The philosophy of TF 2.0 is based on simplicity and ease of use. The major updates include easy model building with tf.keras and eager execution, robust model deployment for production and commercial use for any platform, powerful experimentation techniques and tools for research, and API simplification for a more intuitive organization of APIs. 

The new organization of TF 2.0 is simplified by the following diagram:

The preceding diagram is focused on using the Python API for training and deploying; however, the same process is followed with the other supported languages including Julia, JavaScript, and R. The flow of TF 2.0 is separated into two sections—model training and model deployment, where model training includes the data pipelines, model creation, training, and distribution strategies; and model deployment includes the variety of means of deployment, such as TF Serving, TFLite, TF.js, and other language bindings. The components in this diagram will each be elaborated upon in their respective chapters.

The biggest change in TF 2.0 is the addition of eager execution. Eager execution is an imperative programming environment that evaluates operations immediately, without necessarily building graphs. All operations return concrete values instead of constructing a computational graph that the user can compute later. 

This makes it significantly easier to build and train TensorFlow models and reduces much of the boilerplate code that was attributed to TF 1.x code. Eager execution has an intuitive interface that follows the standard Python code flow. Code written in eager execution is also much easier to debug, as standard Python modules for debugging, such as pdb, can be used to inspect code for sources of error. The creation of custom models is also easier due to the natural Python control flow and support for iteration.

Another major change in TF 2.0 is the migration to tf.keras as the standard module for creating and training TensorFlow models. The Keras API is the central high-level API in TF 2.0, making it easy to get started with TensorFlow. Although Keras is an independent implementation of deep learning concepts, the tf.keras implementation contains enhancements such as eager execution for immediate iteration and debugging, and tf.data is also included for building scalable input pipelines.

An example workflow in tf.keras would be to first load the data using the tf.data module. This allows for large amounts of data to be streamed from the disk without storing all of the data in memory. Then, the developer builds, trains, and validates the model using tf.keras or the premade estimators. The next step would be to run the model and debug it using the benefits of eager execution. Once the model is ready for full-fledged training, use a distribution strategy for distributed training. Finally, when the model is ready for deployment, export the model to a SavedModel module for deployment through any of the distribution strategies shown in the diagram.

The first major difference between TF 1.x and TF 2.0 is the API organization. TF 2.0 has reduced the redundancies in the API structure. Major changes include the removal of tf.app, tf.flags, and tf.logging in favor of other Python modules, such as absl-py and the built-in logging function. 

The tf.contrib library is also now removed from the main TensorFlow repo. The code implemented in this library has either been moved to a different location or has been shifted to the TensorFlow add-ons library. The reason for this move is that the contrib module had grown beyond what could be maintained in a single repository. 

Other changes include the removal of the QueueRunner module in favor of using tf.data, the removal of graph collections, and changes in how variables are treated. The QueueRunner module was a way of providing data to a model for training, but was quite complicated and harder to use than tf.data, which is now the default way of feeding data to a model. Other benefits of using tf.data for the data pipeline are explained in Chapter 3, Designing and Constructing Input Data Pipelines.

Another major change in TF 2.0 is that there are no more global variables. In TF 1.x, variables created using tf.Variable would be put on the default graph and would still be recoverable through their names. TF 1.x had all sorts of mechanisms as an attempt to help users to recover their variables, such as variable scopes, global collections, and helper methods such as tf.get_global_step and tf.global_variables_initializer. All of this is removed in TF 2.0 for the default variable behavior in Python.