diff --git a/bash/workflow_era5.sh b/bash/workflow_era5.sh
index 953acb6dcbf1ba81bb4d7f28d1ff3d6a443811ec..e343d83faa02a19b9a6da167de3609ef95158fc9 100755
--- a/bash/workflow_era5.sh
+++ b/bash/workflow_era5.sh
@@ -9,36 +9,45 @@ set -e
 MODEL=$1
 TRAIN_MODE=$2
 EXP_NAME=$3
-
 DATA_ETL_DIR=/p/scratch/deepacf/${USER}/
 DATA_EXTRA_DIR=${DATA_ETL_DIR}/extractedData/${EXP_NAME}
 DATA_PREPROCESS_DIR=${DATA_ETL_DIR}/preprocessedData/${EXP_NAME}
 DATA_PREPROCESS_TF_DIR=./data/${EXP_NAME}
 RESULTS_OUTPUT_DIR=./results_test_samples/${EXP_NAME}/${TRAIN_MODE}/
 
-if [ $MODEL==savp ]
-then
+if [ $MODEL==savp ]; then
     method_dir=ours_savp
-elif [ $MODEL==gan ]
-then
+elif [ $MODEL==gan ]; then
     method_dir=ours_gan
-elif [ $MODEL==vae ]
-then
+elif [ $MODEL==vae ]; then
     method_dir=ours_vae
 else
     echo "model does not exist" 2>&1
     exit 1
 fi
 
-if [ ${TRAIN_MODE}==pre_trained ]; then
+if [ "$TRAIN_MODE" == pre_trained ]; then
     TRAIN_OUTPUT_DIR=./pretrained_models/kth/${method_dir}
 else
     TRAIN_OUTPUT_DIR=./logs/${EXP_NAME}/${TRAIN_MODE}
-
 fi
+
+CHECKPOINT_DIR=${TRAIN_OUTPUT_DIR}/${method_dir}
+
+echo "===========================WORKFLOW SETUP===================="
+echo "Model ${MODEL}"
+echo "TRAIN MODE ${TRAIN_MODE}"
+echo "Method_dir ${method_dir}"
+echo "DATA_ETL_DIR ${DATA_ETL_DIR}"
+echo "DATA_EXTRA_DIR ${DATA_EXTRA_DIR}"
+echo "DATA_PREPROCESS_DIR ${DATA_PREPROCESS_DIR}"
+echo "DATA_PREPROCESS_TF_DIR ${DATA_PREPROCESS_TF_DIR}"
+echo "TRAIN_OUTPUT_DIR ${TRAIN_OUTPUT_DIR}"
+echo "============================================================="
+
 ##############Datat Preprocessing################
 #To hkl data
-if [ -d ${DATA_PREPROCESS_DIR} ]; then
+if [ -d "$DATA_PREPROCESS_DIR" ]; then
     echo "The Preprocessed Data (.hkl ) exist"
 else
     python ../workflow_video_prediction/DataPreprocess/benchmark/mpi_stager_v2_process_netCDF.py \
@@ -46,24 +55,32 @@ else
 fi
 
 #Change the .hkl data to .tfrecords files
-if [ -d ${DATA_PREPROCESS_TF_DIR} ]; then
-    echo "The Preprocessed Data (tf.records) exist"
+if [ -d "$DATA_PREPROCESS_TF_DIR" ]
+then
+    echo "Step2: The Preprocessed Data (tf.records) exist"
 else
+    echo "Step2: start, hkl. files to tf.records"
     python ./video_prediction/datasets/era5_dataset_v2.py  --source_dir ${DATA_PREPROCESS_DIR}/splits \
     --destination_dir ${DATA_PREPROCESS_TF_DIR}
+    echo "Step2: finish"
 fi
 
 #########Train##########################
-if [ ${TRAIN_MODE}==pre_trained ]; then
-    echo "Using kth trained model "
-else
+if [ "$TRAIN_MODE" == "pre_trained" ]; then
+    echo "step3: Using kth pre_trained model"
+elif [ "$TRAIN_MODE" == "end_to_end" ]; then
+    echo "Step3: Training Starts "
     python ./scripts/train_v2.py --input_dir $DATA_PREPROCESS_TF_DIR --dataset era5  \
     --model ${MODEL} --model_hparams_dict hparams/kth/${method_dir}/model_hparams.json \
     --output_dir ${TRAIN_OUTPUT_DIR}
+    echo "Training ends "
+else
+    echo "TRAIN_MODE is end_to_end or pre_trained"
+    exit 1
 fi
 
 #########Generate results#################
+echo "Step4: Postprocessing start"
 python ./scripts/generate_transfer_learning_finetune.py --input_dir ${DATA_PREPROCESS_TF_DIR} \
---dataset_hparams sequence_length=20 --checkpoint $TRAIN_OUTPUT_DIR\
---mode test --results_dir ${RESULTS_OUTPUT_DIR} \
+--dataset_hparams sequence_length=20 --checkpoint ${CHECKPOINT_DIR_DIR} --mode test --results_dir ${RESULTS_OUTPUT_DIR} \
 --batch_size 4 --dataset era5
\ No newline at end of file
diff --git a/bash/workflow_era5_macOS.sh b/bash/workflow_era5_macOS.sh
index 4ca81535defc0aa3a337643b1c46eff573064a53..baa9f95352d6aa6ca7fac1af7311b72072b69993 100755
--- a/bash/workflow_era5_macOS.sh
+++ b/bash/workflow_era5_macOS.sh
@@ -9,61 +9,78 @@ set -e
 MODEL=$1
 TRAIN_MODE=$2
 EXP_NAME=$3
-
 DATA_ETL_DIR=/p/scratch/deepacf/${USER}/
 DATA_EXTRA_DIR=${DATA_ETL_DIR}/extractedData/${EXP_NAME}
 DATA_PREPROCESS_DIR=${DATA_ETL_DIR}/preprocessedData/${EXP_NAME}
 DATA_PREPROCESS_TF_DIR=./data/${EXP_NAME}
 RESULTS_OUTPUT_DIR=./results_test_samples/${EXP_NAME}/${TRAIN_MODE}/
 
