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Problems for running concurrent futures process pool in JupyterNotebook [duplicate]

In a nutshell
I get a BrokenProcessPool exception when parallelizing my code with concurrent.futures. No further error is displayed. I want to find the cause of the error and ask for ideas of how to do that.
Full problem
I am using concurrent.futures to parallelize some code.
with ProcessPoolExecutor() as pool:
mapObj = pool.map(myMethod, args)
I end up with (and only with) the following exception:
concurrent.futures.process.BrokenProcessPool: A child process terminated abruptly, the process pool is not usable anymore
Unfortunately, the program is complex and the error appears only after the program has run for 30 minutes. Therefore, I cannot provide a nice minimal example.
In order to find the cause of the issue, I wrapped the method that I run in parallel with a try-except-block:
def myMethod(*args):
try:
...
except Exception as e:
print(e)
The problem remained the same and the except block was never entered. I conclude that the exception does not come from my code.
My next step was to write a custom ProcessPoolExecutor class that is a child of the original ProcessPoolExecutor and allows me to replace some methods with cusomized ones. I copied and pasted the original code of the method _process_worker and added some print statements.
def _process_worker(call_queue, result_queue):
"""Evaluates calls from call_queue and places the results in result_queue.
...
"""
while True:
call_item = call_queue.get(block=True)
if call_item is None:
# Wake up queue management thread
result_queue.put(os.getpid())
return
try:
r = call_item.fn(*call_item.args, **call_item.kwargs)
except BaseException as e:
print("??? Exception ???") # newly added
print(e) # newly added
exc = _ExceptionWithTraceback(e, e.__traceback__)
result_queue.put(_ResultItem(call_item.work_id, exception=exc))
else:
result_queue.put(_ResultItem(call_item.work_id,
result=r))
Again, the except block is never entered. This was to be expected, because I already ensured that my code does not raise an exception (and if everything worked well, the exception should be passed to the main process).
Now I am lacking ideas how I could find the error. The exception is raised here:
def submit(self, fn, *args, **kwargs):
with self._shutdown_lock:
if self._broken:
raise BrokenProcessPool('A child process terminated '
'abruptly, the process pool is not usable anymore')
if self._shutdown_thread:
raise RuntimeError('cannot schedule new futures after shutdown')
f = _base.Future()
w = _WorkItem(f, fn, args, kwargs)
self._pending_work_items[self._queue_count] = w
self._work_ids.put(self._queue_count)
self._queue_count += 1
# Wake up queue management thread
self._result_queue.put(None)
self._start_queue_management_thread()
return f
The process pool is set to be broken here:
def _queue_management_worker(executor_reference,
processes,
pending_work_items,
work_ids_queue,
call_queue,
result_queue):
"""Manages the communication between this process and the worker processes.
...
"""
executor = None
def shutting_down():
return _shutdown or executor is None or executor._shutdown_thread
def shutdown_worker():
...
reader = result_queue._reader
while True:
_add_call_item_to_queue(pending_work_items,
work_ids_queue,
call_queue)
sentinels = [p.sentinel for p in processes.values()]
assert sentinels
ready = wait([reader] + sentinels)
if reader in ready:
result_item = reader.recv()
else: #THIS BLOCK IS ENTERED WHEN THE ERROR OCCURS
# Mark the process pool broken so that submits fail right now.
executor = executor_reference()
if executor is not None:
executor._broken = True
executor._shutdown_thread = True
executor = None
# All futures in flight must be marked failed
for work_id, work_item in pending_work_items.items():
work_item.future.set_exception(
BrokenProcessPool(
"A process in the process pool was "
"terminated abruptly while the future was "
"running or pending."
))
# Delete references to object. See issue16284
del work_item
pending_work_items.clear()
# Terminate remaining workers forcibly: the queues or their
# locks may be in a dirty state and block forever.
for p in processes.values():
p.terminate()
shutdown_worker()
return
...
It is (or seems to be) a fact that a process terminates, but I have no clue why. Are my thoughts correct so far? What are possible causes that make a process terminate without a message? (Is this even possible?) Where could I apply further diagnostics? Which questions should I ask myself in order to come closer to a solution?
I am using python 3.5 on 64bit Linux.

