Method dump_checkpoint

utils/pl_utils.py:743–774

(self)

        self._atomic_save(checkpoint, filepath)

    def dump_checkpoint(self):

        checkpoint = {
            'epoch': self.current_epoch,
            'global_step': self.global_step
        }

        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
            checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best

        # save optimizers
        optimizer_states = []
        for i, optimizer in enumerate(self.optimizers):
            if optimizer is not None:
                optimizer_states.append(optimizer.state_dict())

        checkpoint['optimizer_states'] = optimizer_states

        # save lr schedulers
        lr_schedulers = []
        for i, scheduler in enumerate(self.lr_schedulers):
            lr_schedulers.append(scheduler.state_dict())

        checkpoint['lr_schedulers'] = lr_schedulers

        # add the hparams and state_dict from the model
        model = self.get_model()
        checkpoint['state_dict'] = model.state_dict()
        # give the model a chance to add a few things
        model.on_save_checkpoint(checkpoint)

        return checkpoint

    def copy_trainer_model_properties(self, model):
        if isinstance(model, DP):