Releasing memory of huge numpy array in IPython

Python Programming

Question or problem about Python programming:

UPDATE:- This problem solved itself after a machine reboot. Not yet able to figure out why this error was happening before.

I have a function that loads a huge numpy array (~ 980MB) and returns it.

When I first start Ipython and call this function, it loads the array into the variable without any problem.

But if I run the same command again, it exits raising a “Memory Error”.

I tried the following,

del hugeArray

Still the same error was occurring.
I even tried the following

del hugeArray

Initially, gc.collect() returned 145 and the second call returned 48.
But even after this when I call the function, it was still raising a Memory error.

The only way I could load again was to restart ipython.
Is there something I can do to free all memory in ipython, so that I don’t have to restart it?


Following is the output of %whos

Variable   Type      Data/Info
gc         module    
gr         module    <...>rom 'Generate4mRamp.pyc'>
np         module    <...>ages/numpy/__init__.pyc'>
plt        module    <...>s/matplotlib/pyplot.pyc'>

Out of this, gr is my module containing the function which i used to load the data cube.

———How to Reproduce the error

The following simple function is able to reproduce the error.

import numpy as np
import gc

def functionH():
    return cube

testcube=functionH()   #Runs without any issue 

del testcube
testcube=functionH()  # Raises Memory Error

del testcube
testcube=functionH()  # Still Raises Memory Error

This error is occurring only in Ipython. In simple python (>>>) after giving del testcube, there is no Memory Error.

How to solve the problem:

Are you looking at the value? IPython caches output variables as e.g. Out[8], so if you examine it, it will be kept in memory.

You can do %xdel testcube to delete the variable and remove it from IPython’s cache. Alternatively, %reset out or %reset array will clear either all your output history, or only references to numpy arrays.

Hope this helps!