tarzan 0.1.0

high-level IO for tar based dataset

Tarzan

Tar, as a high performance streamable format, has been widely used in the DL community (e.g. TorchData, WebDataset). TFDS-like dataset builder API provides a high-level interface for users to build their own datasets, and is also adopted by HuggingFace.

Why not connect the two? Tarzan provides a minimal high-level API to help users build their own Tar-based datasets. It also maps well between nested feature and Tar file structure to let you peek into the Tar file without extracting it.

Installation

pip install tarzan

Quick Start

1. Define your dataset info, which describes the dataset structure and any metadata.

from tarzan.info import DatasetInfo
from tarzan.features import Features, Text, Scalar, Tensor, Audio

info = DatasetInfo(
   description="A fake dataset",
   features=Features({
       'single': Text(),
       'nested_list': [Scalar('int32')],
       'nested_dict': {
           'inner': Tensor(shape=(None, 3), dtype='float32'),
       },
       'complex': [{
           'inner_1': Text(),
           'inner_2': Audio(sample_rate=16000),
       }]
   }),
   metadata={
       'version': '1.0.0'
   }
)

2. Write your data to Tar files with ShardWriter.

from tarzan.writers import ShardWriter 
with ShardWriter('data_dir', info, max_count=2) as writer:
   for i in range(5):
      writer.write({
          'single': 'hello',
          'nested_list': [1, 2, 3],
          'nested_dict': {
              'inner': [[1, 2, 3], [4, 5, 6]]
          },
          'complex': [{
              'inner_1': 'world',
              'inner_2': 'audio.wav'
          }]
      })

The structure of the data_dir is as follows:

data_dir
├── 00000.tar
├── 00001.tar
├── 00002.tar
└── dataset_info.json

max_count and max_size control the maximum number of samples and the maximum size of each shard. Here we set the max_count to 2 to create 3 shards. dataset_info.json is a json file serialized from `info, which we rely on to read the data later.

cat data_dir/dataset_info.json

{
  "description": "A fake dataset",
  "file_list": [
    "00000.tar",
    "00000.tar",
    "00001.tar",
    "00002.tar"
  ],
  "features": {
    "single": {
      "_type": "Text"
    },
    "nested_list": [
      {
        "shape": [],
        "dtype": "int32",
        "_type": "Scalar"
      }
    ],
    "nested_dict": {
      "inner": {
        "shape": [
          null,
          3
        ],
        "dtype": "float32",
        "_type": "Tensor"
      }
    },
    "complex": [
      {
        "inner_1": {
          "_type": "Text"
        },
        "inner_2": {
          "shape": [
            null
          ],
          "dtype": "float32",
          "_type": "Audio",
          "sample_rate": 16000
        }
      }
    ]
  },
  "metadata": {
    "version": "1.0.0"
  }
}

You can peek the tar file without extracting it and it should map well to the nested feature structure.

tree data_dir/00000.tar

.
├── 0
│   ├── complex
│   │   └── 0
│   │       ├── inner_1
│   │       └── inner_2
│   ├── nested_dict
│   │   └── inner
│   ├── nested_list
│   │   ├── 0
│   │   ├── 1
│   │   └── 2
│   └── single
└── 1
    ├── complex
    │   └── 0
    │       ├── inner_1
    │       └── inner_2
    ├── nested_dict
    │   └── inner
    ├── nested_list
    │   ├── 0
    │   ├── 1
    │   └── 2
    └── single

3.Read the dataset with TarReader

from tarzan.readers import TarReader
reader = TarReader.from_dataset_info('data_dir/dataset_info.json')

for tar_name, idx, example in reader:
    print(tar_name, idx, example)

data_dir/00000.tar 0 {'nested_dict': {'inner': array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float32)}, 'single': 'hello', 'complex': [{'inner_1': 'world', 'inner_2': <tarzan.features.audio.AudioDecoder object at 0x7fb8903443d0>}], 'nested_list': [array(1, dtype=int32), array(2, dtype=int32), array(3, dtype=int32)]}
...

Note that the Audio feature is returned as a lazy read object AudioDecoder to avoid unnecessary read for large audio.