is a problem of Windows OS, because the Windows Operating System does not support "fork"
You can find more information on that here:
https://stackoverflow.com/questions/45110287/workaround-for-using-name-main-in-python-multiprocessing
https://docs.python.org/2/library/multiprocessing.html#windows
Since main.py is loaded again with a different name but "main", the decorated function now points to objects that do not exist anymore, therefore You need to put the decorated Classes and functions into another module. In general (especially on windows) , the main() program should not have anything but the main function, the real thing should happen in the modules. I am also used to put all settings or configurations in a different file - so all processes or threads can access them (and also to keep them in one place together, not to forget typing hints and name completion in Your favorite editor)
The "dill" serializer is able to serialize also the main context, that means the objects in our example are pickled to "main.lib_foo", "main.some_module","main.main" etc. We would not have this limitation when using "pickle" with the downside that "pickle" can not serialize following types:
functions with yields, nested functions, lambdas, cell, method, unboundmethod, module, code, methodwrapper, dictproxy, methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor, xrange, slice, notimplemented, ellipsis, quit
additional dill supports:
save and load python interpreter sessions, save and extract the source code from functions and classes, interactively diagnose pickling errors
To support more types with the decorator, we selected dill as serializer, with the small downside that methods and classes can not be decorated in the main context, but need to reside in a module.
You can find more information on that here: https://stackoverflow.com/questions/45616584/serializing-an-object-in-main-with-pickle-or-dill
Timing : Since spawning takes some unknown timespan (all imports needs to be done again !), You can specify when the timeout should start, please read the section hard timeout_
Here an example that will work on Linux but wont work on Windows (the variable "name" and the function "sleep" wont be found in the spawned process :
.. code-block:: py
main.py:
from time import sleep
from wrapt_timeout_decorator import *
name="my_var_name"
@timeout(5, use_signals=False)
def mytest():
# this example does NOT work on windows, please check the example below !
# You need to move this function into a module to be able to run it on windows.
print("Start ", name)
for i in range(1,10):
sleep(1)
print("{} seconds have passed".format(i))
return i
if __name__ == '__main__':
mytest()
here the same example, which will work on Windows:
.. code-block:: py
# my_program_main.py:
import lib_test
def main():
lib_test.mytest()
if __name__ == '__main__':
main()
.. code-block:: py
# conf_my_program.py:
class ConfMyProgram(object):
def __init__(self):
self.name:str = 'my_var_name'
conf_my_program = ConfMyProgram()
.. code-block:: py
# lib_test.py:
from wrapt_timeout_decorator import *
from time import sleep
from conf_my_program import conf_my_program
# use_signals = False is not really necessary here, it is set automatically under Windows
# but You can force NOT to use Signals under Linux
@timeout(5, use_signals=False)
def mytest():
print("Start ", conf_my_program.name)
for i in range(1,10):
sleep(1)
print("{} seconds have passed".format(i))
return i
as ABADGER1999 points out in his blog <https://anonbadger.wordpress.com/2018/12/15/python-signal-handlers-and-exceptions/>_
using signals and the TimeoutException is probably not the best idea - because it can be catched in the decorated function.
Of course You can use Your own Exception, derived from the Base Exception Class, but the code might still not work as expected -
see the next example - You may try it out in jupyter <https://mybinder.org/v2/gh/bitranox/wrapt_timeout_decorator/master?filepath=jupyter_test_{repository}.ipynb>_:
.. code-block:: py
import time
from wrapt_timeout_decorator import *
# caveats when using signals - the TimeoutError raised by the signal may be catched
# inside the decorated function.
# So You might use Your own Exception, derived from the base Exception Class.
# In Python-3.7.1 stdlib there are over 300 pieces of code that will catch your timeout
# if you were to base an exception on Exception. If you base your exception on BaseException,
# there are still 231 places that can potentially catch your exception.
# You should use use_signals=False if You want to make sure that the timeout is handled correctly !
# therefore the default value for use_signals = False on this decorator !
@timeout(5, use_signals=True)
def mytest(message):
try:
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed - lets assume we read a big file here'.format(i))
# TimeoutError is a Subclass of OSError - therefore it is catched here !
except OSError:
for i in range(1,10):
time.sleep(1)
print('Whats going on here ? - Ooops the Timeout Exception is catched by the OSError ! {}'.format(i))
except Exception:
# even worse !
pass
except:
# the worst - and exists more then 300x in actual Python 3.7 stdlib Code !
# so You never really can rely that You catch the TimeoutError when using Signals !
pass
if __name__ == '__main__':
try:
mytest('starting')
print('no Timeout Occured')
except TimeoutError():
# this will never be printed because the decorated function catches implicitly the TimeoutError !
print('Timeout Occured')
by default we use multiprocessing to archive the timeout function.
