webdataset 0.1.14

Record sequential storage for deep learning.

WebDataset

WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives.

Storing data in POSIX tar archives greatly speeds up I/O operations on rotational storage and on networked file systems because it permits all I/O operations to operate as large sequential reads and writes.

WebDataset fulfills a similar function to Tensorflow's TFRecord/tf.Example classes, but it is much easier to adopt because it does not actually require any kind of data conversion: data is stored in exactly the same format inside tar files as it is on disk, and all preprocessing and data augmentation code remains unchanged.

Installation

    $ pip install webdataset

For the Github version:

    $ pip install git+https://github.com/tmbdev/webdataset.git

Documentation

ReadTheDocs

Using WebDataset

Here is an example of an Imagenet input pipeline used for training common visual object recognition models. Note that this code is identical to the standard FileDataset I/O except for the single call that constructs the WebDataset.

    import torch
    from torchvision import transforms
    import webdataset as wds

    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])

    preproc = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize,
    ]) 

    path = "http://server/imagenet_train-{0000..0147}.tgz"

    dataset = wds.WebDataset(path,
                             decoder="pil",
                             extensions="jpg;png cls",
                             transforms=[preproc, lambda x: x-1])

    loader = torch.utils.data.DataLoader(dataset, batch_size=16, num_workers=4)
    for xs, ys in loader:
        train_batch(xs, ys)

Creating WebDataset

In order to permit record sequential access to data, WebDataset only requires that the files comprising a single training samples are stored adjacent to each other inside the tar archive. Such archives can be easily created using GNU tar:

    tar --sorted -cf dataset.tar dir

On BSD and OSX, you can use:

    find dir -type f -print | sort | tar -T - -cf dataset.tar

Very large datasets are best stored as shards, each comprising a number of samples. Shards can be shuffled, read, and processed in parallel. The companion tarproc library permits easy sharding, as well as parallel processing of web datsets and shards. The tarproc programs simply operate as filters on tar streams, so for sharding, you can use a command like this:

    tar --sorted -cf - dir | tarsplit -s 1e9 -o out

TODO

• support image.* and image=jpg,png,jpeg syntax for extensions