tf.saved_model.load

TensorFlow 1 version

View source on GitHub

Load a SavedModel from export_dir.

tf.saved_model.load(
    export_dir, tags=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 have functions corresponding to their signatures in the .signatures attribute, but also have a .prune 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. These SavedModels also have a .variables attribute containing imported variables, and a .graph attribute representing the whole imported graph. For SavedModels exported from tf.saved_model.save, variables are instead assigned to whichever attributes they were assigned before export.

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.