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Creates a dataset which reads data from the service.

This is useful when the dataset is registered by one process, then used in another process. When the same process is both registering and reading from the dataset, it is simpler to use instead.

Before using from_dataset_id, the dataset must have been registered with the service using register_dataset returns a dataset id for the registered dataset. That is the dataset_id which should be passed to from_dataset_id.

The element_spec argument indicates the tf.TypeSpecs for the elements produced by the dataset. Currently element_spec must be explicitly specified, and match the dataset registered under dataset_id. element_spec defaults to None so that in the future we can support automatically discovering the element_spec by querying the service. is a convenience method which combines register_dataset and from_dataset_id into a dataset transformation. See the documentation for for more detail about how from_dataset_id works.

dispatcher =
dispatcher_address ="://")[1]
worker =
dataset =
dataset_id =, dataset)
dataset =
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

processing_mode A string specifying the policy for how data should be processed by workers. Can be either "parallel_epochs" to have each worker process a copy of the dataset, or "distributed_epoch" to split a single iteration of the dataset across all the workers.
service A string or a tuple indicating how to connect to the service. If it's a string, it should be in the format [<protocol>://]<address>, where <address> identifies the dispatcher address and <protocol> can optionally be used to override the default protocol to use. If it's a tuple, it should be (protocol, address).
dataset_id The id of the dataset to read from. This id is returned by register_dataset when the dataset is registered with the service.
element_spec A nested structure of tf.TypeSpecs representing the type of elements produced by the dataset. Use