Create a TF SavedModel artifact for inference (e.g. via TF-Serving).

(e.g. via TF-Serving).

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.

Usage

# S3 method for keras.src.models.model.Model
export_savedmodel(object, export_dir_base, ...)

Arguments

object

A keras model.

export_dir_base

string, file path where to save the artifact.

...

For forward/backward compatability.

Value

This is called primarily for the side effect of exporting object. The first argument, object is also returned, invisibly, to enable usage with the pipe.

Examples

# Create the artifact
model |> tensorflow::export_savedmodel("path/to/location")

# Later, in a different process / environment...
library(tensorflow)
reloaded_artifact <- tf$saved_model$load("path/to/location")
predictions <- reloaded_artifact$serve(input_data)

# see tfdeploy::serve_savedmodel() for serving a model over a local web api.

See also

Other saving and loading functions:
layer_tfsm()
load_model()
load_model_weights()
register_keras_serializable()
save_model()
save_model_config()
save_model_weights()
with_custom_object_scope()