tf.saved_model.SaveOptions

Options for saving to SavedModel.

tf.saved_model.SaveOptions(
    namespace_whitelist=None,
    save_debug_info=False,
    function_aliases=None,
    experimental_debug_stripper=False,
    experimental_io_device=None,
    experimental_variable_policy=None,
    experimental_custom_gradients=True,
    experimental_image_format=False,
    experimental_skip_saver=False,
    experimental_sharding_callback=None
)

Used in the notebooks

Used in the guide Used in the tutorials

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. If no whitelist is provided, all namespaced ops will be allowed. 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_debug_stripper bool. If set to True, this strips the debug nodes from the graph, from both the nodes and the function defs. Note that this currently only strips the Assert nodes from the graph and converts them into NoOps instead. 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 True. experimental_image_format New (highly) experimental format that is capable of saving models larger than the 2GB protobuf limit. Enabling this option will likely break compatibility with downstream consumers. This option is currently disabled in OSS. experimental_skip_saver If True, will prevent SavedModel from creating its native checkpointing ops - this is for models that do not use SavedModel's native checkpointing functionality to avoid the costs associated with creating and serializing those ops. experimental_sharding_callback tf.train.experimental.ShardingCallback. A pre-made or custom callback that determines how checkpoints are sharded on disk. Pre-made callback options are tf.train.experimental.ShardByDevicePolicy and tf.train.experimental.MaxShardSizePolicy. You may also write a custom callback, see tf.train.experimental.ShardingCallback.

experimental_custom_gradients

experimental_debug_stripper

experimental_image_format

experimental_io_device

experimental_sharding_callback

experimental_skip_saver

experimental_variable_policy

function_aliases

namespace_whitelist

save_debug_info