Large dataset storage format for Pytorch

Related tags

Deep Learningpytorchdatasethdf5hdf5-library
Overview

H5Record

Large dataset ( > 100G, <= 1T) storage format for Pytorch (wip)

Support python 3

pip install h5record

Why?

  • Writing large dataset is still a wild west in pytorch. Approaches seen in the wild include:

    • large directory with lots of small files : slow IO when complex file is fetched, deserialized frequently
    • database approach : depend on what kind of database engine used, usually multi-process read is not supported
    • the above method scale non linear in terms of data - storage size

  • TFRecord solved the above problems well ( multiprocess fetch, (de)compression ), fast serialization ( protobuf )

  • However TFRecord port does not support data size evaluation (used frequently by Dataloader ), no index level access available ( important for data evaluation or verification )

H5Record aim to tackle TFRecord problems by compressing the dataset into HDF5 file with an easy to use interface through predefined interfaces ( String, Image, Sequences, Integer).

Some advantage of using H5Record

  • Support multi-process read

  • Relatively simple to use and low technical debt

  • Support compression/de-compression on the fly

  • Quick load to memory if required

Simple usage

pip install h5record
  1. Sentence Similarity
from h5record import H5Dataset, Float, String

schema = (
    String(name='sentence1'),
    String(name='sentence2'),
    Float(name='label')
)
data = [
    ['Sent 1.', 'Sent 2', 0.1],
    ['Sent 3', 'Sent 4', 0.2],
]

def pair_iter():
    for row in data:
        yield {
            'sentence1': row[0],
            'sentence2': row[1],
            'label': row[2]
        }

dataset = H5Dataset(schema, './question_pair.h5', pair_iter())
for idx in range(len(dataset)):
    print(dataset[idx])

Note

Due to in progress development, this package should be use in care in storage with FAT, FAT-32 format

Comparison between different compression algorithm

No chunking is used

Compression Type File size Read speed row/second
no compression 2.0G 2084.55 it/s
lzf 1.7G 1496.14 it/s
gzip 1.1G 843.78 it/s

benchmarked in i7-9700, 1TB NVMe SSD

If you are interested to learn more feel free to checkout the note as well!

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Comments
  • Example about Image dataset

    Example about Image dataset

    Thanks for your work. Do you have an end to end example about image dataset which includes creating h5records file similar to tfrecord files and then using it in dataloader mechanism just like tf dataset api loader mechanism?

    documentation question 
    opened by meet-minimalist 1
Releases(1.0.4)
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GitHub Repository https://pypi.org/project/h5record
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