def _worker_fn(_):
"""Run evaluation inside the distributed coordinator."""
filtered_callbacks = dist_utils.filter_distributed_callbacks(
callbacks)
return evaluate_distributed(model,
x=x,
y=y,
batch_size=batch_size,
verbose=verbose,
sample_weight=sample_weight,
steps=steps,
callbacks=filtered_callbacks)
def _worker_fn(_):
"""Run training inside the distributed coordinator."""
filtered_callbacks = dist_utils.filter_distributed_callbacks(
callbacks)
return fit_distributed(model,
x=x,
y=y,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=filtered_callbacks,
validation_split=validation_split,
validation_data=validation_data,
shuffle=shuffle,
class_weight=class_weight,
sample_weight=sample_weight,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
validation_freq=validation_freq)
def _worker_fn(_):
callbacks = kwargs.pop('callbacks', None)
filtered_callbacks = dist_utils.filter_distributed_callbacks(
callbacks)
kwargs['callbacks'] = filtered_callbacks
return fn(instance, model, **kwargs)
def _worker_fn(_):
callbacks = kwargs.pop('callbacks', None)
filtered_callbacks = dist_utils.filter_distributed_callbacks(
callbacks, model)
kwargs['callbacks'] = filtered_callbacks
return method(model, **kwargs)