I'm trying to execute a Python script, but I am getting the following error:
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
I'm using python 3.5.2 on a Linux Mint 18.1 Serena OS
Can someone tell me why this happens, and how can I solve?
The SIGSEGV signal indicates a "segmentation violation" or a "segfault". More or less, this equates to a read or write of a memory address that's not mapped in the process.
This indicates a bug in your program. In a Python program, this is either a bug in the interpreter or in an extension module being used (and the latter is the most common cause).
To fix the problem, you have several options. One option is to produce a minimal, self-contained, complete example which replicates the problem and then submit it as a bug report to the maintainers of the extension module it uses.
Another option is to try to track down the cause yourself. gdb is a valuable tool in such an endeavor, as is a debug build of Python and all of the extension modules in use.
After you have gdb installed, you can use it to run your Python program:
gdb --args python <more args if you want>
And then use gdb commands to track down the problem. If you use run then your program will run until it would have crashed and you will have a chance to inspect the state using other gdb commands.
Another possible cause (which I encountered today) is that you're trying to read/write a file which is open. In this case, simply closing the file and rerunning the script solved the issue.
After some times I discovered that I was running a new TensorFlow version that gives error on older computers. I solved the problem downgrading the TensorFlow version to 1.4
When I encounter this problem, I realize there are some memory issues. I rebooted PC and solved it.
This can also be the case if your C-program (e.g. using cpython is trying to access a variable out-of-bound
ctypedef struct ReturnRows:
double[10] your_value
cdef ReturnRows s_ReturnRows # Allocate memory for the struct
s_ReturnRows.your_value = [0] * 12
will fail with
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
For me, I was using the OpenCV library to apply SIFT. In my code, I replaced cv2.SIFT() to cv2.SIFT_create() and the problem is gone.
I got this error in PHP, while running PHPUnit. The reason was a circular dependency.
I received the same error when trying to connect to an Oracle DB using the pyodbc module:
connection = pyodbc.connect()
The error occurred on the following occasions:
The error message could be avoided with the following approaches:
Hope, that will help anyone!
11 : SIGSEGV - This signal is arises when an memory segement is illegally accessed.
There is a module name signal in python through which you can handle this kind of OS signals.
If you want to ignore this SIGSEGV signal, you can do this:
signal.signal(signal.SIGSEGV, signal.SIG_IGN)
However, ignoring the signal can cause some inappropriate behaviours to your code, so it is better to handle the SIGSEGV signal with your defined handler like this:
def SIGSEGV_signal_arises(signalNum, stack):
print(f"{signalNum} : SIGSEGV arises")
# Your code
signal.signal(signal.SIGSEGV, SIGSEGV_signal_arises)
I encountered this problem when I was trying to run my code on an external GPU which was disconnected. I set os.environ['PYOPENCL_CTX']=2 where GPU 2 was not connected. So I just needed to change the code to os.environ['PYOPENCL_CTX'] = 1.
For me these three lines of code already reproduced the error, no matter how much free memory was available:
import numpy as np
from sklearn.cluster import KMeans
X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=1, random_state=0).fit(X)
I could solve the issue by removing an reinstalling the scikit-learn package. A very similar solution to this.
This can also occur if trying to compound threads using concurrent.futures. For example, calling .map inside another .map call.
This can be solved by removing one of the .map calls.
I had the same issue working with kmeans from scikit-learn. Upgrading from scikit-learn 1.0 to 1.0.2 solved it for me.
This issue is often caused by incompatible libraries in your environment. In my case, it was the pyspark library.
I got this error when importing monai. It was solved after I created a new conda environment. Possible reasons I could imagine were either that there were some conflict between different packages, or maybe that my environment name was the same as the package name I wanted to import (monai).
in my case it was a pickled file, specifically a pandas DataFrame. deleting the pickled file fixed the issue.
similar to this:
from pandas import DataFrame
df = DataFrame()
# somewhere
df.from_pickle('my_path.p')
# somewhere later
df.to_pickle('my_path.p')
found on other page. interpreter: python 3.8
cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
this solved issue for me. i was getting SIGSEGV with 2.7, upgraded my python to 3.8 then got different error with OpenCV. and found answer on OpenCV 4.0.0 SystemError: <class 'cv2.CascadeClassifier'> returned a result with an error set.
but eventually one line of code fixed it.