I think I was able to get as far as possible:
I changed the _queue_management_worker method in my changed ProcessPoolExecutor module such that the exit code of the failed process is printed:
def _queue_management_worker(executor_reference,
processes,
pending_work_items,
work_ids_queue,
call_queue,
result_queue):
"""Manages the communication between this process and the worker processes.
...
"""
executor = None
def shutting_down():
return _shutdown or executor is None or executor._shutdown_thread
def shutdown_worker():
...
reader = result_queue._reader
while True:
_add_call_item_to_queue(pending_work_items,
work_ids_queue,
call_queue)
sentinels = [p.sentinel for p in processes.values()]
assert sentinels
ready = wait([reader] + sentinels)
if reader in ready:
result_item = reader.recv()
else:
# BLOCK INSERTED FOR DIAGNOSIS ONLY ---------
vals = list(processes.values())
for s in ready:
j = sentinels.index(s)
print("is_alive()", vals[j].is_alive())
print("exitcode", vals[j].exitcode)
# -------------------------------------------
# Mark the process pool broken so that submits fail right now.
executor = executor_reference()
if executor is not None:
executor._broken = True
executor._shutdown_thread = True
executor = None
# All futures in flight must be marked failed
for work_id, work_item in pending_work_items.items():
work_item.future.set_exception(
BrokenProcessPool(
"A process in the process pool was "
"terminated abruptly while the future was "
"running or pending."
))
# Delete references to object. See issue16284
del work_item
pending_work_items.clear()
# Terminate remaining workers forcibly: the queues or their
# locks may be in a dirty state and block forever.
for p in processes.values():
p.terminate()
shutdown_worker()
return
...
Afterwards I looked up the meaning of the exit code:
from multiprocessing.process import _exitcode_to_name
print(_exitcode_to_name[my_exit_code])
whereby my_exit_code is the exit code that was printed in the block I inserted to the _queue_management_worker. In my case the code was -11, which means that I ran into a segmentation fault. Finding the reason for this issue will be a huge task but goes beyond the scope of this question.

If you are using macOS, there is a known issue with how some versions of macOS uses forking that's not considered fork-safe by Python in some scenarios. The workaround that worked for me is to use no_proxy environment variable.
Edit ~/.bash_profile and include the following (it might be better to specify list of domains or subnets here, instead of *)
no_proxy='*'
Refresh the current context
source ~/.bash_profile
My local versions the issue was seen and worked around are: Python 3.6.0 on
macOS 10.14.1 and 10.13.x
Sources:
Issue 30388
Issue 27126

Multiprocessing Robust to Occasional Failures

I have a 100-1000 timeseries paths and a fairly expensive simulation that I'd like to parallelize. However, the library I'm using hangs on rare occasions and I'd like to make it robust to those issues. This is the current setup:
with Pool() as pool:
res = pool.map_async(simulation_that_occasionally_hangs, (p for p in paths))
all_costs = res.get()
I know get() has a timeout parameter but if I understand correctly that works on the whole process of the 1000 paths. What I'd like to do is check if any single simulation is taking longer than 5 minutes (a normal path takes 4 seconds) and if so just stop that path and continue to get() the rest.
EDIT:
Testing timeout in pebble
def fibonacci(n):
if n == 0: return 0
elif n == 1: return 1
else: return fibonacci(n - 1) + fibonacci(n - 2)
def main():
with ProcessPool() as pool:
future = pool.map(fibonacci, range(40), timeout=10)
iterator = future.result()
all = []
while True:
try:
all.append(next(iterator))
except StopIteration:
break
except TimeoutError as e:
print(f'function took longer than {e.args[1]} seconds')
print(all)
Errors:
RuntimeError: I/O operations still in flight while destroying Overlapped object, the process may crash
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\anaconda3\lib\multiprocessing\spawn.py", line 99, in spawn_main
new_handle = reduction.steal_handle(parent_pid, pipe_handle)
File "C:\anaconda3\lib\multiprocessing\reduction.py", line 87, in steal_handle
_winapi.DUPLICATE_SAME_ACCESS | _winapi.DUPLICATE_CLOSE_SOURCE)
PermissionError: [WinError 5] Access is denied

