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Saves the content of the given dataset.
tf.data.experimental.save(
dataset, path, compression=None, shard_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)
for elem in new_dataset:
print(elem)
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
The saved dataset is saved in multiple file "shards". By default, the dataset
output is divided to shards in a round-robin fashion but custom sharding can
be specified via the shard_func
function. For example, you can save the
dataset to using a single shard as follows:
dataset = make_dataset()
def custom_shard_func(element):
return 0
dataset = tf.data.experimental.save(
path="/path/to/data", ..., shard_func=custom_shard_func)
Args | |
---|---|
dataset
|
The dataset to save. |
path
|
Required. A directory to use for saving the dataset. |
compression
|
Optional. The algorithm to use to compress data when writing
it. Supported options are GZIP and NONE . Defaults to NONE .
|
shard_func
|
Optional. A function to control the mapping of dataset elements to file shards. The function is expected to map elements of the input dataset to int64 shard IDs. If present, the function will be traced and executed as graph computation. |