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Showing posts with the label Hdf5

H5py: Compound Datatypes And Scale-offset In The Compression Pipeline

Using Numpy and h5py, it is possible to create ‘compound datatype’ datasets to be stored in an hdf5… Read more H5py: Compound Datatypes And Scale-offset In The Compression Pipeline

Installing H5py On Os X

I've spent the day trying to get the h5py module of python working, but without success. I'… Read more Installing H5py On Os X

Saving With H5py Arrays Of Different Sizes

I am trying to store about 3000 numpy arrays using HDF5 data format. Arrays vary in length from 530… Read more Saving With H5py Arrays Of Different Sizes

Most Efficient Way To Use A Large Data Set For Pytorch?

Perhaps this question has been asked before, but I'm having trouble finding relevant info for m… Read more Most Efficient Way To Use A Large Data Set For Pytorch?

How To Partially Copy Using Python An Hdf5 File Into A New One Keeping The Same Structure?

I have a large hdf5 file that looks something like this: A/B/dataset1, dataset2 A/C/dataset1, datas… Read more How To Partially Copy Using Python An Hdf5 File Into A New One Keeping The Same Structure?

Pytables Duplicates 2.5 Giga Rows

I currently have a .h5 file, with a table in it consisting of three columns: a text columns of 64 c… Read more Pytables Duplicates 2.5 Giga Rows

HDF5 Core Driver (H5FD_CORE): Loading Selected Dataset(s)

Currently, I load HDF5 data in python via h5py and read a dataset into memory. f = h5py.File('m… Read more HDF5 Core Driver (H5FD_CORE): Loading Selected Dataset(s)

Most Efficient Way To Use A Large Data Set For PyTorch?

Perhaps this question has been asked before, but I'm having trouble finding relevant info for m… Read more Most Efficient Way To Use A Large Data Set For PyTorch?