-if [ $MODEL==savp ]
-then
+if [ $MODEL==savp ]; then
     method_dir=ours_savp
-elif [ $MODEL==gan ]
-then
+elif [ $MODEL==gan ]; then
     method_dir=ours_gan
-elif [ $MODEL==vae ]
-then
+elif [ $MODEL==vae ]; then
     method_dir=ours_vae
 else
     echo "model does not exist" 2>&1
     exit 1
 fi
 
-if [ ${TRAIN_MODE}==pre_trained ]; then
+if [ "$TRAIN_MODE" == pre_trained ]; then
     TRAIN_OUTPUT_DIR=./pretrained_models/kth/${method_dir}
 else
     TRAIN_OUTPUT_DIR=./logs/${EXP_NAME}/${TRAIN_MODE}
 fi
 
+CHECKPOINT_DIR=${TRAIN_OUTPUT_DIR}/${method_dir}
+
+echo "===========================WORKFLOW SETUP===================="
+echo "Model ${MODEL}"
+echo "TRAIN MODE ${TRAIN_MODE}"
+echo "Method_dir ${method_dir}"
+echo "DATA_ETL_DIR ${DATA_ETL_DIR}"
+echo "DATA_EXTRA_DIR ${DATA_EXTRA_DIR}"
+echo "DATA_PREPROCESS_DIR ${DATA_PREPROCESS_DIR}"
+echo "DATA_PREPROCESS_TF_DIR ${DATA_PREPROCESS_TF_DIR}"
+echo "TRAIN_OUTPUT_DIR ${TRAIN_OUTPUT_DIR}"
+echo "============================================================="
+
 ##############Datat Preprocessing################
 #To hkl data
-if [ -d ${DATA_PREPROCESS_DIR} ]; then
-    echo "The Preprocessed Data (.hkl ) exist"
-else
-    python ../workflow_video_prediction/DataPreprocess/benchmark/mpi_stager_v2_process_netCDF.py \
-    --input_dir ${DATA_EXTRA_DIR} --destination_dir ${DATA_PREPROCESS_DIR}
-fi
+#if [ -d "$DATA_PREPROCESS_DIR" ]; then
+#    echo "The Preprocessed Data (.hkl ) exist"
+#else
+#    python ../workflow_video_prediction/DataPreprocess/benchmark/mpi_stager_v2_process_netCDF.py \
+#    --input_dir ${DATA_EXTRA_DIR} --destination_dir ${DATA_PREPROCESS_DIR}
+#fi
 
 #Change the .hkl data to .tfrecords files
-if [ -d ${DATA_PREPROCESS_TF_DIR} ]; then
-    echo "The Preprocessed Data (tf.records) exist"
+if [ -d "$DATA_PREPROCESS_TF_DIR" ]
+then
+    echo "Step2: The Preprocessed Data (tf.records) exist"
 else
+    echo "Step2: start, hkl. files to tf.records"
     python ./video_prediction/datasets/era5_dataset_v2.py  --source_dir ${DATA_PREPROCESS_DIR}/splits \
     --destination_dir ${DATA_PREPROCESS_TF_DIR}
+    echo "Step2: finish"
 fi
 
 #########Train##########################
-if [ ${TRAIN_MODE}==pre_trained ]; then
-    echo "Using kth trained model "
-else
+if [ "$TRAIN_MODE" == "pre_trained" ]; then
+    echo "step3: Using kth pre_trained model"
+elif [ "$TRAIN_MODE" == "end_to_end" ]; then
+    echo "Step3: Training Starts "
     python ./scripts/train_v2.py --input_dir $DATA_PREPROCESS_TF_DIR --dataset era5  \
     --model ${MODEL} --model_hparams_dict hparams/kth/${method_dir}/model_hparams.json \
     --output_dir ${TRAIN_OUTPUT_DIR}
+    echo "Training ends "
+else
+    echo "TRAIN_MODE is end_to_end or pre_trained"
+    exit 1
 fi
 
 #########Generate results#################
+echo "Step4: Postprocessing start"
 python ./scripts/generate_transfer_learning_finetune.py --input_dir ${DATA_PREPROCESS_TF_DIR} \
---dataset_hparams sequence_length=20 --checkpoint $TRAIN_OUTPUT_DIR\
---mode test --results_dir ${RESULTS_OUTPUT_DIR} \
+--dataset_hparams sequence_length=20 --checkpoint ${CHECKPOINT_DIR_DIR} --mode test --results_dir ${RESULTS_OUTPUT_DIR} \
 --batch_size 4 --dataset era5
\ No newline at end of file