tf.data.experimental.load

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Loads a previously saved dataset.

tf.data.experimental.load(
    path, element_spec, compression=None, reader_func=None
)

Example usage:

import tempfile
path = os.path.join(tempfile.gettempdir(), "saved_data")
# Save a dataset
dataset = tf.data.Dataset.range(2)
tf.data.experimental.save(dataset, path)
new_dataset = tf.data.experimental.load(path,
    tf.TensorSpec(shape=(), dtype=tf.int64))
for elem in new_dataset:
  print(elem)
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)

Note that to load a previously saved dataset, you need to specify element_spec -- a type signature of the elements of the saved dataset, which can be obtained via tf.data.Dataset.element_spec. This requirement exists so that shape inference of the loaded dataset does not need to perform I/O.

If the default option of sharding the saved dataset was used, the element order of the saved dataset will be preserved when loading it.

The reader_func argument can be used to specify a custom order in which elements should be loaded from the individual shards. The reader_func is expected to take a single argument -- a dataset of datasets, each containing elements of one of the shards -- and return a dataset of elements. For example, the order of shards can be shuffled when loading them as follows:

def custom_reader_func(datasets):
  datasets = datasets.shuffle(NUM_SHARDS)
  return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE)

dataset = tf.data.experimental.load(
    path="/path/to/data", ..., reader_func=custom_reader_func)