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TensorFlow 1 version
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Return true if the forward compatibility window has expired.
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 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)
to:
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.
Args | |
|---|---|
year
|
A year (e.g., 2018). Must be an int.
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month
|
A month (1 <= month <= 12) in year. Must be an int.
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day
|
A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an
int.
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Returns | |
|---|---|
| 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). |