update

train.py

@@ -179,8 +179,12 @@ if __name__ == "__main__":
         keep_checkpoint_max=cfg.keep_checkpoint_max)
     #Note that this method saves the model file on each card. You need to specify the save path on each card.
     # In this example, get_rank() is added to distinguish different paths.
+    if device_num == 1:
+        outputDirectory = train_dir + "/"
+    if device_num > 1:
+        outputDirectory = train_dir + "/" + str(get_rank()) + "/"
     ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-                                directory=train_dir + "/" + str(get_rank()) + "/",
+                                directory=outputDirectory,
                                 config=config_ck)
     print("============== Starting Training ==============")
     epoch_size = cfg['epoch_size']

train_for_c2net.py

@@ -81,9 +81,13 @@ if __name__ == "__main__":
         keep_checkpoint_max=cfg.keep_checkpoint_max)
     #Note that this method saves the model file on each card. You need to specify the save path on each card.
     # In the example, get_rank() is added to distinguish different paths.
-    ckpoint_cb = ModelCheckpoint(prefix="data_parallel",
-                                 directory=train_dir + "/" + str(get_rank()) + "/",
-                                 config=config_ck)
+    if device_num == 1:
+        outputDirectory = train_dir + "/"
+    if device_num > 1:
+        outputDirectory = train_dir + "/" + str(get_rank()) + "/"
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
+                                directory=outputDirectory,
+                                config=config_ck)
     print("============== Starting Training ==============")
     epoch_size = cfg['epoch_size']
     if (args.epoch_size):

train_for_multidataset.py

@@ -195,13 +195,16 @@ if __name__ == "__main__":
         model = Model(network,net_loss,net_opt,metrics={"accuracy": Accuracy()})
     else:
         model = Model(network, net_loss,net_opt,metrics={"accuracy": Accuracy()},amp_level="O2")
-
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
     #Note that this method saves the model file on each card. You need to specify the save path on each card.
     # In this example, get_rank() is added to distinguish different paths.
+    if device_num == 1:
+        outputDirectory = train_dir + "/"
+    if device_num > 1:
+        outputDirectory = train_dir + "/" + str(get_rank()) + "/"
     ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-                                directory=train_dir + "/" + str(get_rank()) + "/",
+                                directory=outputDirectory,
                                 config=config_ck)
     print("============== Starting Training ==============")
     epoch_size = cfg['epoch_size']