tf.keras.callbacks.ModelCheckpoint vs tf.train.Checkpoint
It depends on whether a custom training loop is required. In most cases, it's not and you can just call model.fit() and pass tf.keras.callbacks.ModelCheckpoint. If you do need to write your custom training loop, then you have to use tf.train.Checkpoint (and tf.train.CheckpointManager) since there's no callback mechanism.
I am kinda new to TensorFlow world but have written some programs in Keras. Since TensorFlow 2 is officially similar to Keras, I am quite confused about what is the difference between tf.keras.callbacks.ModelCheckpoint and tf.train.Checkpoint. If anybody can shed light on this, I would appreciate it.