Overview

The `export_savedmodel` function in TensorFlow is a powerful utility designed for exporting `Estimator` models into a SavedModel format. This format is flexible and can be utilized for serving the model on TensorFlow Serving, mobile devices, or even converting it to other formats for specialized use cases. In this guide, we will explore how to use the `export_savedmodel` function effectively, diving into technical explanations and providing illustrative examples.

Understanding Estimator and SavedModel

What is an Estimator?

An `Estimator` is an abstraction provided by TensorFlow for simplifying the process of training, evaluating, and serving models. It encapsulates the model function and provides easy-to-use methods like `train`, `evaluate`, and `predict`. Estimators can be custom-built or pre-defined and are particularly useful for their robustness and scalability.

What is a SavedModel?

A SavedModel is TensorFlow's preferred serialization format, characterized by its ease of use and flexibility. It contains both the model's architecture and weights, and even metadata required for serving or evaluation. The SavedModel format supports TensorFlow Serving natively and is beneficial for deployment in various environments.

Exporting an Estimator Model

To export an Estimator model into a SavedModel, we use the `export_savedmodel` method. Below is a step-by-step explanation and example of how this is accomplished.

Prerequisites

Before performing an export, ensure:

• TensorFlow is installed and up-to-date.
• An Estimator model is trained and ready for export.

Basic Usage

Consider the following example, which outlines the steps involved in exporting an Estimator model:

• Optimized for Serving: The SavedModel format is ideal for serving, especially using TensorFlow Serving, reducing the latency and optimizing resource usage.
• Platform Independence: Once exported, the SavedModel can be deployed on different platforms without needing the original training codebase.
• Version Management: Exported models are easily versioned, allowing for backward compatibility and smoother transitions between updates.
• Check Export Path: Ensure that the specified export directory allows write access and is free from conflicting exports.
• Validate Inputs: Use consistent feature definitions in both training and serving to avoid mismatches that can lead to runtime errors.