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This document describes SavedModel, the universal serialization format for TensorFlow models.

SavedModel provides a language-neutral format to save machine-learned models that is recoverable and hermetic. It enables higher-level systems and tools to produce, consume and transform TensorFlow models.

The following is a summary of the features in SavedModel:

• Multiple graphs sharing a single set of variables and assets can be added to a single SavedModel. Each graph is associated with a specific set of tags to allow identification during a load or restore operation.
• Support for SignatureDefs
  • Graphs that are used for inference tasks typically have a set of inputs and outputs. This is called a Signature.
  • SavedModel uses SignatureDefs to allow generic support for signatures that may need to be saved with the graphs.
• Support for Assets.
  • For cases where ops depend on external files for initialization, such as vocabularies, SavedModel supports this via assets.
  • Assets are copied to the SavedModel location and can be read when loading a specific meta graph def.
• Support to clear devices before generating the SavedModel.

The following is a summary of features that are NOT supported in SavedModel. Higher-level frameworks and tools that use SavedModel may provide these.

• Implicit versioning.
• Garbage collection.
• Atomic writes to the SavedModel location.

SavedModel manages and builds upon existing TensorFlow primitives such as TensorFlow Saver and MetaGraphDef. Specifically, SavedModel wraps a TensorFlow Saver. The Saver is primarily used to generate the variable checkpoints. SavedModel will replace the existing TensorFlow Inference Model Format as the canonical way to export TensorFlow graphs for serving.

A SavedModel directory has the following structure:

assets/
assets.extra/
variables/
    variables.data-?????-of-?????
    variables.index
saved_model.pb

• SavedModel protocol buffer
  • saved_model.pb or saved_model.pbtxt
  • Includes the graph definitions as MetaGraphDef protocol buffers.
• Assets
  • Subfolder called assets.
  • Contains auxiliary files such as vocabularies, etc.
• Extra assets
  • Subfolder where higher-level libraries and users can add their own assets that co-exist with the model, but are not loaded by the graph.
  • This subfolder is not managed by the SavedModel libraries.
• Variables
  • Subfolder called variables.
  • Includes output from the TensorFlow Saver.
    • variables.data-?????-of-?????
    • variables.index

The APIs for building and loading a SavedModel are described in this section.

The SavedModel builder is implemented in Python.

The SavedModelBuilder class provides functionality to save multiple meta graph defs, associated variables and assets.

To build a SavedModel, the first meta graph must be saved with variables. Subsequent meta graphs will simply be saved with their graph definitions. If assets need to be saved and written or copied to disk, they can be provided when the meta graph def is added. If multiple meta graph defs are associated with an asset of the same name, only the first version is retained.

Each meta graph added to the SavedModel must be annotated with user specified tags. The tags provide a means to identify the specific meta graph to load and restore, along with the shared set of variables and assets. These tags typically annotate a MetaGraph with it's functionality (e.g. serving or training), and possibly hardware specific aspects such as GPU.

The typical usage of builder is as follows:

export_dir = ...
...
builder = saved_model_builder.SavedModelBuilder(export_dir)
with tf.Session(graph=tf.Graph()) as sess:
...
builder.add_meta_graph_and_variables(sess,
                                     [tag_constants.TRAINING],
                                     signature_def_map=foo_signatures,
                                     assets_collection=foo_assets)
...
with tf.Session(graph=tf.Graph()) as sess:
  ...
  builder.add_meta_graph(["bar-tag", "baz-tag"])
...
builder.save()

The SavedModel loader is implemented in C++ and Python.

The Python version of the SavedModel loader provides load and restore capability for a SavedModel. The load operation requires the session in which to restore the graph definition and variables, the tags used to identify the meta graph def to load and the location of the SavedModel. Upon a load, the subset of variables and assets supplied as part of the specific meta graph def, will be restored into the supplied session.

export_dir = ...
...
with tf.Session(graph=tf.Graph()) as sess:
  loader.load(sess, [tag_constants.TRAINING], export_dir)
  ...

The C++ version of the SavedModel loader provides an API to load a SavedModel from a path, while allowing SessionOptions and RunOptions. Similar to the Python version, the C++ version requires the tags associated with the graph to be loaded, to be specified. The loaded version of SavedModel is referred to as SavedModelBundle and contains the meta graph def and the session within which it is loaded.

const string export_dir = ...
SavedModelBundle bundle;
...
LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain},
               &bundle);

SavedModel offers the flexibility to build and load TensorFlow graphs for a variety of use-cases. For the set of most common expected use-cases, SavedModel's APIs provide a set of constants in Python and C++ that are easy to reuse and share across tools consistently.

Sets of tags can be used to uniquely identify a MetaGraphDef saved in a SavedModel. A subset of commonly used tags is specified in:

• Python
• C++.

SignatureDefs are used to define the signature of a computation supported in a TensorFlow graph. Commonly used input keys, output keys and method names are defined in:

• Python
• C++.