The pebble library has been designed to address these kinds of issues. It handles transparently job timeouts and failures such as C library crashes.
You can check the documentation examples to see how to use it. It has a similar interface as concurrent.futures.

Probably the easiest way is to run each heavy simulation in a separate subprocess, with the parent process watching it. Specifically:
def risky_simulation(path):
...
def safe_simulation(path):
p = multiprocessing.Process(target=risky_simulation, args=(path,))
p.start()
p.join(timeout) # Your timeout here
p.kill() # or p.terminate()
# Here read and return the output of the simulation
# Can be from a file, or using some communication object
# between processes, from the `multiprocessing` module
with Pool() as pool:
res = pool.map_async(safe_simulation, paths)
all_costs = res.get()
Notes:
If the simulation may hang, you may want to run it in a separate process (i.e. the Process object should not be a thread), as depending on how it's done, it may catch the GIL.
This solution only uses the pool for the immediate sub-processes, but the computations are off-loaded to new processes. We can also make sure the computations share a pool, but that would result in uglier code, so I skipped it.

Python: How to get hung process to release control back in concurrent.futures pool?

I have a parallelized a large CPU-intensive data processing task using the
concurrent.futures ProcessPoolExecutor method like shown below.
with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
futures_ocr = ([
executor.submit(
MyProcessor,
folder
) for folder in sub_folders
])
is_cancel = wait_for(futures_ocr)
if is_cancel:
print 'shutting down executor'
executor.shutdown()
def wait_for(futures):
"""Handes the future tasks after completion"""
cancelled = False
try:
for future in concurrent.futures.as_completed(futures, timeout=200):
try:
result = future.result()
print 'successfully finished processing folder: ', result.source_folder_path
except concurrent.futures.TimeoutError:
print 'TimeoutError occured'
except TypeError:
print 'TypeError occured'
except KeyboardInterrupt:
print '****** cancelling... *******'
cancelled = True
for future in futures:
future.cancel()
return cancelled
There are certain folders where the process seems to be stuck for a long time, not because of some error in the code but due to the nature of the files being processed. So, I wanted to timeout those types of processes, so that they return if a certain time limit is exceeded. The Pool can then use the process for the next available task.
Adding the timeout in the as_completed() function gives an error while completing.
Traceback (most recent call last):
File "call_ocr.py", line 96, in <module>
main()
File "call_ocr.py", line 42, in main
is_cancel = wait_for(futures_ocr)
File "call_ocr.py", line 59, in wait_for
for future in concurrent.futures.as_completed(futures, timeout=200):
File "/Users/saurav/.pyenv/versions/ocr/lib/python2.7/site-packages/concurrent/futures/_base.py", line 216, in as_completed
len(pending), len(fs)))
concurrent.futures._base.TimeoutError: 3 (of 3) futures unfinished
What am I doing wrong here, and what is the best way to cause timedout processes to stop and relinquish the process back to the Process pool?

