diff --git a/demo/image-classification/README.md b/demo/image-classification/README.md
index 1246f41a2533e1d0f92d6c78f24bd0b9ed849c78..64d3e0c13a5f9effc06440f96b0c7845d8fceac4 100644
--- a/demo/image-classification/README.md
+++ b/demo/image-classification/README.md
@@ -36,6 +36,8 @@ $ pip install --upgrade paddlepaddle
 --checkpoint_dir: 模型保存路径，PaddleHub会自动保存验证集上表现最好的模型。默认为paddlehub_finetune_ckpt
 --dataset: 使用什么数据集进行finetune, 脚本支持分别是{flowers/dogcat/stanforddogs/indoor67/food101}。默认为flowers
 --use_gpu: 是否使用GPU进行训练，如果机器支持GPU且安装了GPU版本的PaddlePaddle，我们建议您打开这个开关。默认关闭
+--use_data_parallel: 是否使用数据并行，打开该开关时，会将数据分散到不同的卡上进行训练（CPU下会分布到不同线程）。默认关闭
+--use_pyreader: 是否使用pyreader进行数据喂入。默认关闭
 ```
 
 ## 进行预测
@@ -51,6 +53,7 @@ $ pip install --upgrade paddlepaddle
 --checkpoint_dir: 模型保存路径，PaddleHub会自动保存验证集上表现最好的模型。默认为paddlehub_finetune_ckpt
 --dataset: 使用什么数据集进行finetune, 脚本支持分别是{flowers/dogcat}。默认为flowers
 --use_gpu: 使用使用GPU进行训练，如果本机支持GPU且安装了GPU版本的PaddlePaddle，我们建议您打开这个开关。默认关闭
+--use_pyreader: 是否使用pyreader进行数据喂入。默认关闭
 ```
 
 `注意`：进行预测时，所选择的module，checkpoint_dir，dataset必须和finetune所用的一样
diff --git a/demo/image-classification/img_classifier.py b/demo/image-classification/img_classifier.py
index 243c47dc46e49df31736b884f6ab69d7ac0660f3..da1940ecacbd74d17fbf043a3f383f73ef2e62ba 100644
--- a/demo/image-classification/img_classifier.py
+++ b/demo/image-classification/img_classifier.py
@@ -8,12 +8,14 @@ import numpy as np
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--num_epoch",      type=int,   default=1,                          help="Number of epoches for fine-tuning.")
-parser.add_argument("--use_gpu",        type=bool,  default=True,                      help="Whether use GPU for fine-tuning.")
-parser.add_argument("--checkpoint_dir", type=str,   default="paddlehub_finetune_ckpt",  help="Path to save log data.")
-parser.add_argument("--batch_size",     type=int,   default=16,                         help="Total examples' number in batch for training.")
-parser.add_argument("--module",         type=str,   default="resnet50",                 help="Module used as feature extractor.")
-parser.add_argument("--dataset",        type=str,   default="flowers",                  help="Dataset to finetune.")
+parser.add_argument("--num_epoch",          type=int,   default=1,                          help="Number of epoches for fine-tuning.")
+parser.add_argument("--use_gpu",            type=bool,  default=False,                      help="Whether use GPU for fine-tuning.")
+parser.add_argument("--checkpoint_dir",     type=str,   default="paddlehub_finetune_ckpt",  help="Path to save log data.")
+parser.add_argument("--batch_size",         type=int,   default=16,                         help="Total examples' number in batch for training.")
+parser.add_argument("--module",             type=str,   default="resnet50",                 help="Module used as feature extractor.")
+parser.add_argument("--dataset",            type=str,   default="flowers",                  help="Dataset to finetune.")
+parser.add_argument("--use_pyreader",       type=bool,  default=False,                      help="Whether use pyreader to feed data.")
+parser.add_argument("--use_data_parallel",  type=bool,  default=False,                      help="Whether use data parallel.")
 # yapf: enable.
 
 module_map = {
@@ -56,6 +58,8 @@ def finetune(args):
     feed_list = [img.name]
 
     config = hub.RunConfig(
+        use_data_parallel=args.use_data_parallel,
+        use_pyreader=args.use_pyreader,
         use_cuda=args.use_gpu,
         num_epoch=args.num_epoch,
         batch_size=args.batch_size,
diff --git a/demo/image-classification/predict.py b/demo/image-classification/predict.py
index bedc38618fd858fc3e986baf5858c5220ed60b15..b0b10130dc832af940927c8f9c4210de966dc438 100644
--- a/demo/image-classification/predict.py
+++ b/demo/image-classification/predict.py
@@ -8,11 +8,12 @@ import numpy as np
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--use_gpu",        type=bool,  default=False,                      help="Whether use GPU for predict.")
-parser.add_argument("--checkpoint_dir", type=str,   default="paddlehub_finetune_ckpt",  help="Path to save log data.")
-parser.add_argument("--batch_size",     type=int,   default=16,                         help="Total examples' number in batch for training.")
-parser.add_argument("--module",         type=str,   default="resnet50",                 help="Module used as a feature extractor.")
-parser.add_argument("--dataset",        type=str,   default="flowers",                  help="Dataset to finetune.")
+parser.add_argument("--use_gpu",            type=bool,  default=False,                      help="Whether use GPU for predict.")
+parser.add_argument("--checkpoint_dir",     type=str,   default="paddlehub_finetune_ckpt",  help="Path to save log data.")
+parser.add_argument("--batch_size",         type=int,   default=16,                         help="Total examples' number in batch for training.")
+parser.add_argument("--module",             type=str,   default="resnet50",                 help="Module used as a feature extractor.")
+parser.add_argument("--dataset",            type=str,   default="flowers",                  help="Dataset to finetune.")
+parser.add_argument("--use_pyreader",       type=bool,  default=False,                      help="Whether use pyreader to feed data.")
 # yapf: enable.
 
 module_map = {
@@ -56,6 +57,7 @@ def predict(args):
 
     config = hub.RunConfig(
         use_data_parallel=False,
+        use_pyreader=args.use_pyreader,
         use_cuda=args.use_gpu,
         batch_size=args.batch_size,
         enable_memory_optim=False,