having a strange issue when trying to run a simple "hello world" program with MPI.
I eventually want to use 100 processes for this MPI script I'm writing in python and was even able to run the hello world test earlier with up to 100 processes. However, now I keep encountering the same error when I try to run the script with ~50 processes.
The specific error I see seems to be stating:
ORTE_ERROR_LOG: The system limit on number of network connections a process can open was reached in file util/listener.c at line 321
After trying to research this, I understand that it has something to do with a process running out of file descriptors and it seems like the most common solutions state that a file is not closing properly. However, my issue here is, I'm not opening any files? My script is just:
print('I am process:', rank)
So what could the issue be stemming from here?
I seem to have found a slight workaround.
I am working on a Mac, so I'm assuming that earlier I was able to stay under my file limit that is at a certain default amount set by the OS. By configuring the max file limit, I was able to bypass the limit amount I was originally hitting, causing my program to crash.
This fix isn't ideal, since my script now takes quite a while to run, but it is at least a temporary one until I can find a better fix.
If anyone would like to attempt this, the solution I found was posted by #tombigel on GitHub and can be found here.
Related
I am facing a problem with a python script getting killed. I had always used this script with no problem at all until two days ago, then it started to print, without any change in the code, the string 'killed' before aborting the execution.
Other people have tried to run the same code on their system and it works fine, as it used to do with me until two days ago.
I have read some old similar question, and I have got the problem could be an out-of-memory issue due to a bad memory management in my code. It sounds a little strange to me, since it used to work perfectly until some days ago and the problem appears on my system only.
Do you have any idea on how to inspect the problem and find a possible solution, please?
Python version: Python 2.7.14+
System: Scientific Linux CERN 7
In your case, it's highly probale that the script you're processing reached some given limit of the amount of resources it's able to use and that depends on your OS and other parameters, are you running something else with the script ? or are there many open files etc ?
The most likely reason for such an error is exceeding memory use, whiwh forces the system to not take risks and break when allocating more starts failing. Maybe you can print in parallel the total memory you're using to have a glimpse of what's happening since the information you've given are not enough to help you :
import os, psutil
process = psutil.Process(os.getpid())
then: (for python 3)
print(process.memory_info().rss)
or: (for python 2.7) (tested)
print(process.memory_info()[0])
I've got a list of files for each of which I'm calling sox. Because it takes a while I thought I'd speed the process up by parallelizing it, each call to sox is independent of each other so I thought it'd be a simple thing.
But it seems you cannot call the same executable from a different process, as that leads to an The process cannot access the file because it is being used by another process. error.
I'm guessing that is the cause because there's no other file I'm using across different processes. And yet I'm quite surprised by this, why would RO access not be possible? And does that really mean there's absolutely no way for me to speed my program up?
Found the error. I had at the end of my sox command 2> $nul to suppress the output. That was of course causing issues. :D
I have a python script, which is used to perform a lab measurement using several devices. The whole setup is rather involved, including communication over serial devices, API calls as well as the use of self-written and commercial drivers. In the end, however, everything boils down to two nested loops, which vary some parameters, collect data and write it to a file.
My problem is that I observe random occurences of a MemoryError, typically after 10 hours, equivalent to ~15k runs of the loops. At the moment, I don't have an idea, where it comes from or how I can trace it further. So I would be happy for suggestions, how to work on my problem. My observations up to this moment are as follows.
The error occurs at random states of the program. Different runs will throw the MemoryError at different lines of my script.
There is never any helpful error message. Python only says MemoryError without any error string. The traceback leads me to some point in the script, where memory is needed (e.g. when building a list), but it appears to be no specific instruction, which is the problem.
My RAM is far from full. The python process in question typically consumes some ten MB of RAM when viewed in the task manager. In addition, the RAM usage appears to be stable for hours. Usually, it increases slowly for some time, just to drop to down to the previous level quickly, which I interpret as the garbage collector kicking in periodically.
So far I did not find any indications for a memory leak. I used memory_profiler to trace the memory usage of my functions and found it to be stable. In addition, I followed this blog entry to observe what the garbage collector does in detail. Again, I could not find any hints for undeleted objects.
I am stuck to Win7 x86 due to a driver, which will only work on a 32bit system. So I cannot follow suggestions like this to go to a 64 bit version of Windows. Anyway, I do not see, how this would help in my situation.
