|TensorFlow 2.0 version||View source on GitHub|
Exports the Trackable object
obj to SavedModel format.
tf.saved_model.save( obj, export_dir, signatures=None )
class Adder(tf.train.Checkpoint): @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)]) def add(self, x): return x + x + 1. to_export = Adder() tf.saved_model.save(to_export, '/tmp/adder')
The resulting SavedModel is then servable with an input named "x", its value having any shape and dtype float32.
signatures argument controls which methods in
obj will be
available to programs which consume
SavedModels, for example serving
APIs. Python functions may be decorated with
@tf.function(input_signature=...) and passed as signatures directly, or
lazily with a call to
get_concrete_function on the method decorated with
signatures argument is omitted,
obj will be searched for
@tf.function-decorated methods. If exactly one
@tf.function is found, that
method will be used as the default signature for the SavedModel. This behavior
is expected to change in the future, when a corresponding
tf.saved_model.load symbol is added. At that point signatures will be
completely optional, and any
@tf.function attached to
obj or its
dependencies will be exported for use with
When invoking a signature in an exported SavedModel,
Tensor arguments are
identified by name. These names will come from the Python function's argument
names by default. They may be overridden by specifying a
in the corresponding
tf.TensorSpec object. Explicit naming is required if
Tensors are passed through a single argument to the Python
The outputs of functions used as
signatures must either be flat lists, in
which case outputs will be numbered, or a dictionary mapping string keys to
Tensor, in which case the keys will be used to name outputs.
Signatures are available in objects returned by
tf.saved_model.load as a
.signatures attribute. This is a reserved attribute:
on an object with a custom
.signatures attribute will raise an exception.
tf.keras.Model objects are also Trackable, this function can be
used to export Keras models. For example, exporting with a signature
class Model(tf.keras.Model): @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)]) def serve(self, serialized): ... m = Model() tf.saved_model.save(m, '/tmp/saved_model/')
Exporting from a function without a fixed signature:
class Model(tf.keras.Model): @tf.function def call(self, x): ... m = Model() tf.saved_model.save( m, '/tmp/saved_model/', signatures=m.call.get_concrete_function( tf.TensorSpec(shape=[None, 3], dtype=tf.float32, name="inp")))
tf.keras.Model instances constructed from inputs and outputs already have a
signature and so do not require a
@tf.function decorator or a
argument. If neither are specified, the model's forward pass is exported.
x = input_layer.Input((4,), name="x") y = core.Dense(5, name="out")(x) model = training.Model(x, y) tf.saved_model.save(model, '/tmp/saved_model/') # The exported SavedModel takes "x" with shape [None, 4] and returns "out" # with shape [None, 5]
Variables must be tracked by assigning them to an attribute of a tracked
object or to an attribute of
obj directly. TensorFlow objects (e.g. layers
tf.keras.layers, optimizers from
tf.train) track their variables
automatically. This is the same tracking scheme that
uses, and an exported
Checkpoint object may be restored as a training
checkpoint by pointing
tf.train.Checkpoint.restore to the SavedModel's
"variables/" subdirectory. Currently variables are the only stateful objects
tf.saved_model.save, but others (e.g. tables) will be supported
in the future.
tf.function does not hard-code device annotations from outside the function
body, instead using the calling context's device. This means for example that
exporting a model which runs on a GPU and serving it on a CPU will generally
work, with some exceptions.
tf.device annotations inside the body of the
function will be hard-coded in the exported model; this type of annotation is
discouraged. Device-specific operations, e.g. with "cuDNN" in the name or with
device-specific layouts, may cause issues. Currently a
is another exception: active distribution strategies will cause device
placements to be hard-coded in a function. Exporting a single-device
computation and importing under a
DistributionStrategy is not currently
supported, but may be in the future.
SavedModels exported with
tf.saved_model.save strip default-valued
automatically, which removes one source of incompatibilities when the consumer
of a SavedModel is running an older TensorFlow version than the
producer. There are however other sources of incompatibilities which are not
handled automatically, such as when the exported model contains operations
which the consumer does not have definitions for.
The current implementation of
tf.saved_model.save targets serving use-cases,
but omits information which will be necessary for the planned future
tf.saved_model.load. Exported models using the current
save implementation, and other existing SavedModels, will not be compatible
tf.saved_model.load when it is implemented. Further,
save will in the
future attempt to export
@tf.function-decorated methods which it does not
currently inspect, so some objects which are exportable today will raise
exceptions on export in the future (e.g. due to complex/non-serializable
default arguments). Such backwards-incompatible API changes are expected only
prior to the TensorFlow 2.0 release.
obj: A trackable object to export.
export_dir: A directory in which to write the SavedModel.
signatures: Optional, either a
tf.functionwith an input signature specified or the result of
f, in which case
fwill be used to generate a signature for the SavedModel under the default serving signature key.
signaturesmay also be a dictionary, in which case it maps from signature keys to either
tf.functioninstances with input signatures or concrete functions. The keys of such a dictionary may be arbitrary strings, but will typically be from the
objis not trackable.
Not well supported when graph building. From TensorFlow 1.x,
tf.compat.v1.enable_eager_execution() should run first. Calling
tf.saved_model.save in a loop when graph building from TensorFlow 1.x will
add new save operations to the default graph each iteration.
May not be called from within a function body.