Model export for inference

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export method

Model.export(filepath, format="tf_saved_model", verbose=True)

Create a TF SavedModel artifact for inference.

Note: This can currently only be used with the TensorFlow or JAX backends.

This method lets you export a model to a lightweight SavedModel artifact that contains the model's forward pass only (its call() method) and can be served via e.g. TF-Serving. The forward pass is registered under the name serve() (see example below).

The original code of the model (including any custom layers you may have used) is no longer necessary to reload the artifact – it is entirely standalone.

Arguments

• filepath: str or pathlib.Path object. Path where to save the artifact.
• verbose: whether to print all the variables of the exported model.

Example

# Create the artifact
model.export("path/to/location")

# Later, in a different process/environment...
reloaded_artifact = tf.saved_model.load("path/to/location")
predictions = reloaded_artifact.serve(input_data)

If you would like to customize your serving endpoints, you can use the lower-level keras.export.ExportArchive class. The export() method relies on ExportArchive internally.

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ExportArchive class

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")

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.

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add_endpoint method

ExportArchive.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)

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add_variable_collection method

ExportArchive.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

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track method

ExportArchive.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.

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write_out method

ExportArchive.write_out(filepath, options=None, verbose=True)

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.
• verbose: whether to print all the variables of an exported SavedModel.

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.