tf.saved_model.load

TensorFlow 1 version

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Load a SavedModel from export_dir.

tf.saved_model.load(
    export_dir, tags=None, options=None
)

Signatures associated with the SavedModel are available as functions:

imported = tf.saved_model.load(path)
f = imported.signatures["serving_default"]
print(f(x=tf.constant([[1.]])))

Objects exported with tf.saved_model.save additionally have trackable objects and functions assigned to attributes:

exported = tf.train.Checkpoint(v=tf.Variable(3.))
exported.f = tf.function(
    lambda x: exported.v * x,
    input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
tf.saved_model.save(exported, path)
imported = tf.saved_model.load(path)
assert 3. == imported.v.numpy()
assert 6. == imported.f(x=tf.constant(2.)).numpy()

Loading Keras models

Keras models are trackable, so they can be saved to SavedModel. The object returned by tf.saved_model.load is not a Keras object (i.e. doesn't have .fit, .predict, etc. methods). A few attributes and functions are still available: .variables, .trainable_variables and .__call__.

model = tf.keras.Model(...)
tf.saved_model.save(model, path)
imported = tf.saved_model.load(path)
outputs = imported(inputs)

Use tf.keras.models.load_model to restore the Keras model.

Importing SavedModels from TensorFlow 1.x

SavedModels from tf.estimator.Estimator or 1.x SavedModel APIs have a flat graph instead of tf.function objects. These SavedModels will be loaded with the following attributes:

• .signatures: A dictionary mapping signature names to functions.
• .prune(feeds, fetches): A method which allows you to extract functions for new subgraphs. This is equivalent to importing the SavedModel and naming feeds and fetches in a Session from TensorFlow 1.x.

imported = tf.saved_model.load(path_to_v1_saved_model)
pruned = imported.prune("x:0", "out:0")
pruned(tf.ones([]))

See tf.compat.v1.wrap_function for details.

• .variables: A list of imported variables.
• .graph: The whole imported graph.
• .restore(save_path): A function that restores variables from a checkpoint saved from tf.compat.v1.Saver.

Consuming SavedModels asynchronously

When consuming SavedModels asynchronously (the producer is a separate process), the SavedModel directory will appear before all files have been written, and tf.saved_model.load will fail if pointed at an incomplete SavedModel. Rather than checking for the directory, check for "saved_model_dir/saved_model.pb". This file is written atomically as the last tf.saved_model.save file operation.