Basically this is done like that :
the program is forked
the forked function is run in a subprocess
parameters and results are passed via pipe (pickled, we use dill here)
if there is no result within the timeout period, the forked process will be terminated with SIGTERM
since there is only ONE ALARM Signal on Unix per process, You need to use use_signals = False for nested timeouts.
The outmost decorator might use Signals, all nested Decorators needs to use use_signals=False (the default)
You may try it out in jupyter <https://mybinder.org/v2/gh/bitranox/wrapt_timeout_decorator/master?filepath=jupyter_test_{repository}.ipynb>_:
.. code-block:: py
# main.py
import mylib
# this example will work on Windows and Linux
# since the decorated function is not in the __main__ scope but in another module !
if __name__ == '__main__':
mylib.outer()
.. code-block:: py
# mylib.py
from wrapt_timeout_decorator import *
import time
# this example will work on Windows and Linux
# since the decorated function is not in the __main__ scope but in another module !
@timeout(1, use_signals=True)
def outer():
inner()
@timeout(5)
def inner():
time.sleep(3)
print("Should never be printed if you call outer()")
Specify an alternate exception to raise on timeout:
.. code-block:: py
import time
from wrapt_timeout_decorator import *
@timeout(5, timeout_exception=StopIteration)
def mytest(message):
# this example does NOT work on windows, please check the section
# "use with Windows" in the README.rst
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed'.format(i))
if __name__ == '__main__':
mytest('starting')
.. code-block:: py
@timeout(dec_timeout, use_signals, timeout_exception, exception_message, dec_allow_eval, dec_hard_timeout)
def decorated_function(*args, **kwargs):
# interesting things happens here ...
...
"""
dec_timeout the timeout period in seconds, or a string that can be evaluated when dec_allow_eval = True
type: float, integer or string
default: None (no Timeout set)
can be overridden by passing the kwarg dec_timeout to the decorated function*
use_signals if to use signals (linux, osx) to realize the timeout. The most accurate method but with caveats.
By default the Wrapt Timeout Decorator does NOT use signals !
Please note that signals can only be used in the main thread and only on linux. In all other cases
(not the main thread, or under Windows) signals cant be used anyway and will be disabled automatically.
In general You dont need to set use_signals Yourself.
Please read the sections - `Caveats using Signals` and `Caveats using Multiprocessing`
type: boolean
default: False
can be overridden by passing the kwarg use_signals to the decorated function*
timeout_exception the Exception that will be raised if a timeout occurs.
type: exception
default: TimeoutError, on Python < 3.3: Assertion Error (since TimeoutError does not exist on that Python Versions)
exception_message custom Exception message.
type: str
default : 'Function {function_name} timed out after {dec_timeout} seconds' (will be formatted)
dec_allow_eval will allow to evaluate the parameter dec_timeout.
If enabled, the parameter of the function dec_timeout, or the parameter passed
by kwarg dec_timeout will be evaluated if its type is string. You can access :
wrapped (the decorated function object and all the exposed objects below)
instance Example: 'instance.x' - see example above or doku
args Example: 'args[0]' - the timeout is the first argument in args
kwargs Example: 'kwargs["max_time"] * 2'
type: bool
default: false
can be overridden by passing the kwarg dec_allow_eval to the decorated function*
dec_hard_timeout only relevant when signals can not be used. In that case a new process needs to be created.
The creation of the process on windows might take 0.5 seconds and more, depending on the size
of the main module and modules to be imported. Especially useful for small timeout periods.
dec_hard_timeout = True : the decorated function will time out after dec_timeout, no matter what -
that means if You set 0.1 seconds here, the subprocess can not be created in that time and the
function will always time out and never run.
dec_hard_timeout = False : the decorated function will time out after the called function
is allowed to run for dec_timeout seconds. The time needed to create that process is not considered.
