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Introducing License Enforcement in Socket
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Tarfiles are commonly used for storing large amounts of data in an efficient, sequential access, compressed file format, in particualr for deep learning applications. For processing and data transformation, people usually unpack them, operate over the files, and tar up the result again.
This library and set of utilities permits operating directly on tar files. This is faster than operating on files on file systems, and it is usually easier too.
The following are less commonly used utilities that are specifically useful for deep learning:
The utilities allow operating on stdin/stdout when necessary, allowing command line pipes to be constructed. For example:
$ gsutil cat gs://bucket/file.tar | tarsort | tarsplit -o output
from tarproclib import reader, gopen
from itertools import islice
gopen.handlers["gs"] = "gsutil cat '{}'"
for sample in islice(reader.TarIterator("gs://lpr-imagenet/imagenet_train-0000.tgz"), 0, 10):
print(sample.keys())
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
FAQs
Big data data processing for tar archives.
We found that tarproc demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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