# tf.distribute.experimental.PreemptionWatcher

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/distribute/failure_handling/preemption_watcher.py#L43-L131) |

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:
          preemption_watcher.block_until_worker_exit()
          keep_running = True
        else:
          raise e

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| ## Attributes ||
|---|---|
| `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. |
| `platform` | A PlatformDevice to indicate the current job's platform. Refer to failure_handling_util.py for the definition of enum class PlatformDevice. |

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## Methods

### `block_until_worker_exit`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/distribute/failure_handling/preemption_watcher.py#L116-L131)

    block_until_worker_exit()

Block coordinator until workers exit.

In some rare cases, another error could be raised during the
preemption grace period. This will cause the coordinator to reconnect to the
same TPU workers, which will be killed later. It prevents the coordinator to
reconnect to new TPU workers, and falls back to a hard restart. To avoid
this situation, this method will block the coordinator to reconnect until
workers exit. This method will be a no-op for non-TPU platform.