remove enbale_hccl doc

tutorials/source_en/advanced_use/distributed_training.md

@@ -84,7 +84,7 @@ export DEVICE_ID=0
 
 ### Invoking the Collective Communication Library
 
-You need to enable the distributed API `enable_hccl` in the `context.set_context()` API, set the `device_id` parameter, and invoke `init()` to complete the initialization operation.
+You need to set the `device_id` parameter, and invoke `init()` to complete the initialization operation.
 
 In the sample, the graph mode is used during runtime. On the Ascend AI processor, Huawei Collective Communication Library (HCCL) is used.
 
@@ -94,7 +94,7 @@ from mindspore import context
 from mindspore.communication.management import init
 
 if __name__ == "__main__":
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", enable_hccl=True, device_id=int(os.environ["DEVICE_ID"]))
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=int(os.environ["DEVICE_ID"]))
     init()
     ...   
 ```

tutorials/source_zh_cn/advanced_use/distributed_training.md

@@ -97,16 +97,15 @@ from mindspore import context
 from mindspore.communication.management import init
 
 if __name__ == "__main__":
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", enable_hccl=True, device_id=int(os.environ["DEVICE_ID"]))
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=int(os.environ["DEVICE_ID"]))
     init()
     ...   
 ```
 
 其中，  
 - `mode=context.GRAPH_MODE`：使用分布式训练需要指定运行模式为图模式（PyNative模式不支持并行）。
-- `enable_hccl=True`：使能HCCL通信。
 - `device_id`：卡的物理序号，即卡所在机器中的实际序号。
-- `init()`：完成分布式训练初始化操作。
+- `init()`：使能HCCL通信，并完成分布式训练初始化操作。
 
 ## 数据并行模式加载数据集
 
@@ -240,7 +239,6 @@ from resnet import resnet50
 device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(enable_task_sink=True, device_id=device_id) # set task_sink and device_id
-context.set_context(enable_hccl=True) # set enable_hccl
 context.set_context(enable_loop_sink=True)
 
 def test_train_cifar(num_classes=10, epoch_size=10):

tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py

@@ -36,7 +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(enable_task_sink=True, device_id=device_id) # set task_sink and device_id
-context.set_context(enable_hccl=True) # set enable_hccl
 context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=False)
 init()

tutorials/tutorial_code/resnet/cifar_resnet50.py

@@ -106,16 +106,10 @@ def create_dataset(repeat_num=1, training=True):
 
 if __name__ == '__main__':
     # in this way by judging the mark of args, users will decide which function to use
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     epoch_size = args_opt.epoch_size
     net = resnet50(args_opt.batch_size, args_opt.num_classes)