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[tf.data] Add usage notes to the Dataset.from_generator() documentation.

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PiperOrigin-RevId: 178289691
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Derek Murray 2017-12-07 13:52:24 -08:00 committed by TensorFlower Gardener
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tensorflow/python/data/ops/dataset_ops.py
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@ -286,6 +286,23 @@ class Dataset(object):
sess.run(value) # (2, array([1, 1])) sess.run(value) # (2, array([1, 1]))
``` ```
NOTE: The current implementation of `Dataset.from_generator()` uses
@{tf.py_func} and inherits the same constraints. In particular, it
requires the `Dataset`- and `Iterator`-related operations to be placed
on a device in the same process as the Python program that called
`Dataset.from_generator()`. The body of `generator` will not be
serialized in a `GraphDef`, and you should not use this method if you
need to serialize your model and restore it in a different environment.
NOTE: If `generator` depends on mutable global variables or other external
state, be aware that the runtime may invoke `generator` multiple times
(in order to support repeating the `Dataset`) and at any time
between the call to `Dataset.from_generator()` and the production of the
first element from the generator. Mutating global variables or external
state can cause undefined behavior, and we recommend that you explicitly
cache any external state in `generator` before calling
`Dataset.from_generator()`.
Args: Args:
generator: A callable object that takes no arguments and returns an generator: A callable object that takes no arguments and returns an
object that supports the `iter()` protocol. object that supports the `iter()` protocol.