Processing Datasets Larger Than RAM with NumPy Memory-Mapped Arrays
Use numpy.memmap to read and write arrays stored on disk with normal NumPy indexing, keeping RAM usage proportional to the data you actually touch.
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Use numpy.memmap to read and write arrays stored on disk with normal NumPy indexing, keeping RAM usage proportional to the data you actually touch.
Symptom NumPy’s np.memmap maps a file into memory, but the library offers no built‑in detection of file size changes after the mapping is created. Truncating or extending the backing file can lead to silent data corruption, unexpected ValueError s, or stale reads, while concurrent writes from multiple processes may race without any protection. Goal To clarif
Goal Determine whether modifications made to the backing file of a numpy.memmap opened with mode='r+' are reflected in the in‑memory array across different operating systems. Uncertainty The NumPy documentation notes that memmap supports lazy loading and that mode='r+' allows read/write access, but it does not explicitly state how external changes to the fil
When persisting large arrays to disk using np.memmap or np.lib.format.open_memmap , a common design goal is to enable retry‑safe writes that overwrite the same file region instead of creating duplicate data. The current documented workflow is to open the file in 'r+' or 'w+' mode, modify the array in place, and invoke flush() to push pages to the OS page cac
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