Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

Chapter 14. TensorFlow Models in Production with TF Serving

The TensorFlow models are trained and validated in the development environment. Once released, they need to be hosted somewhere to be made available to application engineers and software engineers to integrate into various applications. TensorFlow provides a high-performance server for this purpose, known as TensorFlow Serving.

For serving TensorFlow models in production, one would need to save them after training offline and then restore the trained models in the production environment. A TensorFlow model consists of the following files when saved:

  • meta-graph: The meta-graph represents the protocol buffer definition of the graph. The meta-graph is saved in files with the .meta extension.
  • checkpoint: The checkpoint represents the values of various variables. The checkpoint is saved in two files: one with the .index extension and one with the .data-00000-of-00001 extension.

In this chapter, we shall learn various ways to save and restore...