modify save_checkpoint

tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -165,7 +165,7 @@ The parameter name is model\_parallel\_weight and the dividing strategy is to pe
 2. Call the `save_checkpoint` API to write the parameter data to a file and generate a new checkpoint file.
 
    ```
-   save_checkpoint(param_list, “./CKP-Integrated_1-4_32.ckpt”)
+   save_checkpoint(save_obj, “./CKP-Integrated_1-4_32.ckpt”)
    ```
 
    In the preceding information:

tutorials/source_en/advanced_use/customized_debugging_information.md

@@ -138,7 +138,7 @@ Here are two examples to further understand the usage of custom Callback.
 - Save the checkpoint file with the highest accuracy during training.
 
     ```python
-    from mindspore.train.serialization import _exec_save_checkpoint
+    from mindspore.train.serialization import save_checkpoint
 
     class SaveCallback(Callback):
         def __init__(self, model, eval_dataset):
@@ -155,7 +155,7 @@ Here are two examples to further understand the usage of custom Callback.
             if result['acc'] > self.acc:
                 self.acc = result['acc']
                 file_name = str(self.acc) + ".ckpt"
-                _exec_save_checkpoint(train_network=cb_params.train_network, ckpt_file_name=file_name)
+                save_checkpoint(save_obj=cb_params.train_network, ckpt_file_name=file_name)
                 print("Save the maximum accuracy checkpoint,the accuracy is", self.acc)
 
 

tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -169,7 +169,7 @@ strategy = build_searched_strategy("./strategy_train.cpkt")
 
 2. 调用`save_checkpoint`接口，将参数数据写入文件，生成新的CheckPoint文件。
    ```
-   save_checkpoint(param_list, “./CKP-Integrated_1-4_32.ckpt”)
+   save_checkpoint(save_obj, “./CKP-Integrated_1-4_32.ckpt”)
    ```
     其中，
     - `save_checkpoint`： 通过该接口将网络模型参数信息存入文件。

tutorials/source_zh_cn/advanced_use/customized_debugging_information.md

@@ -140,7 +140,7 @@ Callback可以把训练过程中的重要信息记录下来，通过一个字典
 - 保存训练过程中精度最高的checkpoint文件。
 
     ```python
-    from mindspore.train.serialization import _exec_save_checkpoint
+    from mindspore.train.serialization import save_checkpoint
 
     class SaveCallback(Callback):
         def __init__(self, model, eval_dataset):
@@ -157,7 +157,7 @@ Callback可以把训练过程中的重要信息记录下来，通过一个字典
             if result['acc'] > self.acc:
                 self.acc = result['acc']
                 file_name = str(self.acc) + ".ckpt"
-                _exec_save_checkpoint(train_network=cb_params.train_network, ckpt_file_name=file_name)
+                save_checkpoint(save_obj=cb_params.train_network, ckpt_file_name=file_name)
                 print("Save the maximum accuracy checkpoint,the accuracy is", self.acc)
 
 

tutorials/source_zh_cn/advanced_use/gradient_accumulation.md

@@ -52,7 +52,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from mindspore.train.dataset_helper import DatasetHelper
-from mindspore.train.serialization import _exec_save_checkpoint
+from mindspore.train.serialization import save_checkpoint
 from model_zoo.official.cv.lenet.src.dataset import create_dataset
 from model_zoo.official.cv.lenet.src.lenet import LeNet5
 ```
@@ -199,7 +199,7 @@ class GradientAccumulation:
 
             train_dataset.reset()
 
-        _exec_save_checkpoint(self._train_forward_backward, "gradient_accumulation.ckpt", )
+        save_checkpoint(self._train_forward_backward, "gradient_accumulation.ckpt", )
 ```
 
 ### 训练并保存模型

tutorials/tutorial_code/gradient_accumulation/train.py

@@ -9,7 +9,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from mindspore.train.dataset_helper import DatasetHelper
-from mindspore.train.serialization import _exec_save_checkpoint
+from mindspore.train.serialization import save_checkpoint
 from model_zoo.official.cv.lenet.src.dataset import create_dataset
 from model_zoo.official.cv.lenet.src.lenet import LeNet5
 
@@ -124,7 +124,7 @@ class GradientAccumulation:
 
             train_dataset.reset()
 
-        _exec_save_checkpoint(self._train_forward_backward, "gradient_accumulation.ckpt", )
+        save_checkpoint(self._train_forward_backward, "gradient_accumulation.ckpt", )
 
 
 if __name__ == "__main__":