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+# Quick start
+
+VisualDL is a deep learning visualization tool. It can be used to visualize intermediate and final results for training.
+Currently, VisualDL supports visualization features as follows:
+
+- Scalar: plot of trends, can be used to show error trends during training.
+- Image: image visualization, can be used to show intermediate images from CNN.
+- Histogram: can be used to show parameter distribution and trend.
+- Graph: can be used to visualize model structure.
+
+VisualDL provides both Python SDK and C++ SDK in nature. It can support various frameworks. 
+Users can retrieve visualization data by simply adding a few lines of code using Pythong SDK. 
+In addition, users can also have a deep integration by using the C++ SDK at a lower level.
+
+## A Simple Python Demo on Scalar
+For simplicity, we first try to use Python SDK.
+
+The first step of using VisualDL is to create a `LogWriter' that can store visualization data.
+
+
+```python
+from VisualDL import LogWriter
+from random import random
+
+logw = LogWriter("./random_log", sync_cycle=30)
+```
+
+The first parameter points to a folder; the second parameter `sync_cycle` specifies out of how memory operations should be 
+store the data into hard drive. 
+
+There are different modes for model training, such as training, validating and testing. All these correspond to `mode' in VisualDL.
+We can use the following pattern to specify mode:
+
+
+```python
+with logw.mode("train") as logger:
+    pass
+```
+
+Next we create a `Scalar` component. Each component needs a tag. A tag can be a string of any length.
+For example, `layer/classification/error`.
+
+```
+# create scalars in mode train and test.
+with logw.mode('train') as logger:
+    scalar0 = logger.scalar("scratch/scalar")
+
+with logw.mode('test') as logger:
+    scalar1 = logger.scalar("scratch/scalar")
+
+# add scalar records.
+for step in range(200):
+    scalar0.add_record(step, step * 1. / 200)
+    scalar1.add_record(step, 1. - step * 1. / 200)
+```
+
+The example above randomly generated some logs. Next we can open the board page:
+
+```
+visualDL --logdir ./random_log --port 8080
+```
+
+Point your browser to `http://0.0.0.0:8080`, you can see the scalar as follows:
+
+<p align="center">
+<img src="./images/scratch_scalar.png"/>
+</p>
+
+## Scalar Demo in C++
+VisualDL's C++ SDK is very similar to its Python SDK. The Python demo above can be writen in C++ as follows:
+
+```c++
+  const auto dir = "./randomlog";
+  LogWriter logwriter(dir, 30);
+  auto logger = logwriter.AsMode("train");
+
+  components::Scalar<float> scalar0(writer.AddTablet("scalar0"));
+  components::Scalar<float> scalar1(writer.AddTablet("scalar1"));
+
+  for (int step = 0; step < 200; step++) {
+    scalar0.AddRecord(step, step * 1. / 200);
+    scalar1.AddRecord(step, 1. - step * 1. / 200);
+  }
+```
+
+## Visualization Based on ONNX Model Structure
+VisualDL supports the visualization for the format in [ONNX](https://github.com/onnx/onnx). 
+Currently, ONNX supports format conversion among various deep learning frameworks such as `MXNet`, `PyTorch`, `Caffe2`, `Caffe`.
+
+```
+visualDL --logdir somedir --model_pb <path_to_model>
+```
+
+For example, for the MNIST dataset, Graph component can render model graph as below:
+
+<p align=center>
+<img width="70%" src="https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/mxnet/mxnet_graph.gif" />
+</p>
+
+