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Convenience function to build a SavedModel suitable for serving. (deprecated)
tf.compat.v1.saved_model.simple_save(
session, export_dir, inputs, outputs, legacy_init_op=None
)
In many common cases, saving models for serving will be as simple as:
simple_save(session,
export_dir,
inputs={"x": x, "y": y},
outputs={"z": z})
Although in many cases it's not necessary to understand all of the many ways to configure a SavedModel, this method has a few practical implications:
- It will be treated as a graph for inference / serving (i.e. uses the tag
saved_model.SERVING
) - The SavedModel will load in TensorFlow Serving and supports the Predict API. To use the Classify, Regress, or MultiInference APIs, please use either tf.Estimator or the lower level SavedModel APIs.
- Some TensorFlow ops depend on information on disk or other information
called "assets". These are generally handled automatically by adding the
assets to the
GraphKeys.ASSET_FILEPATHS
collection. Only assets in that collection are exported; if you need more custom behavior, you'll need to use the SavedModelBuilder.
More information about SavedModel and signatures can be found here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md