Model export for inference
ExportArchive class
tf.keras.export.ExportArchive()
ExportArchive is used to write SavedModel artifacts (e.g. for inference).
If you have a Keras model or layer that you want to export as SavedModel for
serving (e.g. via TensorFlow-Serving), you can use ExportArchive
to configure the different serving endpoints you need to make available,
as well as their signatures. Simply instantiate an ExportArchive,
use track() to register the layer(s) or model(s) to be used,
then use the add_endpoint() method to register a new serving endpoint.
When done, use the write_out() method to save the artifact.
The resulting artifact is a SavedModel and can be reloaded via
tf.saved_model.load.
Examples
Here's how to export a model for inference.
export_archive = ExportArchive()
export_archive.track(model)
export_archive.add_endpoint(
name="serve",
fn=model.call,
input_signature=[tf.TensorSpec(shape=(None, 3), dtype=tf.float32)],
)
export_archive.write_out("path/to/location")
# Elsewhere, we can reload the artifact and serve it.
# The endpoint we added is available as a method:
serving_model = tf.saved_model.load("path/to/location")
outputs = serving_model.serve(inputs)
Here's how to export a model with one endpoint for inference and one endpoint for a training-mode forward pass (e.g. with dropout on).
export_archive = ExportArchive()
export_archive.track(model)
export_archive.add_endpoint(
name="call_inference",
fn=lambda x: model.call(x, training=False),
input_signature=[tf.TensorSpec(shape=(None, 3), dtype=tf.float32)],
)
export_archive.add_endpoint(
name="call_training",
fn=lambda x: model.call(x, training=True),
input_signature=[tf.TensorSpec(shape=(None, 3), dtype=tf.float32)],
)
export_archive.write_out("path/to/location")