tf.keras.export.ExportArchive

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

View aliases

Compat aliases for migration

`tf.compat.v1.keras.export.ExportArchive`

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.

Methods

`add_endpoint`

View source

add_endpoint(
    name, fn, input_signature=None
)

Arguments

name Str, name of the 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).

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).

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
)

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(
    layer
)

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

`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.