Update introduction README (#89)

docs/README.md

-# Documents
+# VisualDL (Visualize the Deep Learning)
+
+## Introduction
+
+VisualDL is a visualization tool for deep learning, including scalar, parameter distribution, model structure, image visualization and other features.
+The project is under development, and will provide more features.
+
+Most of the DNN platforms are using Python. VisualDL supports Python out of the box.
+By just adding a few lines of configuration to the code, VisualDL can provide a rich visual support for the training process.
+
+In addition to Python SDK, the underlying VisualDL is written in C++, and its exposed C++ SDK can be integrated into other platforms.
+Users can access the original features and monitor customized matrix. 
+
+## Components
+VisualDL supports four componments：
+
+- graph
+- scalar
+- image
+- histogram
+
+### graph
+Compatible with ONNX (Open Neural Network Exchange) [https://github.com/onnx/onnx]. VisualDL is compatible with the mainstream DNN platforms such as PaddlePaddle, Pytorch, and MXNet through Python SDK.
+
+<p align="center">
+<img src="./images/introduction_graph.png"/>
+</p>
+
+### scalar
+Show the error trend throughout the training. 
+
+<p align="center">
+<img src="./images/introduction_scalar.png"/>
+</p>
+
+### image
+To visualize any tensor, or model generated images
+
+<p align="center">
+<img src="./images/introduction_image.png"/>
+</p>
+
+### histogram
+To visualize the distribution of elements in any tensor
+
+<p align="center">
+<img src="./images/introduction_histogram.png"/>
+</p>
+
+## SDK
+VisualDL also provides Python SDK and C++ SDK for different platforms.
+
+### Python SDK
+Take the simplest Scalar component, for example, to try to create a scalar component and log the data:
+
+```python
+import random
+from visualdl import LogWriter
+
+logdir = "./tmp"
+logger = LogWriter(dir, sync_cycle=10)
+
+# mark the components with 'train' label.
+with logger.mode("train"):
+    # create a scalar component called 'scalars/scalar0'
+    scalar0 = logger.scalar("scalars/scalar0")
+    
+
+# add some records during DL model running, lets start from another block.
+with logger.mode("train"):
+    # add scalars
+    for step in range(100):
+        scalar0.add_record(step, random.random())
+```
+
+### C++ SDK
+The same code for C++ SDK in the above Python SDK is as follows
+```c++
+#include <cstdlib>
+#include <string>
+#include "visualdl/sdk.h"
+
+namespace vs = visualdl;
+namepsace cp = visualdl::components;
+
+int main() {
+  const std::string dir = "./tmp";
+  vs::LogWriter logger(dir, 10);
+  
+  logger.SetMode("train");
+  auto tablet = logger.NewTablet("scalars/scalar0");
+  
+  cp::Scalar<float> scalar0(tablet);
+  
+  for (int step = 0; step < 1000; step++) {
+    float v = (float)std::rand() / RAND_MAX;
+    scalar0.AddRecord(step, v);
+  }
+
+  return 0;
+}
+```
+## Activate Board
+When the log data has been generated during the training process, the board can be started for real-time data visualization.
+
+```
+visualDL --logdir <some log dir>
+```
+
+the Board supports configuration of remote access.
+
+- `--host` Configure IP
+- `--port` Configure port