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+## Visual DL
+`Visual DL`: makes your deep learning jobs more alive via visualization.
+
+At present, most deep learning frameworks are using Python. The status of training process is recorded 
+by logs. A sample log is as follow:
+
+
+```shell
+loss:[2.532566] acc:[0.0859375] pass_acc:[0.0859375]
+loss:[2.6916795] acc:[0.09375] pass_acc:[0.08984375]
+loss:[2.7038598] acc:[0.1171875] pass_acc:[0.09895834]
+loss:[2.6405232] acc:[0.125] pass_acc:[0.10546875]
+```
+
+This method can show short-term training status. But it can hardly show long-term trends.
+Visual DL can help you visualize the whole training process and construct plots as below:
+
+
+<p align="center">
+<img src="scalar.png"/>
+</p>
+
+The above is just one of Visual DL's many features. Visual DL has the following advantages:
+
+### Comprehensive Usability 
+
+1. Scalar: support scalar line/dot data visualization, like the figure above.
+    - can show metrics such as loss, accuracy, etc via lines and dots and let user see trends easily
+    - can draw several curves in one figure so that it is easy to compare various metrics
+
+2. Image: support image display.
+    - display input and intermediate images, easy to check data sample
+    - support display for convolutional layer, easy to see results after each layer
+
+<p align="center">
+<img src="dog.png" height="300" width="300"/>
+</p>
+	- support image-generating tasks to preview generated image
+<p align="center">
+<img src="image-gan.png" height="300" width="300"/>
+</p>
+
+3. Histogram: display of parameter distribution, easy to check distribution curves in each tensor, 
+show the trend of parameter distribution.
+
+	- help users understand the training process and the underneath reason for the change from one parameter distribution to another
+	- help users judge if the training is on the track. For example, if parameter change rate becomes close to 0 or grows rapidly, 
+	then exploding and vanishing gradients might happen 
+<p align="center">
+<img src="histogram.png" />
+</p>
+
+4. Graph: visualize the model structure of deep learning networks.
+    - Graph supports the preview of [ONNX](http://onnx.ai/) model. Since models of MXNet, Caffe2, PyTorch and CNTK can be converted to ONNX models easily, 
+    Visual DL can also support these models indirectly
+    - easy to see wrong configuration of a network
+    - help understand network structure
+    
+
+<p align="center">
+<img src="graph.png" height="250" width="400"/>
+</p>
+
+### Easy to Integrate
+
+Visual DL provides independent Python SDK. If the training task is based on Python, user can simply 
+use Visual DL by installing the Visual DL wheel package and importing it into her/his own project. 
+
+
+a. Install Visual DL package.
+
+```shell
+pip install visualdl
+```
+b. In your own Python code, add logic for Visual DL to collect logs.
+
+```python
+import visualdl
+```
+
+### Easy to Use
+a. Add log collecting logic to your own Python code.
+
+```python
+import visualdl
+
+log_writer = LogWriter("./log", sync_cycle=30)
+with log_writer.mode('train') as logger:
+	loss_writer = logger.scalar("loss")
+
+for step in steps:
+	loss = train_process()
+	loss_writer.add_record(step, loss)
+```
+
+b. Launch Visual DL service and you can see the visualization results.
+
+```shell
+visualDL --logdir ./log --port 8080
+```
+
+### Purely Open Source
+As a deep learning visualization tool, Visual DL support most deep learning frameworks. On the SDK perspective, 
+it is easy to integrate into Python and C++ projects. Through ONNX, Visual DL's Graph component can support
+many popular frameworks such as PaddlePaddle, MXNet, PyTorch and  Caffe2.
+
+Welcome everyone to comment and contribute to make Visual DL easier to use, with more features.