Export the model as an artifact for inference.

(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.

Note: This feature is currently supported only with TensorFlow, JAX and Torch backends.

Usage

# S3 method for class 'keras.src.models.model.Model'
export_savedmodel(
  object,
  export_dir_base,
  ...,
  format = "tf_saved_model",
  verbose = TRUE,
  input_signature = NULL
)

Arguments

object

A keras model.

export_dir_base

string, file path where to save the artifact.

...

Additional keyword arguments:

Specific to the JAX backend and format="tf_saved_model":
- is_static: Optional bool. Indicates whether fn is static. Set to FALSE if fn involves state updates (e.g., RNG seeds and counters).
- jax2tf_kwargs: Optional dict. Arguments for jax2tf.convert. See the documentation for jax2tf.convert. If native_serialization and polymorphic_shapes are not provided, they will be automatically computed.

format

string. The export format. Supported values: "tf_saved_model" and "onnx". Defaults to "tf_saved_model".

verbose

whether to print all the variables of the exported model.

input_signature

Optional. Specifies the shape and dtype of the model inputs. Can be a structure of keras.InputSpec, tf.TensorSpec, backend.KerasTensor, or backend tensor. If not provided, it will be automatically computed. Defaults to NULL.

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.

Here's how to export an ONNX for inference.

# Export the model as a ONNX artifact
model |> export_savedmodel("path/to/location", format = "onnx")

# Load the artifact in a different process/environment
onnxruntime <- reticulate::import("onnxruntime")
ort_session <- onnxruntime$InferenceSession("path/to/location")
input_data <- list(....)
names(input_data) <- sapply(ort_session$get_inputs(), `[[`, "name")
predictions <- ort_session$run(NULL, input_data)

Usage

Arguments

Value

Examples

See also