See individual tutorial's README for details

A tutorial of Image classification with ResNet.

• Data pipeline with TensorFlow Dataset API
• Model pipeline with Keras (TensorFlow 2's offical high level API)
• Multi-GPU with distributed strategy
• Customized training with callbacks (TensorBoard, Customized learning schedule)

This tutorial explains how to do transfer learning with TensorFlow 2. We will cover:

• Handling Customized Dataset
• Restore Backbone with Keras's application API
• Restore backbone from disk

This tutorial explains how use checkpoint to save and restore model during training.

• Use tf.keras.ModelCheckpoint to save checkpoint
• Resume training from a pre-saved checkpoint

This tutorial explains how to implement early stopping in TensorFlow 2.

• Use tf.keras.EarlyStopping callback to achieve early stopping.

This tutorial explains how to do distributed training across multiple nodes:

• Code boilerplate for multi-node distributed training
• Run code across multiple machines