Fast And Efficient Way Of Serializing And Retrieving A Large Number Of Numpy Arrays From Hdf5 File

I have a huge list of numpy arrays, specifically 113287, where each array is of shape 36 x 2048. In terms of memory, this amounts to 32 Gigabytes. As of now, I have serialized thes

Solution 1:

According to Out[7], "img_feats" is a large 3d array. (113287, 36, 2048) shape.

Define ds as the dataset (doesn't load anything):

ds = hf[group_key]

x = ds[0]    # should be a (36, 2048) arrayarr = ds[:]   # should load the whole dataset into memory.arr = ds[:n]   # load a subset, slice 

According to h5py-reading-writing-data :

HDF5 datasets re-use the NumPy slicing syntax to read and write to the file. Slice specifications are translated directly to HDF5 “hyperslab” selections, and are a fast and efficient way to access data in the file.

I don't see any point in wrapping that in list(); that is, in splitting the 3d array in a list of 113287 2d arrays. There's a clean mapping between 3d datasets on the HDF5 file and numpy arrays.

h5py-fancy-indexing warns that fancy indexing of a dataset is slower. That is, seeking to load, say [1, 1000, 3000, 6000] subarrays of that large dataset.

You might want to experiment with writing and reading some smaller datasets if working with this large one is too confusing.

Solution 2:

One way would be to put each sample into its own group and index directly into those. I am thinking the conversion takes long because it tries to load the entire data set into a list (which it has to read from disk). Re-organizing the h5 file such that

• group
  • sample
    • 36 x 2048 may help in indexing speed.