On 01/12/16 01:12, Chris Kaynor wrote:
> On Wed, Nov 30, 2016 at 4:54 PM, duncan smith <firstname.lastname@example.org> wrote:
>> Thanks. So something like the following might do the job?
>> def _execute(command):
>> p = subprocess.Popen(command, shell=False,
>> out_data, err_data = p.communicate()
>> if err_data:
>> print err_data
> I did not notice it when I sent my first e-mail (but noted it in my
> second one) that the docstring in to_image is presuming that
> shell=True. That said, as it seems everybody is at a loss to explain
> your issue, perhaps there is some oddity, and if everything appears to
> work with shell=False, it may be worth changing to see if it does fix
> the problem. With other information since provided, it is unlikely,
> Not specifying the stdin may help, however it will only reduce the
> file handle count by 1 per call (from 2), so there is probably a root
> problem that it will not help.
> I would expect the communicate change to fix the problem, except for
> your follow-up indicating that you had tried that before without
> Removing the manual stdout.read may fix it, if the problem is due to
> hanging processes, but again, your follow-up indicates thats not the
> problem - you should have zombie processes if that were the case.
> A few new questions that you have not answered (nor have they been
> asked in this thread): How much memory does your system have? Are you
> running a 32-bit or 64-bit Python? Is your Python process being run
> with any additional limitations via system commands (I don't know the
> command, but I know it exists; similarly, if launched from a third
> app, it could be placing limits)?
8 Gig, 64 bit, no additional limitations (other than any that might be
imposed by IDLE). In this case the simulation does consume *a lot* of
memory, but that hasn't been the case when I've hit this in the past. I
suppose that could be the issue here. I'm currently seeing if I can
reproduce the problem after adding the p.communicate(), but it seems to
be using more memory than ever (dog slow and up to 5 Gig of swap). In
the meantime I'm going to try to refactor to reduce memory requirements
- and 32 Gig of DDR3 has been ordered. I'll also dig out some code that
generated the same problem before to see if I can reproduce it. Cheers.