That means if You set 0.1 seconds here, and the time to create the subprocess is 0.5 seconds,
the decorated function will time out after 0.6 seconds in total, allowing the decorated function to run
for 0.1 seconds.
type: bool
default: false
can be overridden by passing the kwarg dec_hard_timeout to the decorated function*
* that means the decorated_function must not use that kwarg itself, since this kwarg will be popped from the kwargs
"""
decorator parameters starting with \dec_* and use_signals can be overridden by kwargs with the same name :
.. code-block:: py
import time
from wrapt_timeout_decorator import *
@timeout(dec_timeout=5, use_signals=False)
def mytest(message):
# this example does NOT work on windows, please check the section
# "use with Windows" in the README.rst
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed'.format(i))
if __name__ == '__main__':
mytest('starting',dec_timeout=12) # override the decorators setting. The kwarg dec_timeout will be not
# passed to the decorated function.
By default, timeout-decorator uses signals to limit the execution time
of the given function. This approach does not work if your function is
executed not in the main thread (for example if it's a worker thread of
the web application) or when the operating system does not support signals (aka Windows).
There is an alternative timeout strategy for this case - by using multiprocessing.
This is done automatically, so you dont need to set use_signals=False.
You can force not to use signals on Linux by passing the parameter use_signals=False to the timeout
decorator function for testing. If Your program should (also) run on Windows, I recommend to test under
Windows, since Windows does not support forking (read more under Section use with Windows).
The following Code will run on Linux but NOT on Windows :
.. code-block:: py
import time
from wrapt_timeout_decorator import *
@timeout(5, use_signals=False)
def mytest(message):
# this example does NOT work on windows, please check the section
# "use with Windows" in the README.rst
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed'.format(i))
if __name__ == '__main__':
mytest('starting')
.. warning:: Make sure that in case of multiprocessing strategy for timeout, your function does not return objects which cannot be pickled, otherwise it will fail at marshalling it between master and child processes. To cover more cases, we use multiprocess and dill instead of multiprocessing and pickle.
Since Signals will not work on Windows, it is disabled by default, whatever You set.
when using multiprocessing, the subprocess is monitored if it is still alive.
if the subprocess was terminated or killed (for instance by OOMKiller),
multiprocessing.context.ProcessError will be raised.
By default the subprocess is monitored every 5 seconds, but can be set with parameter
dec_poll_subprocess. polling can be turned off by setting to 0.0 seconds
.. code-block:: python
from wrapt_timeout_decorator import timeout
@timeout(10, use_signals=False, timeout_exception=TimeoutError, dec_poll_subprocess=1)
def slow_process() -> None:
# should have enough time to finish
# but instead it gets terminated, and the
# poll the subprocess every second
logger.error(f"Slow process started at {get_str_time()}")
time.sleep(5)
logger.error(f"Slow process done at {get_str_time()}")
def fake_oom_killer() -> None:
logger.error(f"Fake OOMKiller started at {get_str_time()}")
time.sleep(2)
# kill sibling slow_process
# hacky way to find it
target = psutil.Process().parent().children(recursive=True)[-1]
target.kill()
logger.error(f"Killed {target.pid} at {get_str_time()}")
def start_processes() -> None:
"""
starts the 'fake_oom_killer' and 'slow_process' process -
and kill 'slow_process' after two seconds
>>> start_processes()
Traceback (most recent call last):
...
multiprocessing.context.ProcessError: Function slow_process was terminated or killed after ... seconds
"""
process_oom_killer = multiprocessing.Process(target=fake_oom_killer, args=())
process_oom_killer.start()
slow_process()
process_oom_killer.join()
def get_str_time() -> str:
t = time.localtime()
current_time = time.strftime("%H:%M:%S", t)
return current_time
if __name__ == '__main__':
start_processes()
You can use the timout also as function, without using as decorator:
.. code-block:: py
import time
from wrapt_timeout_decorator import *
def mytest(message):
print(message)
for i in range(1,10):
time.sleep(1)
print('{} seconds have passed'.format(i))
if __name__ == '__main__':
timeout(dec_timeout=5)(mytest)('starting')
This is very powerful, but can be also very dangerous if you accept strings to evaluate from UNTRUSTED input.
read: https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html
If enabled, the parameter of the function dec_timeout, or the parameter passed by kwarg dec_timeout will be evaluated if its type is string.
