# SignatureDefs in SavedModel for TensorFlow Serving

## Objective

This document provides examples for the intended usage of SignatureDefs in SavedModel that map to TensorFlow Serving's APIs.

## Overview

A SignatureDef defines the signature of a computation supported in a TensorFlow graph. SignatureDefs aim to provide generic support to identify inputs and outputs of a function and can be specified when building a SavedModel.

## Background

TF-Exporter and SessionBundle used Signatures which are similar in concept but required users to distinguish between named and default signatures in order for them to be retrieved correctly upon a load. For those who previously used TF-Exporter/SessionBundle, Signatures in TF-Exporter will be replaced by SignatureDefs in SavedModel.

## SignatureDef Structure

A SignatureDef requires specification of:

• inputs as a map of string to TensorInfo.
• outputs as a map of string to TensorInfo.
• method_name (which corresponds to a supported method name in the loading tool/system).

Note that TensorInfo itself requires specification of name, dtype and tensor shape. While tensor information is already present in the graph, it is useful to explicitly have the TensorInfo defined as part of the SignatureDef since tools can then perform signature validation, etc. without having to read the graph definition.

For ease of reuse and sharing across tools and systems, commonly used constants related to SignatureDefs that will be supported in TensorFlow Serving are defined as constants. Specifically:

In addition, SavedModel provides a util to help build a signature-def.

## Sample structures

TensorFlow Serving provides high level APIs for performing inference. To enable these APIs, models must include one or more SignatureDefs that define the exact TensorFlow nodes to use for input and output. See below for examples of the specific SignatureDefs that TensorFlow Serving supports for each API.

Note that TensorFlow Serving depends on the keys of each TensorInfo (in the inputs and outputs of the SignatureDef), as well as the method_name of the SignatureDef. The actual contents of the TensorInfo are specific to your graph.

### Classification SignatureDef

Classification SignatureDefs support structured calls to TensorFlow Serving's Classification API. These prescribe that there must be an inputs Tensor, and that there are two optional output Tensors: classes and scores, at least one of which must be present.

signature_def: {
key  : "my_classification_signature"
value: {
inputs: {
key  : "inputs"
value: {
name: "tf_example:0"
dtype: DT_STRING
tensor_shape: ...
}
}
outputs: {
key  : "classes"
value: {
name: "index_to_string:0"
dtype: DT_STRING
tensor_shape: ...
}
}
outputs: {
key  : "scores"
value: {
name: "TopKV2:0"
dtype: DT_FLOAT
tensor_shape: ...
}
}
method_name: "tensorflow/serving/classify"
}
}


### Predict SignatureDef

Predict SignatureDefs support calls to TensorFlow Serving's Predict API. These signatures allow you to flexibly support arbitrarily many input and output Tensors. For the example below, the signature my_prediction_signature has a single logical input Tensor images that are mapped to the actual Tensor in your graph x:0.

Predict SignatureDefs enable portability across models. This means that you can swap in different SavedModels, possibly with different underlying Tensor names (e.g. instead of x:0 perhaps you have a new alternate model with a Tensor z:0), while your clients can stay online continuously querying the old and new versions of this model without client-side changes.

Predict SignatureDefs also allow you to add optional additional Tensors to the outputs, that you can explicitly query. Let's say that in addition to the output key below of scores, you also wanted to fetch a pooling layer for debugging or other purposes. In that case, you would simply add an additional Tensor with a key like pool and appropriate value.

signature_def: {
key  : "my_prediction_signature"
value: {
inputs: {
key  : "images"
value: {
name: "x:0"
dtype: ...
tensor_shape: ...
}
}
outputs: {
key  : "scores"
value: {
name: "y:0"
dtype: ...
tensor_shape: ...
}
}
method_name: "tensorflow/serving/predict"
}
}


### Regression SignatureDef

Regression SignatureDefs support structured calls to TensorFlow Serving's Regression API. These prescribe that there must be exactly one inputs Tensor, and one outputs Tensor.

signature_def: {
key  : "my_regression_signature"
value: {
inputs: {
key  : "inputs"
value: {
name: "x_input_examples_tensor_0"
dtype: ...
tensor_shape: ...
}
}
outputs: {
key  : "outputs"
value: {
name: "y_outputs_0"
dtype: DT_FLOAT
tensor_shape: ...
}
}
method_name: "tensorflow/serving/regress"
}
}