tf.distribute.experimental.PreemptionWatcher

Watch preemption signal and store it.

tf.distribute.experimental.PreemptionWatcher()

Notice: Currently only support Borg TPU environment with TPUClusterResolver.

This class provides a way to monitor the preemption signal during training on TPU. It will start a background thread to watch the training process, trying to fetch preemption message from the coordination service. When preemption happens, the preempted worker will write the preemption message to the coordination service. Thus getting a non-empty preemption message means there is a preemption happened.

User can use the preemption message as a reliable preemption indicator, and then set the coordinator to reconnect to the TPU worker instead of a fully restart triggered by Borg. For example, a training process with preemption recovery will be like:

keep_running = True
preemption_watcher = None
while keep_running:
  try:
    # Initialize TPU cluster and stratygy.
    resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
    tf.config.experimental_connect_to_cluster(resolver)
    tf.tpu.experimental.initialize_tpu_system(resolver)
    strategy = tf.distribute.TPUStrategy(resolver)

    # PreemptionWatcher must be created after connected to cluster.
    preemption_watcher = tf.distribute.experimental.PreemptionWatcher()
    train_model(strategy)
    keep_running = False
  except Exception as e:
    if preemption_watcher and preemption_watcher.preemption_message:
      keep_running = True
    else:
      raise e

preemption_message A variable to store the preemption message fetched from the coordination service. If it is not None, then there is a preemption happened.