tf.compat.v1.saved_model.simple_save

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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
)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.simple_save.

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.

Args: