|TensorFlow 1 version||View source on GitHub|
Return true if the forward compatibility window has expired.
Compat aliases for migration
See Migration guide for more details.
tf.compat.forward_compatible( year, month, day )
Forward-compatibility refers to scenarios where the producer of a TensorFlow model (a GraphDef or SavedModel) is compiled against a version of the TensorFlow library newer than what the consumer was compiled against. The "producer" is typically a Python program that constructs and trains a model while the "consumer" is typically another program that loads and serves the model.
TensorFlow has been supporting a 3 week forward-compatibility window for programs compiled from source at HEAD.
For example, consider the case where a new operation
created with the intent of replacing the implementation of an existing Python
tf.add. The Python wrapper implementation should change from
def add(inputs, name=None): return gen_math_ops.add(inputs, name)
from tensorflow.python.compat import compat def add(inputs, name=None): if compat.forward_compatible(year, month, day): # Can use the awesome new implementation. return gen_math_ops.my_new_awesome_add(inputs, name) # To maintain forward compatibility, use the old implementation. return gen_math_ops.add(inputs, name)
day specify the date beyond which binaries
that consume a model are expected to have been updated to include the
new operations. This date is typically at least 3 weeks beyond the date
the code that adds the new operation is committed.
A year (e.g., 2018). Must be an
A month (1 <= month <= 12) in year. Must be an
A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an
|True if the caller can expect that serialized TensorFlow graphs produced can be consumed by programs that are compiled with the TensorFlow library source code after (year, month, day).|