pdfcoffee

Chapter 2
5. Use tf.layers modules to combine predefined "lego bricks" whenever it
is possible, either with Sequential or Functional APIs, or with Subclassing.
Use Estimators if you need to have production-ready models, in particular
if these models need to scale on multiple GPUs, CPUs, or on multiple servers.
When needed, consider converting a tf.keras model into an Estimator.
6. Consider using a distribution strategy across GPUs, CPUs, and multiple
servers. With tf.keras it is easy.
Many other recommendations can be made but the preceding ones are the top six.
TensorFlow 2.x makes the initial learning step very easy and adopting tf.keras
makes it very easy for beginners.
The TensorFlow 2.x ecosystem
Today, TensorFlow 2.x is a rich learning ecosystem where, in addition to the core
learning engine, there is a large collection of tools that can be freely used.
In particular:
• TensorFlow.js (https://www.tensorflow.org/js) is a collection
of APIs to train and run inference directly in browsers or in Node.js.
• TensorFlow Lite (https://www.tensorflow.org/lite) is a lightweight
version of TensorFlow for embedded and mobile devices. Currently, both
Android and iOS are supported in Java and C++.
• TensorFlow Hub (https://www.tensorflow.org/hub) is a complete library
supporting the most common machine learning architectures. As of April
2019, Hub only partially supports the tf.Keras API but this issue (https://
github.com/tensorflow/tensorflow/issues/25362) is going to be solved
soon. We will see an example of Hub in Chapter 5, Advanced Convolutional
Neural Networks.
• TensorFlow Extended (TFX) (https://github.com/tensorflow/tfx) is a
complete end-to-end platform for learning, including tools for transformation
(TfTransform), analysis (TensorFlow Model Analysis), and for efficiently
serving learning models during inference (TensorFlow Serving). TFX
pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines.
• TensorBoard is a visual environment for inspecting, debugging, and
optimizing models and metrics.
• Sonnet is a library similar to Keras, developed by DeepMind for training
their models.
• TensorBoard Federated is a framework for machine learning and other
computations on decentralized data.
[ 81 ]