MultiProc goes into infinite loop (resource management related)

[chrisgorgo]

Actual behavior

In particular occasions MultiProc claims a job is running, but actually goes into an infinite loop and never finishes.

Expected behavior.

Tasks should either run and finish or an exception should be raised if there aren't enough resources to run the task.

How to replicate the behavior

Run MRIQC 0.10.4 on https://openneuro.org/datasets/ds001338/versions/00001 with docker being allocated 7GB of RAM and 2 CPUs

Full command to use

mriqc //d/data/ParanoiaStory_20180421_154028/ParanoiaStory/ //d/scratch/mriqc_out participant --participant_label tb3858 --n_procs 2 --verbose

[chrisgorgo]

The job MRIQC is running forever in this particular occasion is compute_tsnr.

[chrisgorgo]

More info - if I run the same workflow in linear it works. If I increase amount of available ram to 15gb it also works.

[oesteban]

My bet is that, due to a MemoryError, one process had to get killed. In the latest refactor of MultiProc, we tried to make sure that child processes killed were reported. However, (and this may be a design problem of multiprocessing) if the worker is killed, a new one is spun up silently (https://github.com/python/cpython/blob/master/Lib/multiprocessing/pool.py#L406). I guess the node kept running as a zombie for a little while since the pool was running in non-daemon mode.

I'll start writing a unit test for this:

1. Spin up a workflow, and run MultiProc with n_proc=2.
2. Start two endless nodes, which store their PID in a common place.
3. Query the PIDs of the workers
4. Kill one worker (use the PIDs of 2. to make sure none of the tasks are killed directly)
5. Check whether Nipype ends with error or keeps going silently.

WDYT @satra?

[oesteban]

A proof of concept that successfully replicates this problem:

from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu

wf = pe.Workflow('testworkflow')
def _print():
    import os
    from time import sleep
    while True:
        print(os.getpid())
        sleep(10)

wf.add_nodes([
    pe.Node(niu.Function(function=_print), name='node1'),
    pe.Node(niu.Function(function=_print), name='node2')
])
wf.run('MultiProc', plugin_args={'n_procs': 2})

Then I killed one (or both) PIDs printed during runtime. Killing the processes kept Nipype indefinitely polling without error.

[oesteban]

Okay, since the PID of the worker and the node are the same, I wanted to check whether this happens because the death of the worker, the child or both:

from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces.base import CommandLine

wf = pe.Workflow('testworkflow')

wf.add_nodes([
    pe.Node(CommandLine(command='bash', args='-c "while true; do echo $$: running node1; sleep 10; done"'), terminal_output='stream', name='node1'),
    pe.Node(CommandLine(command='bash', args='-c "while true; do echo $$: running node2; sleep 10; done"'), terminal_output='stream', name='node2')
])
wf.run('MultiProc', plugin_args={'n_procs': 2})

Each node1 and node2 fork a command /usr/bin/sh -c 'bash -c "while true; do echo $$: running node1; sleep 10; done"', with again forks and starts a bash -c "while true; do echo $$: running node1; sleep 10; done"

• If I kill any of the workers: everything keeps running as if nothing happened.
• If I kill the child of the process forked by Nipype (without /usr/bin/sh): everything keeps running as if nothing happened. The process persists as a zombie.
• If I kill the process directly forked by Nipype (with /usr/bin/sh): the process is killed, the child is finally killed (and the zombie cleared up) and Nipype gets informed, getting:

  RuntimeError: Command:
  bash -c "while true; do echo $$: running node2; sleep 10; done"
  Standard output:
  26044: running node2
  ...
  26044: running node2
  Standard error:
  Return code: -9
  

  Consequently, the other node keeps going:

  180423-15:52:00,776 workflow DEBUG:
  	 Tasks currently running: 1. Pending: 1.
  180423-15:52:00,777 workflow INFO:
  	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 1/2.
                       Currently running:
                         * testworkflow.node1
  180423-15:52:02,774 workflow DEBUG:
  	 Progress: 2 jobs, 1/1/0 (done/running/ready), 1/0 (pending_tasks/waiting).
  180423-15:52:02,774 workflow DEBUG:
  	 Tasks currently running: 1. Pending: 1.
  

[oesteban]

https://bugs.python.org/msg315684

[satra]

https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures - that is very recent addition to python (won't work on 2.7 or 3.4- as far as i know). but they are trying to move everyone off of 2.7.

[effigies]

There is a 2.7 backport, if we want to move to concurrent.futures: https://pypi.org/project/futures/

[oesteban]

About the backport:

The ProcessPoolExecutor class has known (unfixable) problems on Python 2 and should not be relied on for mission critical work.

I've used concurrent.futures and it is very convenient.

One early question I have is: is it possible to change the context? (e.g. we use the 'forkserver' context in FMRIPREP to save memory). I think it is, but we would need to check first.

Then, I have to say, this change will probably mean a deep rewrite of MultiProc, and I think we have enough refactoring with nipype-2.0.

With the bug report (https://bugs.python.org/issue22393), the reporter also filed a patch. We could test this patch in nipype, and distribute a patched multiprocessing.Pool. WDYT?

[oesteban]

Hmm, I'm seeing that in https://bugs.python.org/issue22393 the author says the patch is prone to https://bugs.python.org/issue6721 - deadlocks when forking + logging (which we extensively do).

This would point at concurrent.futures again.

An alternative option is have MultiProc deprecated. If python >=3.5 then concurrent.futures is used. Otherwise, the old MultiProc is used and a big fat WARNING is issued.

Opinions?

[oesteban]

BTW, I will remove my self-assignment because I don't think I can undertake this issue at the moment. If nobody takes it then I can re-consider /cc @chrisfilo