Method save

python/paddle/distributed/auto_parallel/static/engine.py:2374–2457

Saves the model, parameters, optimizer state to path. If `training` is set to False, only inference model will be saved. Args: path (str): The file prefix to save model. The format is 'dirname/file_prefix' or 'file_prefix'. if empty str.

(self, path: str, training: bool = True)

        program.set_state_dict(state_dict)

    def save(self, path: str, training: bool = True) -> None:
        """
        Saves the model, parameters, optimizer state to path.
        If `training` is set to False, only inference model will be saved.

        Args:
            path (str): The file prefix to save model. The format
                is 'dirname/file_prefix' or 'file_prefix'. if empty str.
                A exception will be raised.
            training (bool, optional): Whether to save for training. If not, save
                for inference only. If `training` is set to True, the optimizer state
                will be saved. Otherwise, only the model and parameters are saved.
                This function will silently overwrite existing file at the target
                location. Default: True.

        Returns:
            None

        Examples:

            .. code-block:: pycon

                >>> import paddle
                >>> import paddle.vision.transforms as T
                >>> from paddle.distributed.fleet import auto
                >>> from paddle.vision.datasets import MNIST

                >>> transform = T.Compose([
                ...     T.Transpose(),
                ...     T.Normalize([127.5], [127.5])
                >>> ])
                >>> train_dataset = MNIST(mode='train', transform=transform)

                >>> model = paddle.vision.models.LeNet()
                >>> loss = paddle.nn.CrossEntropyLoss()
                >>> optimizer = paddle.optimizer.Adam(
                ...     learning_rate=0.001,
                ...     parameters=model.parameters(),
                ... )
                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))

                >>> engine = auto.Engine(model, loss, optimizer, metrics)
                >>> engine.fit(train_dataset, epochs=1, batch_size=64)
                >>> engine.save("./my_model")

        """
        if training:
            assert self._mode in self._dist_contexts
            dist_context = self._dist_contexts[self._mode]
            serial_program = dist_context.serial_main_program
            dist_main_prog = dist_context.dist_main_programs[self._cur_rank]
            self._saver.save(
                path,
                serial_program=serial_program,
                dist_main_program=dist_main_prog,
                dist_context=dist_context,
            )
        else: