tf.keras.export.ExportArchive

ExportArchive is used to write SavedModel artifacts (e.g. for inference).

tf.keras.export.ExportArchive()

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")

Note on resource tracking:

ExportArchive is able to automatically track all tf.Variables used by its endpoints, so most of the time calling .track(model) is not strictly required. However, if your model uses lookup layers such as IntegerLookup, StringLookup, or TextVectorization, it will need to be tracked explicitly via .track(model).

Explicit tracking is also required if you need to be able to access the properties variables, trainable_variables, or non_trainable_variables on the revived archive.

non_trainable_variables

trainable_variables

variables

Methods

add_endpoint

View source

add_endpoint(
    name, fn, input_signature=None, jax2tf_kwargs=None
)

Register a new serving endpoint.

Arguments
name Str, name of the endpoint.
fn A function. It should only leverage resources (e.g. tf.Variable objects or tf.lookup.StaticHashTable objects) that are available on the models/layers tracked by the ExportArchive (you can call .track(model) to track a new model). The shape and dtype of the inputs to the function must be known. For that purpose, you can either 1) make sure that fn is a tf.function that has been called at least once, or

2) provide an input_signature argument that specifies the shape and dtype of the inputs (see below).

input_signature Used to specify the shape and dtype of the inputs to fn. List of tf.TensorSpec objects (one per positional input argument of fn). Nested arguments are allowed (see below for an example showing a Functional model with 2 input arguments).
jax2tf_kwargs Optional. A dict for arguments to pass to jax2tf. Supported only when the backend is JAX. See documentation for jax2tf.convert. The values for native_serialization and polymorphic_shapes, if not provided, are automatically computed.

Returns
The tf.function wrapping fn that was added to the archive.

Example:

Adding an endpoint using the input_signature argument when the model has a single input argument:

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)],
)

Adding an endpoint using the input_signature argument when the model has two positional input arguments:

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),
        tf.TensorSpec(shape=(None, 4), dtype=tf.float32),
    ],
)

Adding an endpoint using the input_signature argument when the model has one input argument that is a list of 2 tensors (e.g. a Functional model with 2 inputs):

model = keras.Model(inputs=[x1, x2], outputs=outputs)

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),
            tf.TensorSpec(shape=(None, 4), dtype=tf.float32),
        ],
    ],
)

This also works with dictionary inputs:

model = keras.Model(inputs={"x1": x1, "x2": x2}, outputs=outputs)

export_archive = ExportArchive()
export_archive.track(model)
export_archive.add_endpoint(
    name="serve",
    fn=model.call,
    input_signature=[
        {
            "x1": tf.TensorSpec(shape=(None, 3), dtype=tf.float32),
            "x2": tf.TensorSpec(shape=(None, 4), dtype=tf.float32),
        },
    ],
)

Adding an endpoint that is a tf.function:

@tf.function()
def serving_fn(x):
    return model(x)

# The function must be traced, i.e. it must be called at least once.
serving_fn(tf.random.normal(shape=(2, 3)))

export_archive = ExportArchive()
export_archive.track(model)
export_archive.add_endpoint(name="serve", fn=serving_fn)

add_variable_collection

View source

add_variable_collection(
    name, variables
)

Register a set of variables to be retrieved after reloading.

Arguments
name The string name for the collection.
variables A tuple/list/set of tf.Variable instances.

Example:

export_archive = ExportArchive()
export_archive.track(model)
# Register an endpoint
export_archive.add_endpoint(
    name="serve",
    fn=model.call,
    input_signature=[tf.TensorSpec(shape=(None, 3), dtype=tf.float32)],
)
# Save a variable collection
export_archive.add_variable_collection(
    name="optimizer_variables", variables=model.optimizer.variables)
export_archive.write_out("path/to/location")

# Reload the object
revived_object = tf.saved_model.load("path/to/location")
# Retrieve the variables
optimizer_variables = revived_object.optimizer_variables

track

View source

track(
    resource
)

Track the variables (and other assets) of a layer or model.

By default, all variables used by an endpoint function are automatically tracked when you call add_endpoint(). However, non-variables assets such as lookup tables need to be tracked manually. Note that lookup tables used by built-in Keras layers (TextVectorization, IntegerLookup, StringLookup) are automatically tracked in add_endpoint().

Arguments
resource A trackable TensorFlow resource.

write_out

View source

write_out(
    filepath, options=None
)

Write the corresponding SavedModel to disk.

Arguments
filepath str or pathlib.Path object. Path where to save the artifact.
options tf.saved_model.SaveOptions object that specifies SavedModel saving options.

Note on TF-Serving: all endpoints registered via add_endpoint() are made visible for TF-Serving in the SavedModel artifact. In addition, the first endpoint registered is made visible under the alias "serving_default" (unless an endpoint with the name "serving_default" was already registered manually), since TF-Serving requires this endpoint to be set.