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)