add insatll nvidia_dali in prepare.sh

test_tipc/config/ResNet/ResNet50_vd_train_use_dali.txt

+===========================train_params===========================
+model_name:ResNet50_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null|amp
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.use_dali=True
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##

test_tipc/prepare.sh

@@ -108,6 +108,11 @@ if [[ $FILENAME == *GeneralRecognition* ]];then
    exit 0
 fi
 
+if [[ $FILENAME == *use_dali* ]];then
+    python_name=$(func_parser_value "${lines[2]}")
+    ${python_name} -m pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly --upgrade nvidia-dali-nightly-cuda102
+fi
+
 if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ];then
     # pretrain lite train data
     cd dataset