diff --git a/example/resnet50_cifar10/dataset.py b/example/resnet50_cifar10/dataset.py
index 9ed16f08b5555d2f6d73c28388a28282fb1dca8e..1889da95b65ff79277fb2cdd5c988933520bd4e0 100755
--- a/example/resnet50_cifar10/dataset.py
+++ b/example/resnet50_cifar10/dataset.py
@@ -40,9 +40,9 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
     rank_id = int(os.getenv("RANK_ID"))
 
     if device_num == 1:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True)
+        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
     else:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True,
+        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                num_shards=device_num, shard_id=rank_id)
 
     resize_height = config.image_height
@@ -68,11 +68,8 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
 
     type_cast_op = C2.TypeCast(mstype.int32)
 
-    ds = ds.map(input_columns="label", operations=type_cast_op)
-    ds = ds.map(input_columns="image", operations=trans)
-
-    # apply shuffle operations
-    ds = ds.shuffle(buffer_size=config.buffer_size)
+    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
+    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
 
     # apply batch operations
     ds = ds.batch(batch_size, drop_remainder=True)
diff --git a/example/resnet50_cifar10/run_distribute_train.sh b/example/resnet50_cifar10/run_distribute_train.sh
index e78e2bf104598249383fd6e9abb8d0e28a4bd713..5165f58cab237c4733da84f60b27980e0ca67c8c 100755
--- a/example/resnet50_cifar10/run_distribute_train.sh
+++ b/example/resnet50_cifar10/run_distribute_train.sh
@@ -36,6 +36,7 @@ ulimit -u unlimited
 export DEVICE_NUM=8
 export RANK_SIZE=8
 export MINDSPORE_HCCL_CONFIG_PATH=$1
+export RANK_TABLE_FILE=$1
 
 for((i=0; i<${DEVICE_NUM}; i++))
 do
diff --git a/example/resnet50_cifar10/train.py b/example/resnet50_cifar10/train.py
index b18c3778de013e77bac8a0f0386d00b1930ee5d5..0a0299b2bdd7d10d6de27cc3bee07de8c81a0194 100755
--- a/example/resnet50_cifar10/train.py
+++ b/example/resnet50_cifar10/train.py
@@ -61,7 +61,7 @@ if __name__ == '__main__':
             context.set_context(enable_hccl=True)
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+            auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
             init()
         else:
             context.set_context(enable_hccl=False)
diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index 46c28dec888da86a335cb33a637473b5f541238b..9cd6d8e05b3bc58726906ab72ae1ce6a60d14c53 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -359,7 +359,12 @@ void MsContext::GetGeOptions(std::map<std::string, std::string> *ge_options) con
   }
 
   // Enable auto mixed precision according to the context options
-  (*ge_options)["ge.exec.auto_mix_precision"] = std::to_string(auto_mixed_precision_flag_);
+  if (auto_mixed_precision_flag_) {
+    (*ge_options)["ge.exec.precision_mode"] = "allow_mix_precision";
+  } else {
+    (*ge_options)["ge.exec.precision_mode"] = "must_keep_origin_dtype";
+  }
+
   // Disable the global variable acc, only enable it whlie adding training graph in pipeline
   (*ge_options)["ge.exec.variable_acc"] = "0";
 #endif