remove some context params

tutorials/source_en/quick_start/quick_start.md

@@ -99,8 +99,7 @@ if __name__ == "__main__":
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented (default: CPU)')
     args = parser.parse_args()
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
-                        enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     ...
 ```
 

tutorials/source_zh_cn/advanced_use/distributed_training.md

@@ -250,7 +250,6 @@ from resnet import resnet50
 device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=device_id) # set device_id
-context.set_context(enable_loop_sink=True)
 
 def test_train_cifar(num_classes=10, epoch_size=10):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True)
@@ -263,7 +262,7 @@ def test_train_cifar(num_classes=10, epoch_size=10):
     model.train(epoch_size, dataset, callbacks=[loss_cb], dataset_sink_mode=True)
 ```
 其中，  
-- `dataset_sink_mode=True`，`enable_loop_sink=True`：表示采用数据集的下沉模式，即训练的计算下沉到硬件平台中执行。
+- `dataset_sink_mode=True`：表示采用数据集的下沉模式，即训练的计算下沉到硬件平台中执行。
 - `LossMonitor`：能够通过回调函数返回Loss值，用于监控损失函数。
 
 ## 运行脚本

tutorials/source_zh_cn/quick_start/quick_start.md

@@ -101,8 +101,7 @@ if __name__ == "__main__":
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented (default: CPU)')
     args = parser.parse_args()
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
-                        enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     ...
 ```
 

tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py

@@ -36,8 +36,6 @@ from resnet import resnet50
 device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=device_id) # set device_id
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=False)
 init()
 
 rank_id = get_rank()

tutorials/tutorial_code/lenet.py

@@ -195,8 +195,7 @@ if __name__ == "__main__":
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented (default: CPU)')
     args = parser.parse_args()
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
-                        enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     # download mnist dataset
     download_dataset()
     # learning rate setting

tutorials/tutorial_code/resnet/cifar_resnet50.py

@@ -55,8 +55,6 @@ data_home = args_opt.dataset_path
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=device_id)
-context.set_context(enable_loop_sink=False)
-context.set_context(enable_mem_reuse=False)
 
 def create_dataset(repeat_num=1, training=True):
     """

tutorials/tutorial_code/sample_for_cloud/resnet50_train.py

@@ -117,8 +117,6 @@ def resnet50_train(args_opt):
     # set graph mode and parallel mode
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
     context.set_context(device_id=device_id)
-    context.set_context(enable_loop_sink=True)
-    context.set_context(enable_mem_reuse=True)
     if device_num > 1:
         context.set_auto_parallel_context(device_num=device_num,
                                           parallel_mode=ParallelMode.DATA_PARALLEL,