You can access :
"wrapped" (the decorated function and its attributes)
"instance" Example: 'instance.x' - an attribute of the instance of the class instance
"args" Example: 'args[0]' - the timeout is the first argument in args
"kwargs" Example: 'kwargs["max_time"] * 2'
and of course all attributes You can think of - that makes it powerful but dangerous. by default allow_eval is disabled - but You can enable it in order to cover some edge cases without modifying the timeout decorator.
.. code-block:: py
# this example does NOT work on windows, please check the section
# "use with Windows" in the README.rst
def class FunnyMemes(object):
def __init__(self,x):
self.x=x
@timeout('instance.x', dec_allow_eval=True)
def swallow(self):
while True:
time.sleep(0.5)
print('swallow')
@timeout(1)
def parrot(self):
while True:
time.sleep(0.5)
print('parrot')
@timeout(dec_timeout='args[0] + kwargs.pop("more_time",0)', dec_allow_eval=True)
def knight(self,base_delay):
while True:
time.sleep(base_delay)
print('knight')
def main():
my_memes = FunnyMemes(2)
my_memes.swallow() # this will time out after 2 seconds
my_memes.swallow(dec_timeout='instance.x * 2 + 1') # this will time out after 5 seconds
my_memes.parrot(dec_timeout='instance.x * 2 + 1', dec_allow_eval=True) # this will time out after 5 seconds
my_memes.knight(1,more_time=4) # this will time out after 5 seconds
if __name__ == '__main__':
main()
remember that decorated functions (and their results !) needs to be pickable under Windows. In order to detect pickle problems You can use :
.. code-block:: py
from wrapt_timeout_decorator import *
# always remember that the "object_to_pickle" should not be defined within the main context
detect_unpickable_objects(object_to_pickle, dill_trace=True) # type: (Any, bool) -> Dict
when signals=False (on Windows), logging in the wrapped function can be tricky. Since a new process is created, we can not use the logger object of the main process. Further development is needed to connect to the main process logger via a socket or queue.
When the wrapped function is using logger=logging.getLogger(), a new Logger Object is created. Setting up that Logger can be tricky (File Logging from two Processes is not supported ...) I think I will use a socket to implement that (SocketHandler and some Receiver Thread)
Until then, You need to set up Your own new logger in the decorated function, if logging is needed. Again - keep in mind that You can not write to the same logfile from different processes ! (although there are logging modules which can do that)
when use_signals = False (this is the only method available on Windows), the timeout function is realized by starting another process and terminate that process after the given timeout. Under Linux fork() of a new process is very fast, under Windows it might take some considerable time, because the main context needs to be reloaded on spawn(). Spawning of a small module might take something like 0.5 seconds and more.
By default, when using signals=False, the timeout begins after the new process is created.
This means that the timeout given, is the time the decorated process is allowed to run, not included the time excluding the time to setup the process itself. This is especially important if You use small timeout periods :
for Instance:
.. code-block:: py
@timeout(0.1)
def test():
time.sleep(0.2)
the total time to timeout on linux with use_signals = False will be around 0.1 seconds, but on windows this can take about 0.6 seconds: 0.5 seconds to spawn the new process, and giving the function test() 0.1 seconds to run !
If You need that a decorated function should timeout exactly** after the given timeout period, You can pass the parameter dec_hard_timeout=True. in this case the called function will timeout exactly** after the given time, no matter how long it took to spawn the process itself. In that case, if You set up the timeout too short, the process might never run and will always timeout during spawning.
** well, more or less exactly - it still takes some short time to return from the spawned process - so be extra cautious on very short timeouts !
for local MYPY Testing please make sure that the stub file "wrapt.pyi" is in in the MYPY Path (once!), in order to preserve the decorated function signature.
.. code-block::
Usage: wrapt_timeout_decorator [OPTIONS] COMMAND [ARGS]...
The better timout decorator
Options: --version Show the version and exit. --traceback / --no-traceback return traceback information on cli -h, --help Show this message and exit.
