[SOLVED] Fastest save and load options for a numpy array

Issue

I have a script that generates two-dimensional numpy arrays with dtype=float and shape on the order of (1e3, 1e6). Right now I’m using np.save and np.load to perform IO operations with the arrays. However, these functions take several seconds for each array. Are there faster methods for saving and loading the entire arrays (i.e., without making assumptions about their contents and reducing them)? I’m open to converting the arrays to another type before saving as long as the data are retained exactly.

Solution

For really big arrays, I’ve heard about several solutions, and they mostly on being lazy on the I/O :

  • NumPy.memmap, maps big arrays to binary form
    • Pros :
      • No dependency other than Numpy
      • Transparent replacement of ndarray (Any class accepting ndarray accepts memmap )
    • Cons :
      • Chunks of your array are limited to 2.5G
      • Still limited by Numpy throughput
  • Use Python bindings for HDF5, a bigdata-ready file format, like PyTables or h5py

    • Pros :
      • Format supports compression, indexing, and other super nice features
      • Apparently the ultimate PetaByte-large file format
    • Cons :
      • Learning curve of having a hierarchical format ?
      • Have to define what your performance needs are (see later)
  • Python’s pickling system (out of the race, mentioned for Pythonicity rather than speed)

    • Pros:
      • It’s Pythonic ! (haha)
      • Supports all sorts of objects
    • Cons:
      • Probably slower than others (because aimed at any objects not arrays)

Numpy.memmap

From the docs of NumPy.memmap :

Create a memory-map to an array stored in a binary file on disk.

Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory

The memmap object can be used anywhere an ndarray is accepted. Given any memmap fp , isinstance(fp, numpy.ndarray) returns True.


HDF5 arrays

From the h5py doc

Lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want.

The format supports compression of data in various ways (more bits loaded for same I/O read), but this means that the data becomes less easy to query individually, but in your case (purely loading / dumping arrays) it might be efficient

Answered By – Jiby

Answer Checked By – Marie Seifert (BugsFixing Admin)

Leave a Reply

Your email address will not be published. Required fields are marked *