diff --git a/README.md b/README.md
index b6b84eb..1351ae3 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,6 @@ Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent
 
 
 ## Requirements
-- hyperopt>=0.1
 - scipy>=0.19.0
 - numpy>=1.12.1
 - pandas>=0.19.2
@@ -22,25 +21,14 @@ Dependency can be installed using the following command:
 pip install -r requirements.txt
 ```
 
-
-## Traffic Data
+## Data Preparation
 The traffic data file for Los Angeles, i.e., `df_highway_2012_4mon_sample.h5`, is available [here](https://drive.google.com/open?id=1tjf5aXCgUoimvADyxKqb-YUlxP8O46pb), and should be
-put into the `data/` folder.
+put into the `data/METR-LA` folder.
 Besides, the locations of sensors are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
-
-## Graph Construction
- As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
- supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
-
 ```bash
-python gen_adj_mx.py  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
-    --output_pkl_filename=data/sensor_graph/adj_mx.pkl
-```
-
-## Train the Model
-```bash
-python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml
+python -m scripts.generate_training_data --output_dir=data/METR-LA
 ```
+The generated train/val/test dataset will be saved at `data/METR-LA/{train,val,test}.npz`.
 
 
 ## Run the Pre-trained Model
@@ -51,6 +39,21 @@ python run_demo.py
 The generated prediction of DCRNN is in `data/results/dcrnn_predictions_[1-12].h5`.
 
 
+## Model Training
+```bash
+python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml
+```
+Each epoch takes about 5min with a single GTX 1080 Ti. There is a chance that train/val loss will explode,  gradient explosion, 
+
+## Graph Construction
+ As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
+ supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
+
+```bash
+python gen_adj_mx.py  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
+    --output_pkl_filename=data/sensor_graph/adj_mx.pkl
+```
+
 More details are being added ...
 
 ## Citation
diff --git a/requirements.txt b/requirements.txt
index 6ab6fc5..ec17d78 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
-hyperopt>=0.1
 scipy>=0.19.0
 numpy>=1.12.1
 pandas>=0.19.2