Commands: info get program informations
.. code-block::
python -m pip --upgrade pip
python -m pip --upgrade setuptools
.. code-block::
python -m pip install --upgrade wrapt_timeout_decorator
.. code-block::
python -m pip install --upgrade wrapt_timeout_decorator[test]
.. code-block::
python -m pip install --upgrade git+https://github.com/bitranox/wrapt_timeout_decorator.git
.. code-block::
# Insert following line in Your requirements.txt:
# for the latest Release on pypi:
wrapt_timeout_decorator
# for the latest development version :
wrapt_timeout_decorator @ git+https://github.com/bitranox/wrapt_timeout_decorator.git
# to install and upgrade all modules mentioned in requirements.txt:
python -m pip install --upgrade -r /<path>/requirements.txt
.. code-block::
# cd ~
$ git clone https://github.com/bitranox/wrapt_timeout_decorator.git
$ cd wrapt_timeout_decorator
python -m pip install -e .[test]
.. code-block:: shell
# from Your shell's homedirectory:
$ git clone https://github.com/bitranox/wrapt_timeout_decorator.git
$ cd wrapt_timeout_decorator
# to run the tests:
$ make test
# to install the package
$ make install
# to clean the package
$ make clean
# uninstall the package
$ make uninstall
following modules will be automatically installed :
.. code-block:: bash
## Project Requirements
cli_exit_tools
lib_detect_testenv
# class decorators are failing on windows with dill 0.3.5, 0.3.5.1
dill>0.3.0,!=0.3.5,!=0.3.5.1;sys_platform=="win32"
dill;sys_platform!="win32"
multiprocess
psutil
wrapt
I would love for you to fork and send me pull request for this project.
please Contribute <https://github.com/bitranox/wrapt_timeout_decorator/blob/master/CONTRIBUTING.md>_This software is licensed under the MIT license <http://en.wikipedia.org/wiki/MIT_License>_
2023-07-13:
- check for killed child processes (for instance by OOMKiller)
- change dill requirements for windows
- require minimum python 3.8
- remove python 3.7 tests
- introduce PEP517 packaging standard
- introduce pyproject.toml build-system
- remove mypy.ini
- remove pytest.ini
- remove setup.cfg
- remove setup.py
- remove .bettercodehub.yml
- remove .travis.yml
- update black config
- clean ./tests/test_cli.py
- add codeql badge
- move 3rd_party_stubs outside the src directory to ./.3rd_party_stubs
- add pypy 3.10 tests
- add python 3.12-dev tests
2022-06-01: update to github actions checkout@v3 and setup-python@v3
2022-05-23: update requirements.txt
2022-05-23: - set dill version < 0.3.5 on windows, because decorating class methods fails with dill 0.3.5 upwards - update tests to the latest python versions
2022-04-26: add tests for thread lock
2022-04-26: preserve Signature of the decorator
2022-03-29: remedy mypy Untyped decorator makes function "cli_info" untyped
2022-03-28: extend time on test_timeout_decorator_arg - github macos seems to be slow, so sometimes that test fails
2022-03-25: fix github actions windows test
2022-03-23: extend time on test_timeout_ok_timeout_as_kwarg - github macos seems to be slow, so sometimes that test fails
2022-03-10: extend time on test_timeout_alternate_exception - github macos seems to be slow, so sometimes that test fails
2022-03-01: github actions pipeline, codestyle black, fix requirements
2019-09-02: strict mypy static type checking, housekeeping
2019-05-03: pointing out caveats when using signals, the decorator defaults now to NOT using Signals !
2019-05-03: support nested decorators, mypy static type checking
2019-04-23: import multiprocess as multiprocess, not as multiprocessing - that might brake other packages
2019-04-09: initial PyPi release
2019-04-03: added pickle analyze convenience function
2019-03-27: added OsX and Windows tests, added parameter dec_hard_timeout for Windows, 100% Code Coverage
2019-02-26: complete refractoring and code cleaning
2019-02-25: fix pickle detection, added some tests, codecov now correctly combining the coverage of all tests
2019-02-24: fix pickle detection when use_signals = False, drop Python2.6 support since wrapt dropped it.
2018-09-13: use multiprocessing.pipe instead of queue If we are not able to use signals, we need to spawn a new process. This was done in the past by pickling the target function and put it on a queue - now this is done with a half-duplex pipe.
2017-12-02: automatic detection if we are in the main thread. Signals can only be used in the main thread. If the decorator is running in a subthread, we automatically disable signals.
2017-11-30: using dill and multiprocess to enhance windows functionality
2017-11-10: Initial public release