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Class DatasetBuilder

Abstract base class for all datasets.

DatasetBuilder has 3 key methods:

  • documents the dataset, including feature names, types, and shapes, version, splits, citation, etc.
  • tfds.DatasetBuilder.download_and_prepare: downloads the source data and writes it to disk.
  • tfds.DatasetBuilder.as_dataset: builds an input pipeline using

Configuration: Some DatasetBuilders expose multiple variants of the dataset by defining a tfds.core.BuilderConfig subclass and accepting a config object (or name) on construction. Configurable datasets expose a pre-defined set of configurations in tfds.DatasetBuilder.builder_configs.

Typical DatasetBuilder usage:

mnist_builder = tfds.builder("mnist:3.*.*")
mnist_info =
datasets = mnist_builder.as_dataset()

train_dataset, test_dataset = datasets["train"], datasets["test"]
assert isinstance(train_dataset,

# And then the rest of your input pipeline
train_dataset = train_dataset.repeat().shuffle(1024).batch(128)
train_dataset = train_dataset.prefetch(2)
features =
image, label = features['image'], features['label']


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Constructs a DatasetBuilder.

Callers must pass arguments as keyword arguments.


  • data_dir: str, directory to read/write data. Defaults to datasets are stored.
  • config: tfds.core.BuilderConfig or str name, optional configuration for the dataset that affects the data generated on disk. Different builder_configs will have their own subdirectories and versions.
  • version: str. Optional version at which to load the dataset. An error is raised if specified version cannot be satisfied. Eg: '1.2.3', '1.2.*'. The special value "experimental_latest" will use the highest version, even if not default. This is not recommended unless you know what you are doing, as the version could be broken.



tfds.core.BuilderConfig for this builder.



tfds.core.DatasetInfo for this builder.




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Constructs a

Callers must pass arguments as keyword arguments.

The output types vary depending on the parameters. Examples:

builder = tfds.builder('imdb_reviews:1.*.*')

# Default parameters: Returns the dict of
ds_all_dict = builder.as_dataset()
assert isinstance(ds_all_dict, dict)
print(ds_all_dict.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_dict['test'],
# Each dataset (test, train, unsup.) consists of dictionaries
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b"I've watched the movie ..">}
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b'If you love Japanese ..'>}

# With as_supervised: only contains (feature, label) tuples
ds_all_supervised = builder.as_dataset(as_supervised=True)
assert isinstance(ds_all_supervised, dict)
print(ds_all_supervised.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_supervised['test'],
# Each dataset (test, train, unsup.) consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)

# Same as above plus requesting a particular split
ds_test_supervised = builder.as_dataset(as_supervised=True, split='test')
assert isinstance(ds_test_supervised,
# The dataset consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)


  • split: tfds.core.SplitBase, which subset(s) of the data to read. If None (default), returns all splits in a dict <key: tfds.Split, value:>.
  • batch_size: int, batch size. Note that variable-length features will be 0-padded if batch_size is set. Users that want more custom behavior should use batch_size=None and use the API to construct a custom pipeline. If batch_size == -1, will return feature dictionaries of the whole dataset with tf.Tensors instead of a
  • shuffle_files: bool, whether to shuffle the input files. Defaults to True if split == tfds.Split.TRAIN and False otherwise.
  • decoders: Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.
  • as_supervised: bool, if True, the returned will have a 2-tuple structure (input, label) according to If False, the default, the returned will have a dictionary with all the features.
  • in_memory: bool, if True, loads the dataset in memory which increases iteration speeds. Note that if True and the dataset has unknown dimensions, the features will be padded to the maximum size across the dataset.

Returns:, or if split=None, dict<key: tfds.Split, value:>.

If batch_size is -1, will return feature dictionaries containing the entire dataset in tf.Tensors instead of a


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Downloads and prepares dataset for reading.


  • download_dir: str, directory where downloaded files are stored. Defaults to "~/tensorflow-datasets/downloads".
  • download_config:, further configuration for downloading and preparing dataset.


  • IOError: if there is not enough disk space available.

Class Members

  • VERSION = None
  • builder_configs
  • name = 'dataset_builder'