The iPython console, from which the script is being launched, often behaves strange after the error occurred. Sometimes, a new MemoryError is thrown even for very simple operations. Often, the console is marked by Windows as "not responding" after some time. A menu pops up, where besides the usual options to wait for the process or to terminate it, there is a third option to "restore" the program (whatever that means). Doing so usually causes the console to work normal again.
At this point, I am somewhat out of ideas on how to proceed. The general receipe to comment out parts of the script until it works is highly undesirable in my case. As stated above, each test run will take several hours, meaning a potential downtime of weeks for my lab equipment. Going that direction, appears unfeasable to me. Is there any more direct approach to learn, what is crashing behind the scenes? How can I understand that python apparently fails to malloc?
scipy.fft seems to hang when running this simple script:
import scipy
from scipy.io import wavfile
sound = 'sounds/silence/iPhone5.wav'
fs, data = wavfile.read(sound)
print scipy.fft(data)
on certain files. Try this file for example.
A few things I noticed:
Running the individual commands from the interactive interpreter does not hang.
Running with other sound files does not always hang the script (it's not just this file that isn't working though)
Sometimes I get WavFileWarning: chunk not understood, but it doesn't seem to be related to when it happens
If I terminate the script with Ctrl+C I get the result as if it never got stuck.
Opening the file with wave or audiolab leads to the same result.
Is this a bug or am I doing something wrong?
Check the value of data.shape for the files that hang up the system. If your data length happens to be a prime number, or the product of several large prime numbers, there isn't much that the FFT algorithm can do to speed up calculation of the DFT. If you pad with zeros, or trim your data to the nearest power of 2, everything should run much, much faster.
This should have been a comment, but there's just not enough space there...
You could do a bit more debugging, which might help a bit.
(Assuming you're on some sort of unix-like OS)
When the program gets stuck, does it idle or use a lot of CPU? You could use "top" or similar to check.
What is the program doing when it appears stuck? Can you get a stack trace? Either using a debugger like gdb or some other tool.
And I guess what really should be step one. Search the net for your symptoms. If it is a bug, it is likely already found and reported. It might even be fixed already.
By looking at a stack trace it should be possible to see if the program is stuck waiting for something, stuck in a loop somewhere or just doing lots of work.
It might also be able to tell you if the problem is in python code, C extensions or somewhere else. Being used to read stack traces is of course a plus. :)
I have a problem that I seriously spent months on now!
Essentially I am running code that requires to read from and save to HD5 files. I am using h5py for this.
It's very hard to debug because the problem (whatever it is) only occurs in like 5% of the cases (each run takes several hours) and when it gets there it crashes python completely so debugging with python itself is impossible. Using simple logs it's also impossible to pinpoint to the exact crashing situation - it appears to be very random, crashing at different points within the code, or with a lag.
I tried using OllyDbg to figure out whats happening and can safely conclude that it consistently crashes at the following location: http://i.imgur.com/c4X5W.png
It seems to be shortly after calling the python native PyObject_ClearWeakRefs, with an access violation error message. The weird thing is that the file is successfully written to. What would cause the access violation error? Or is that python internal (e.g. the stack?) and not file (i.e. my code) related?
Has anyone an idea whats happening here? If not, is there a smarter way of finding out what exactly is happening? maybe some hidden python logs or something I don't know about?
Thank you
PyObject_ClearWeakRefs is in the python interpreter itself. But if it only happens in a small number of runs, it could be hardware related. Things you could try:
Run your program on a different machine. if it doesn't crash there, it is probably a hardware issue.
Reinstall python, in case the installed version has somehow become corrupted.
Run a memory test program.
Thanks for all the answers. I ran two versions this time, one with a new python install and my same program, another one on my original computer/install, but replacing all HDF5 read/write procedures with numpy read/write procedures.
The program continued to crash on my second computer at odd times, but on my primary computer I had zero crashes with the changed code. I think it is thus safe to conclude that the problems were HDF5 or more specifically h5py related. It appears that more people encountered issues with h5py in that respect. Given that any error in my application translates to potentially large financial losses I decided to dump HDF5 completely in favor of other stable solutions.
Use a try catch statement. This can be put into the program in order to stop the program from crashing when erroneous data is entered