diff --git a/official/cv/unet/train.py b/official/cv/unet/train.py
index f6986c597f2001b8f1fcf8928ee4cffb5c553d6f..3fe67f67561696b97c24123db9f1486133e5c2b5 100644
--- a/official/cv/unet/train.py
+++ b/official/cv/unet/train.py
@@ -89,7 +89,9 @@ def train_net(cross_valid_ind=1,
                                                    eval_resize=config.eval_resize, split=split, shuffle=False)
     else:
         repeat = config.repeat
-        dataset_sink_mode = True
+        dataset_sink_mode = False
+        if config.device_target == "GPU":
+            dataset_sink_mode = True
         per_print_times = 1
         train_dataset, valid_dataset = create_dataset(data_dir, repeat, batch_size, True, cross_valid_ind,
                                                       run_distribute, config.crop, config.image_size)
diff --git a/official/recommend/naml/README.md b/official/recommend/naml/README.md
index 95481ff1f51a56ab49606fa04a53f82cd51414b9..c3371b9fc6cc4f325de00fe2f9a24d1ca28e928e 100644
--- a/official/recommend/naml/README.md
+++ b/official/recommend/naml/README.md
@@ -22,7 +22,9 @@ NAML is a multi-view news recommendation approach. The core of NAML is a news en
 
 # [Dataset](#contents)
 
-Dataset used: [MIND](https://msnews.github.io/)
+Dataset used: [MIND](https://msnews.github.io/). You can download [MINDlarge_train](https://mind201910small.blob.core.windows.net/release/MINDlarge_train.zip),
+[MINDlarge_dev](https://mind201910small.blob.core.windows.net/release/MINDlarge_dev.zip),
+[MINDlarge_utils](https://mind201910small.blob.core.windows.net/release/MINDlarge_utils.zip)
 
 MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users.