/TensorFlow 2.4


Return true if the forward compatibility window has expired.

See Version compatibility.

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 MyNewAwesomeAdd is created with the intent of replacing the implementation of an existing Python wrapper - tf.add. The Python wrapper implementation should change from something like:

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)

Where year, month, and 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.

year A year (e.g., 2018). Must be an int.
month A month (1 <= month <= 12) in year. Must be an int.
day A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an int.
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).

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Code samples licensed under the Apache 2.0 License.