How Can I Explicitly Release Memory in Python to Process Large Datasets Efficiently?

Explicit Memory Release in Python

When working with large datasets, it's crucial to manage memory usage effectively to avoid memory errors. Python provides mechanisms to explicitly free memory, offering greater control over memory management.

In the context of processing a large input file and creating numerous triangles, we face the challenge of storing triangles in memory before outputting them in the OFF format. This can lead to memory issues.

To explicitly free memory, we can leverage the garbage collector using gc.collect(). This method triggers the release of unreferenced memory. However, before invoking the garbage collector, it's necessary to mark the data we no longer need for deletion using del. Here's an example:

import gc

del my_array
del my_object
gc.collect()

By explicitly marking the arrays and objects for deletion, we inform the garbage collector that they can be released, freeing up memory resources. This approach provides greater control over memory management in Python, allowing us to handle large datasets efficiently.

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