Why Can\'t I Create Large NumPy Arrays on Ubuntu, and How Can I Fix the Memory Allocation Error?

Memory Allocation Issue in Numpy Array Creation

Problem

When creating a large NumPy array with 'uint8' data type on Ubuntu 18, you may encounter the error:

numpy.core._exceptions.MemoryError: Unable to allocate array with shape and data type uint8

This occurs despite the system having ample memory available and not experiencing the same issue on MacOS.

Solution

The root cause of this issue is the operating system's overcommit handling mode. By default, overcommit is disabled, which means the kernel will refuse allocations that exceed the available memory.

To resolve this:

1. Check the current overcommit mode by running cat /proc/sys/vm/overcommit_memory.
2. Enable aggressive overcommit by running echo 1 > /proc/sys/vm/overcommit_memory (as root).

Understanding Overcommit

With aggressive overcommit enabled, the system allows allocations even if they exceed physical memory. This is because the kernel expects that only a fraction of the allocated memory will be actively used.

Implications

While aggressive overcommit can solve the allocation issue, it should be used with caution:

• Avoid using aggressive overcommit for non-sparse arrays, as it can lead to memory exhaustion.
• If you're manually writing to memory locations, ensure that page faults are explicitly triggered to allocate physical memory.
• Be aware that the system may experience performance degradation if the allocated memory is actively used.

Example

With aggressive overcommit enabled, the following code should work:

import numpy as np
a = np.zeros((156816, 36, 53806), dtype='uint8')
print(a.nbytes)  # Output: 303755101056

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