add english intro (#154)

docs/introduction_en.md

+# VisualDL (Visualize the Deep Learning)
+
+## Introduction
+VisualDL is a deep learning visualization tool that can help design deep learning jobs. 
+It includes features such as scalar, parameter distribution, model structure and image visualization. 
+Currently it is being developed at a high pace. 
+New features will be continuously added.
+
+At present, most DNN frameworks use Python as their primary language. VisualDL supports Python by nature. 
+Users can get plentiful visualization results by simply add a few lines of Python code into their model before training.
+  
+  
+Besides Python SDK, VisualDL was writen in C++ on the low level. It also provides C++ SDK that 
+can be integrated into other platforms.  
+
+
+## Component
+VisualDL now provides 4 components:
+
+- graph
+- scalar
+- image
+- histogram
+
+### Graph
+Graph is compatible with ONNX(Open Neural Network Exchange)[https://github.com/onnx/onnx], 
+Cooperated with Python SDK, VisualDL can be compatible with most major DNN frameworks, including 
+PaddlePaddle, PyTorch and MXNet.
+
+<p align="center">
+  <img src="../demo/mxnet/mxnet_graph.gif" width="600" />
+</p>
+
+### Scalar
+Scalar can be used to show the trends of error during training.
+
+
+<p align="center">
+<img src="./images/scalar_demo.png" width="600"/>
+</p>
+
+### Image
+Image can be used to visualize any tensor or intermediate generated image.
+
+<p align="center">
+<img src="./images/image_demo.png" width="600"/>
+</p>
+
+### Histogram
+Histogram can be used to visualize parameter distribution and trends for any tensor.
+
+<p align="center">
+<img src="./images/histogram_demo.png" width="600"/>
+</p>
+
+## SDK
+VisualDL provides both Python SDK and C++ SDK in order to fit more use cases.
+
+
+### Python SDK
+Below is an example of creating a simple Scalar component and inserting data from different timestamps:
+
+```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
+Here is the C++ SDK identical to the Python SDK example above:
+
+```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;
+}
+```
+
+## Launch Board
+After some logs have been generated during training, users can launch board to see real-time data visualization.
+
+
+```
+visualDL --logdir <some log dir>
+```
+
+Board also supports the parameters below for remote access:
+
+- `--host` set IP
+- `--port` set port
+- `--model_pb` specify ONNX format for model file