The concurrent.futures implementation does not support such use case.
The timeout which can be passed to its functions and methods allows to set for how long to wait for results but has no effect on the actual computation itself.
The pebble library supports such use case.
from concurrent.futures import TimeoutError
from pebble import ProcessPool
def function(n):
return n
with ProcessPool() as pool:
future = pool.schedule(function, args=[1], timeout=10)
try:
results = future.result()
except TimeoutError as error:
print("function took longer than %d seconds" % error.args[1])

Failures with Python multiprocessing.Pool when maxtasksperchild is set

I am using Python 2.7.8 on Linux and am seeing a consistent failure in a program that uses multiprocessing.Pool(). When I set maxtasksperchild to None, then all is well, when testing across a variety of values for processes. But if I set maxtasksperchild=n (n>=1), then I invariably end with an uncaught exception. Here is the main block:
if __name__ == "__main__":
options = parse_cmdline()
subproc = Sub_process(options)
lock = multiprocessing.Lock()
[...]
pool = multiprocessing.Pool(processes=options.processes,
maxtasksperchild=options.maxtasksperchild)
imap_it = pool.imap(recluster_block, subproc.input_block_generator())
#import pdb; pdb.set_trace()
for count, result in enumerate(imap_it):
print "Count = {}".format(count)
if result is None or len(result) == 0:
# presumably error was reported
continue
(interval, block_id, num_hpcs, num_final, retlist) = result
for c in retlist:
subproc.output_cluster(c, lock)
print "About to close_outfile."
subproc.close_outfile()
print "About to close pool."
pool.close()
print "About to join pool."
pool.join()
For debugging I have added a print statement showing the number of times through the loop. Here are a couple runs:
$ $prog --processes=2 --maxtasksperchild=2
Count = 0
Count = 1
Count = 2
Traceback (most recent call last):
File "[...]reclustering.py", line 821, in <module>
for count, result in enumerate(imap_it):
File "[...]/lib/python2.7/multiprocessing/pool.py", line 659, in next
raise value
TypeError: 'int' object is not callable
$ $prog --processes=2 --maxtasksperchild=1
Count = 0
Count = 1
Traceback (most recent call last):
[same message as above]
If I do not set maxtasksperchild, the program runs to completion successfully. Also, if I uncomment the "import pdb; pdb.set_trace()" line and enter the debugger, then the problem does not appear (Heisenbug). So, am I doing something wrong in the code here? Are there conditions on the code that generates the input (subproc.input_block_generator) or the code that processes it (recluster_block), that are known to cause issues like this? Thanks!

maxtasksperchild causes multiprocessing to respawn child processes. The idea is to get rid of any cruft that is building up. The problem is, you can get new cruft from the parent. When the child respawns, it gets the current state of the parent process, which is different than the orignal spawn. You are doing your work in the script's global namespace, so you are changing the environment the child will see quite a bit. Specifically, you use a variable called 'count' that masks a previous 'from itertools import count' statement.
To fix this:
use namespaces (itertools.count, like you said in the comment) to reduce name collisions
do your work in a function so that local variables aren't propagated to the child.

