Diagnosing an issue with plugin modules, process pools, and the import system


Wasim Lorgat


July 10, 2022

This is a walkthrough of the rather surprising mechanics underlying a failing test that brings together plugin modules (that register themselves on import), process pools, and Python’s import system.

What went wrong

It starts with a failed test:

pat = r"\[\d, \d+.\d+, \d+.\d+, \d+.\d+, '\d\d:\d\d'\]"
test_stdout(lambda: learn.fit(1), pat, regex=True)

The idea is to test that calling learn.fit(1) writes text to stdout (standard output) that matches the regex pattern pat. For those less familiar, this form of testing is common in projects that use nbdev.

Here’s what actually happens:

  1. The nbprocess_test command creates a ProcessPoolExecutor with some number of workers and tasks (each notebook being a separate task).
  2. Worker 1 processes one task, say task 1, which creates an IPython InteractiveShell, then runs import fastai.callbacks.progress which adds ProgressCallback to the the variable fastcore.basics.defaults.
  3. Worker 1 processes another task, say task 3, which creates a fresh InteractiveShell, calls learner.fit, and tests stdout. Since ProgressCallback has been registered in this process by task 1, a progress bar is also printed to stdout, breaking the test.

Let’s break down the underlying mechanics. There are three behaviours that come together to cause this sequence of events:

  1. ProcessPoolExecutors reuse processes. It seems obvious in hindsight since that’s how pools usually work, but I had never realised it until now. In the example above, worker 1 executes task 1 and task 3 in the same process.
  2. fastai callbacks register themselves on import. In this case, fastai.callbacks.progress adds ProgressCallback to defaults.callbacks.
  3. Changes to imported modules persist across IPython InteractiveShells. nbprocess_test runs each test in parallel using execnb, which implements a sub-class of InteractiveShell.

Next, we’ll verify these behaviours with tiny experiments. I highly recommend using tiny experiments to understand complex systems.

ProcessPoolExecutors reuse processes

Perhaps we should know this simply from the name, but I didn’t, so we’ll figure it out with a tiny experiment. Start by creating a pool with 2 max_workers:

from concurrent.futures import ProcessPoolExecutor

pool = ProcessPoolExecutor(max_workers=2)

There aren’t any processes in the pool yet:


Submit a task: the function os.getpid, which will return the process ID of the worker that runs it. Since there are no processes in the pool, submit will start a new worker process, and have it execute the task. ProcessPoolExecutor.submit returns a Future object, and Future.result returns the task’s return value:

import os

future = pool.submit(os.getpid)

No matter how many times you manually rerun the above cell, it will aways be executed on the same process. Notice that the process is now also available in the pool:

{45907: <SpawnProcess name='SpawnProcess-1' pid=45907 parent=45899 started>}

If we submit another task:

future = pool.submit(os.getpid)

…it’s still executed on the same process.

Let’s try executing two processes at the same time:

futures = [pool.submit(os.getpid) for _ in range(2)]
[o.result() for o in futures]
[45907, 45907]

Weird. They’re both executed on the same process…

{45907: <SpawnProcess name='SpawnProcess-1' pid=45907 parent=45899 started>,
 45908: <SpawnProcess name='SpawnProcess-2' pid=45908 parent=45899 started>}

It looks like another process was started! I haven’t confirmed this, but I suspect that when we submitted two futures, the pool determined that it needed more workers, so it started another. However, the first worker’s task ended before the second worker started up, so the first worker processed both.

Since we instantiated the pool with 2 max_workers, these two processes will execute all tasks, no matter how many we submit:

futures = [pool.submit(os.getpid) for _ in range(10)]
[o.result() for o in futures]
[45907, 45907, 45907, 45907, 45907, 45907, 45907, 45907, 45907, 45907]

Shutdown the pool to free up any resources:


fastai callbacks register themselves on import

This one is easy to demonstrate. defaults has no callbacks attribute to start with:

from fastcore.basics import defaults


defaults.callbacks is populated after importing ProgressCallback:

from fastai.callback.progress import ProgressCallback


Changes to imported modules persist across IPython InteractiveShells

Why is any of the above a problem? Didn’t we say that nbprocess_test creates a separate shell for each notebook? Yes it does, but it turns out that changes to imported modules persist across shells.

from execnb.shell import CaptureShell

First make sure that CaptureShell doesn’t have a foo attribute - this will make sense in a second:

assert not hasattr(CaptureShell, 'foo')

Now add the foo attribute:

CaptureShell.foo = 'bar'

We can see foo inside a CaptureShell:

shell = CaptureShell()
shell.run('from execnb.shell import CaptureShell; CaptureShell.foo')

This happens because when we first imported from the execnb.shell module it was cached in sys.modules:

import sys

<module 'execnb.shell' from '/Users/seem/code/execnb/execnb/shell.py'>

In fact, sys.modules['execnb.shell'].CaptureShell is another name for CaptureShell:

sys.modules['execnb.shell'].CaptureShell is CaptureShell

Python caches imports to speed up consecutive imports from the same modules. InteractiveShell (and its sub-classes) have the same sys.modules which causes this behaviour:

shell = CaptureShell()
shell.run("import sys; sys.modules['execnb.shell'].CaptureShell.foo")

exec with empty globals and locals uses the same sys.modules too so it doesn’t avoid the issue:

exec("import sys; print('execnb.shell' in sys.modules)", {}, {})
exec("from execnb.shell import CaptureShell; print(CaptureShell.foo)", {}, {})

In the end, we agreed that the test itself was broken, because it made assumptions about its environment without ensuring that they were true. Tests like this may fail - regardless of the above behaviour - if we import any module that registers a callback. For example, this fails too:

from fastai.callback.progress import *
from nbprocess.test import test_nb


The fix is to explicitly run learner.fit with the precise list of callbacks required.