Understanding Memory Mapping in Numpy for Deep Learning: Pt 2
Blog post from Comet
The article explores the concept of memory mapping in Python using NumPy, focusing on the differences and applications of the `memmap()` function compared to the `load()` method with `mmap_mode`. While `load()` with `mmap_mode` allows for efficient memory usage by mapping data to memory without fully loading it, `memmap()` offers a more comprehensive solution by requiring specification of data shape, mode, and type for effective manipulation. Despite its complexity, `memmap()` can handle larger datasets by using Raw Binary Format (RBF) for storage, ensuring that the data can be accurately reconstructed with minimal memory footprint. The article emphasizes the importance of understanding binary formats and the correct use of arguments to achieve desired results, as demonstrated by transforming a NumPy binary file into a smaller RBF file, which maintains the same low memory occupation when mapped. Through practical examples and comparisons, it concludes by reinforcing the utility of these methods for tasks beyond deep learning, such as efficient data storage and retrieval.
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