> how do you handle and account for preemption?

I do most of my experiments with Jupyter Notebooks and Keras on top of TensorFlow. Keras has a ModelCheckpoint callback (https://keras.io/callbacks/#modelcheckpoint) which saves a model to disk after each epoch and is super easy to implement (1 LOC), and a good idea even if I wasn't training on a preemptable instance. In the event of an unexpected preemption, I can just retransform the data (easy with a Jupyter-organized workflow), load the last-saved model (1 LOC) and resume training.

The drawback there is if the epochs are long, which could risk in losing more-than-wanted progress due to a preemption.