tf.saved_model.SaveOptions

Options for saving to SavedModel.

tf.saved_model.SaveOptions(
    namespace_whitelist=None, save_debug_info=False, function_aliases=None,
    experimental_io_device=None, experimental_variable_policy=None,
    experimental_custom_gradients=False
)

This function may be used in the options argument in functions that save a SavedModel (tf.saved_model.save, tf.keras.models.save_model).

namespace_whitelist List of strings containing op namespaces to whitelist when saving a model. Saving an object that uses namespaced ops must explicitly add all namespaces to the whitelist. The namespaced ops must be registered into the framework when loading the SavedModel.
save_debug_info Boolean indicating whether debug information is saved. If True, then a debug/saved_model_debug_info.pb file will be written with the contents of a GraphDebugInfo binary protocol buffer containing stack trace information for all ops and functions that are saved.
function_aliases Python dict. Mapping from string to object returned by @tf.function. A single tf.function can generate many ConcreteFunctions. If a downstream tool wants to refer to all concrete functions generated by a single tf.function you can use the function_aliases argument to store a map from the alias name to all concrete function names. E.g.

class Adder(tf.Module):
  @tf.function
  def double(self, x):
    return x + x
 
model = Adder()
model.double.get_concrete_function(
  tf.TensorSpec(shape=[], dtype=tf.float32, name="float_input"))
model.double.get_concrete_function(
  tf.TensorSpec(shape=[], dtype=tf.string, name="string_input"))
 
options = tf.saved_model.SaveOptions(
  function_aliases={'double': model.double})
tf.saved_model.save(model, '/tmp/adder', options=options)

experimental_io_device string. Applies in a distributed setting. Tensorflow device to use to access the filesystem. If None (default) then for each variable the filesystem is accessed from the CPU:0 device of the host where that variable is assigned. If specified, the filesystem is instead accessed from that device for all variables.

This is for example useful if you want to save to a local directory, such as "/tmp" when running in a distributed setting. In that case pass a device for the host where the "/tmp" directory is accessible.

experimental_variable_policy The policy to apply to variables when saving. This is either a saved_model.experimental.VariablePolicy enum instance or one of its value strings (case is not important). See that enum documentation for details. A value of None corresponds to the default policy.
experimental_custom_gradients Boolean. When True, will save traced gradient functions for the functions decorated by tf.custom_gradient. Defaults to False.

experimental_custom_gradients Instance of member_descriptor
experimental_io_device Instance of member_descriptor
experimental_variable_policy Instance of member_descriptor
function_aliases Instance of member_descriptor
namespace_whitelist Instance of member_descriptor
save_debug_info Instance of member_descriptor