|View source on GitHub|
Callback to back up and restore the training state.
tf.keras.callbacks.experimental.BackupAndRestore( backup_dir )
Used in the notebooks
|Used in the tutorials|
BackupAndRestore callback is intended to recover from interruptions that
happened in the middle of a model.fit execution by backing up the
training states in a temporary checkpoint file (based on TF CheckpointManager)
at the end of each epoch. If training restarted before completion, the
training state and model are restored to the most recently saved state at the
beginning of a new model.fit() run.
Note that user is responsible to bring jobs back up.
This callback is important for the backup and restore mechanism for fault
tolerance purpose. And the model to be restored from an previous checkpoint is
expected to be the same as the one used to back up. If user changes arguments
passed to compile or fit, the checkpoint saved for fault tolerance can become
- This callback is not compatible with disabling eager execution.
- A checkpoint is saved at the end of each epoch, when restoring we'll redo any partial work from an unfinished epoch in which the training got restarted (so the work done before a interruption doesn't affect the final model state).
- This works for both single worker and multi-worker mode, only MirroredStrategy and MultiWorkerMirroredStrategy are supported for now.
def on_epoch_begin(self, epoch, logs=None):
if epoch == 4:
callback = tf.keras.callbacks.experimental.BackupAndRestore(
model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10,
batch_size=1, callbacks=[callback, InterruptingCallback()],
history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10,
batch_size=1, callbacks=[callback], verbose=0)
# Only 6 more epochs are run, since first trainning got interrupted at
# zero-indexed epoch 4, second training will continue from 4 to 9.
||String, path to store the checkpoint. e.g. backup_dir = os.path.join(working_dir, 'backup') This is the directory in which the system stores temporary files to recover the model from jobs terminated unexpectedly. The directory cannot be reused elsewhere to store other files, e.g. by BackupAndRestore callback of another training, or by another callback (ModelCheckpoint) of the same training.|
set_model( model )
set_params( params )