One of the strengths of TF 2.0 is to be able to train and inference your model in a distributed manner on multiple GPUs and TPUs without writing a lot of code. This is simplified using the distribution strategy API, tf.distribute.Strategy(...), which is readily available for use. The fit() API section, which explains tf.keras.Model.fit(...), showed how this function was used to train a model. In this section, we will show how to train tf.keras-based models across multiple GPUs and TPUs using a distribution strategy. It's worth noting that tf.distribute.Strategy(...) is available with high-level APIs such as tf.keras and tf.estimator, along with having support for custom training loops as well or for any computation in general. Also, the distribution strategy described here is supported for eagerly executed programs, such as models written using TF...
What's New in TensorFlow 2.0
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What's New in TensorFlow 2.0
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Overview of this book
TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features.
What's New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You'll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis.
By the end of the book, you'll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.
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Current Title:
What's New in TensorFlow 2.0
Table of Contents (13 chapters)
Title Page
Copyright and Credits
Contributors
About Packt
Preface
Free Chapter
Getting Started with TensorFlow 2.0
Keras Default Integration and Eager Execution
Designing and Constructing Input Data Pipelines
Model Training and Use of TensorBoard
Model Inference Pipelines - Multi-platform Deployments
AIY Projects and TensorFlow Lite
Migrating From TensorFlow 1.x to 2.0
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Customer Reviews