tf.saved_model.SaveOptions

Options for saving to SavedModel.

View aliases

Compat aliases for migration

tf.compat.v1.saved_model.SaveOptions

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

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

Args
`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. ```python class MyModel: @tf.function def func(): ... @tf.function def serve(): ... func() model = MyModel() signatures = { 'serving_default': model.serve.get_concrete_function(), } options = tf.saved_model.SaveOptions(function_aliases={ 'my_func': func, }) tf.saved_model.save(model, export_dir, signatures, options) ```</td> </tr><tr> <td>`experimental_io_device`</td> <td> 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.

Args

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. ```python class MyModel: @tf.function def func(): ...

@tf.function def serve(): ... func()

model = MyModel() signatures = { 'serving_default': model.serve.get_concrete_function(), } options = tf.saved_model.SaveOptions(function_aliases={ 'my_func': func, }) tf.saved_model.save(model, export_dir, signatures, options) ``</td> </tr><tr> <td>experimental_io_device</td> <td> string. Applies in a distributed setting. Tensorflow device to use to access the filesystem. IfNone` (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.

Class Variables
experimental_io_device
experimental_variable_policy
function_aliases
namespace_whitelist
save_debug_info

Class Variables

experimental_io_device

experimental_variable_policy

function_aliases

namespace_whitelist

save_debug_info