Python multiprocessing: synchronizing file-like object

I'm trying to make a file like object which is meant to be assigned to sys.stdout/sys.stderr during testing to provide deterministic output. It's not meant to be fast, just reliable. What I have so far almost works, but I need some help getting rid of the last few edge-case errors.
Here is my current implementation.
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
from os import getpid
class MultiProcessFile(object):
"""
helper for testing multiprocessing
multiprocessing poses a problem for doctests, since the strategy
of replacing sys.stdout/stderr with file-like objects then
inspecting the results won't work: the child processes will
write to the objects, but the data will not be reflected
in the parent doctest-ing process.
The solution is to create file-like objects which will interact with
multiprocessing in a more desirable way.
All processes can write to this object, but only the creator can read.
This allows the testing system to see a unified picture of I/O.
"""
def __init__(self):
# per advice at:
# http://docs.python.org/library/multiprocessing.html#all-platforms
from multiprocessing import Queue
self.__master = getpid()
self.__queue = Queue()
self.__buffer = StringIO()
self.softspace = 0
def buffer(self):
if getpid() != self.__master:
return
from Queue import Empty
from collections import defaultdict
cache = defaultdict(str)
while True:
try:
pid, data = self.__queue.get_nowait()
except Empty:
break
cache[pid] += data
for pid in sorted(cache):
self.__buffer.write( '%s wrote: %r\n' % (pid, cache[pid]) )
def write(self, data):
self.__queue.put((getpid(), data))
def __iter__(self):
"getattr doesn't work for iter()"
self.buffer()
return self.__buffer
def getvalue(self):
self.buffer()
return self.__buffer.getvalue()
def flush(self):
"meaningless"
pass
... and a quick test script:
#!/usr/bin/python2.6
from multiprocessing import Process
from mpfile import MultiProcessFile
def printer(msg):
print msg
processes = []
for i in range(20):
processes.append( Process(target=printer, args=(i,), name='printer') )
print 'START'
import sys
buffer = MultiProcessFile()
sys.stdout = buffer
for p in processes:
p.start()
for p in processes:
p.join()
for i in range(20):
print i,
print
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
print
print 'DONE'
print
buffer.buffer()
print buffer.getvalue()
This works perfectly 95% of the time, but it has three edge-case problems. I have to run the test script in a fast while-loop to reproduce these.
3% of the time, the parent process output isn't completely reflected. I assume this is because the data is being consumed before the Queue-flushing thread can catch up. I haven't though of a way to wait for the thread without deadlocking.
.5% of the time, there's a traceback from the multiprocess.Queue implementation
.01% of the time, the PIDs wrap around, and so sorting by PID gives the wrong ordering.
In the very worst case (odds: one in 70 million), the output would look like this:
START
DONE
302 wrote: '19\n'
32731 wrote: '0 1 2 3 4 5 6 7 8 '
32732 wrote: '0\n'
32734 wrote: '1\n'
32735 wrote: '2\n'
32736 wrote: '3\n'
32737 wrote: '4\n'
32738 wrote: '5\n'
32743 wrote: '6\n'
32744 wrote: '7\n'
32745 wrote: '8\n'
32749 wrote: '9\n'
32751 wrote: '10\n'
32752 wrote: '11\n'
32753 wrote: '12\n'
32754 wrote: '13\n'
32756 wrote: '14\n'
32757 wrote: '15\n'
32759 wrote: '16\n'
32760 wrote: '17\n'
32761 wrote: '18\n'
Exception in thread QueueFeederThread (most likely raised during interpreter shutdown):
Traceback (most recent call last):
File "/usr/lib/python2.6/threading.py", line 532, in __bootstrap_inner
File "/usr/lib/python2.6/threading.py", line 484, in run
File "/usr/lib/python2.6/multiprocessing/queues.py", line 233, in _feed
<type 'exceptions.TypeError'>: 'NoneType' object is not callable
In python2.7 the exception is slightly different:
Exception in thread QueueFeederThread (most likely raised during interpreter shutdown):
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 552, in __bootstrap_inner
File "/usr/lib/python2.7/threading.py", line 505, in run
File "/usr/lib/python2.7/multiprocessing/queues.py", line 268, in _feed
<type 'exceptions.IOError'>: [Errno 32] Broken pipe
How do I get rid of these edge cases?

The solution came in two parts. I've successfully run the test program 200 thousand times without any change in output.
The easy part was to use multiprocessing.current_process()._identity to sort the messages. This is not a part of the published API, but it is a unique, deterministic identifier of each process. This fixed the problem with PIDs wrapping around and giving a bad ordering of output.
The other part of the solution was to use multiprocessing.Manager().Queue() rather than the multiprocessing.Queue. This fixes problem #2 above because the manager lives in a separate Process, and so avoids some of the bad special cases when using a Queue from the owning process. #3 is fixed because the Queue is fully exhausted and the feeder thread dies naturally before python starts shutting down and closes stdin.

I have encountered far fewer multiprocessing bugs with Python 2.7 than with Python 2.6. Having said this, the solution I used to avoid the "Exception in thread QueueFeederThread" problem is to sleep momentarily, possibly for 0.01s, in each process in which the the Queue is used. It is true that using sleep is not desirable or even reliable, but the specified duration was observed to work sufficiently well in practice for me. You can also try 0.1s.