Follow comments.

fluid/object_detection/train.py

@@ -18,6 +18,7 @@ add_arg('batch_size',       int,   32,        "Minibatch size.")
 add_arg('num_passes',       int,   25,        "Epoch number.")
 add_arg('parallel',         bool,  True,      "Whether use parallel training.")
 add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
+add_arg('use_nccl',         bool,  False,     "Whether use NCCL.")
 add_arg('dataset',          str, 'pascalvoc', "coco or pascalvoc.")
 add_arg('model_save_dir',   str, 'model',     "The path to save model.")
 add_arg('pretrained_model', str, 'pretrained/ssd_mobilenet_v1_coco/', "The init model path.")
@@ -57,7 +58,7 @@ def parallel_do(args,
 
     if args.parallel:
         places = fluid.layers.get_places()
-        pd = fluid.layers.ParallelDo(places)
+        pd = fluid.layers.ParallelDo(places, use_nccl=args.use_nccl)
         with pd.do():
             image_ = pd.read_input(image)
             gt_box_ = pd.read_input(gt_box)
@@ -224,7 +225,8 @@ def parallel_exe(args,
             return os.path.exists(os.path.join(pretrained_model, var.name))
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
+    train_exe = fluid.ParallelExecutor(use_cuda=args.use_gpu,
+                                       loss_name=loss.name)
 
     train_reader = paddle.batch(
         reader.train(data_args, train_file_list), batch_size=batch_size)