fix_distributed_training_doc bug

tutorials/source_en/advanced_use/distributed_training.md

@@ -256,11 +256,13 @@ 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
 
-def test_train_cifar(num_classes=10, epoch_size=10):
+def test_train_cifar(epoch_size=10):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True)
     loss_cb = LossMonitor()
-    dataset = create_dataset(epoch_size)
-    net = resnet50(32, num_classes)
+    dataset = create_dataset(data_path, epoch_size)
+    batch_size = 32
+    num_classes = 10
+    net = resnet50(batch_size, num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
@@ -342,17 +344,14 @@ The running time is about 5 minutes, which is mainly occupied by operator compil
 Log files are saved in the device directory. The env.log file records environment variable information. The train.log file records the loss function information. The following is an example:
 
 ```
-resnet50_distributed_training.py::test_train_feed ===============ds_num 195
-global_step: 194, loss: 1.997
-global_step: 389, loss: 1.655
-global_step: 584, loss: 1.723
-global_step: 779, loss: 1.807
-global_step: 974, loss: 1.417
-global_step: 1169, loss: 1.195
-global_step: 1364, loss: 1.238
-global_step: 1559, loss: 1.456
-global_step: 1754, loss: 0.987
-global_step: 1949, loss: 1.035
-end training
-PASSED
+epoch: 1 step: 156, loss is 2.0084016
+epoch: 2 step: 156, loss is 1.6407638
+epoch: 3 step: 156, loss is 1.6164391
+epoch: 4 step: 156, loss is 1.6838071
+epoch: 5 step: 156, loss is 1.6320667
+epoch: 6 step: 156, loss is 1.3098773
+epoch: 7 step: 156, loss is 1.3515002
+epoch: 8 step: 156, loss is 1.2943741
+epoch: 9 step: 156, loss is 1.2316195
+epoch: 10 step: 156, loss is 1.1533381
 ```

tutorials/source_zh_cn/advanced_use/distributed_training.md

@@ -254,11 +254,13 @@ 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
 
-def test_train_cifar(num_classes=10, epoch_size=10):
+def test_train_cifar(epoch_size=10):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True)
     loss_cb = LossMonitor()
-    dataset = create_dataset(epoch_size)
-    net = resnet50(32, num_classes)
+    dataset = create_dataset(data_path, epoch_size)
+    batch_size = 32
+    num_classes = 10
+    net = resnet50(batch_size, num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
@@ -340,17 +342,14 @@ cd ../
 日志文件保存device目录下，env.log中记录了环境变量的相关信息，关于Loss部分结果保存在train.log中，示例如下：
 
 ```
-resnet50_distributed_training.py::test_train_feed ===============ds_num 195
-global_step: 194, loss: 1.997
-global_step: 389, loss: 1.655
-global_step: 584, loss: 1.723
-global_step: 779, loss: 1.807
-global_step: 974, loss: 1.417
-global_step: 1169, loss: 1.195
-global_step: 1364, loss: 1.238
-global_step: 1559, loss: 1.456
-global_step: 1754, loss: 0.987
-global_step: 1949, loss: 1.035
-end training
-PASSED
+epoch: 1 step: 156, loss is 2.0084016
+epoch: 2 step: 156, loss is 1.6407638
+epoch: 3 step: 156, loss is 1.6164391
+epoch: 4 step: 156, loss is 1.6838071
+epoch: 5 step: 156, loss is 1.6320667
+epoch: 6 step: 156, loss is 1.3098773
+epoch: 7 step: 156, loss is 1.3515002
+epoch: 8 step: 156, loss is 1.2943741
+epoch: 9 step: 156, loss is 1.2316195
+epoch: 10 step: 156, loss is 1.1533381
 ```