Why Can\'t I Allocate a Large Numpy Array, and How Can I Fix It?

Unable to Allocate Large Array in Numpy: Understanding the Error and its Resolution

The issue of facing an "Unable to allocate array with shape and data type" error when attempting to create a large Numpy array can be attributed to the overcommit handling mode of the system.

Memory Overcommit Handling

In Linux systems, memory overcommit handling determines how the system manages memory allocation. Overcommit mode 0, the default, restricts excessive memory requests to ensure system stability. When the overcommit limit is reached, allocating memory may fail.

The Case of Large Array Allocation

When allocating a large array in Numpy, as in the provided example with a shape of (156816, 36, 53806), the required memory size significantly exceeds the physical memory available. In the given case, it amounts to approximately 282 GB.

Overcommit Memory Mode

The solution to this error lies in enabling the "always overcommit" mode by setting /proc/sys/vm/overcommit_memory to 1. This overrides the default heuristics and permits memory allocations regardless of available physical memory.

Sparse Arrays and Memory Allocation

It's important to note that enabling overcommit mode does not allocate physical memory for the entire array at once. Instead, memory is allocated as pages are written to it. This means that if the array is sparse, with a majority of zero elements, the actual memory usage will be significantly lower than the theoretical size.

Caution and Considerations

While enabling overcommit mode resolves the allocation issue, it should be used with caution. Excessive memory allocations can lead to system performance degradation if the available resources are exceeded. Additionally, it's essential to match the overcommit setting with the actual resource availability to avoid potential system instability.

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