I'm facing an issue with allocating huge arrays in numpy on Ubuntu 18 while not facing the same issue on MacOS.

I am trying to allocate memory for a numpy array with shape (156816, 36, 53806) with

np.zeros((156816, 36, 53806), dtype='uint8')

and while I'm getting an error on Ubuntu OS

>>> import numpy as np
>>> np.zeros((156816, 36, 53806), dtype='uint8')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
numpy.core._exceptions.MemoryError: Unable to allocate array with shape (156816, 36, 53806) and data type uint8

I'm not getting it on MacOS:

>>> import numpy as np 
>>> np.zeros((156816, 36, 53806), dtype='uint8')
array([[[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]],

       ...,

       [[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)

I've read somewhere that np.zeros shouldn't be really allocating the whole memory needed for the array, but only for the non-zero elements. Even though the Ubuntu machine has 64gb of memory, while my MacBook Pro has only 16gb.

versions:

Ubuntu
os -> ubuntu mate 18
python -> 3.6.8
numpy -> 1.17.0

mac
os -> 10.14.6
python -> 3.6.4
numpy -> 1.17.0

PS: also failed on Google Colab

In the default mode, 0,

Heuristic overcommit handling. Obvious overcommits of address space are refused. Used for a typical system. It ensures a seriously wild allocation fails while allowing overcommit to reduce swap usage. root is allowed to allocate slightly more memory in this mode. This is the default.

The exact heuristic used is not well explained here, but this is discussed more on Linux over commit heuristic and on this page.

You can check your current overcommit mode by running

$ cat /proc/sys/vm/overcommit_memory
0​

>>> 156816 * 36 * 53806 / 1024.0**3
282.8939827680588

~282 GB, and the kernel is saying well obviously there's no way I'm going to be able to commit that many physical pages to this, and it refuses the allocation.

If (as root) you run:

$ echo 1 > /proc/sys/vm/overcommit_memory

>>> import numpy as np
>>> a = np.zeros((156816, 36, 53806), dtype='uint8')
>>> a.nbytes
303755101056

You can then go ahead and write to any location within the array, and the system will only allocate physical pages when you explicitly write to that page. So you can use this, with care, for sparse arrays.

change the data type to another one which uses less memory works. For me, I change the data type to numpy.uint8:

data['label'] = data['label'].astype(np.uint8)