import tempfilepath = os.path.join(tempfile.gettempdir(), "saved_data")# Save a datasetdataset = 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)
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:
Required. A path pointing to a previously saved dataset.
element_spec
Optional. A nested structure of tf.TypeSpec objects matching
the structure of an element of the saved dataset and specifying the type
of individual element components. If not provided, the nested structure of
tf.TypeSpec saved with the saved dataset is used. Note that this
argument is required in graph mode.
compression
Optional. The algorithm to use to decompress the data when
reading it. Supported options are GZIP and NONE. Defaults to NONE.
reader_func
Optional. A function to control how to read data from shards.
If present, the function will be traced and executed as graph computation.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]