GitHub homepage readme (#163)

docs/introduction_cn.md → README.cn.md

+<p align="center">
+  <img src="https://github.com/daming-lu/large_files/blob/master/visualdl-readme.png" width="60%" />
+</p>
+
 # VisualDL (Visualize the Deep Learning)
 
 ## 介绍
@@ -21,21 +25,21 @@ VisualDL 目前支持4种组件：
 兼容 ONNX(Open Neural Network Exchange)[https://github.com/onnx/onnx], 通过与 python SDK的结合，VisualDL可以兼容包括 PaddlePaddle, pytorch, mxnet在内的大部分主流DNN平台。
 
 <p align="center">
-  <img src="../demo/mxnet/mxnet_graph.gif" width="600" />
+  <img src="https://github.com/daming-lu/large_files/blob/master/graph_demo.gif" width="60%" />
 </p>
 
 ### scalar
 可以用于展示训练测试的误差趋势
 
 <p align="center">
-<img src="./images/scalar_demo.png" width="600"/>
+<img src="https://github.com/daming-lu/large_files/blob/master/loss_scalar.gif" width="60%"/>
 </p>
 
 ### image
 可以用于可视化任何tensor，或模型生成的图片
 
 <p align="center">
-<img src="./images/image_demo.png" width="600"/>
+<img src="https://github.com/daming-lu/large_files/blob/master/loss_image.gif" width="60%"/>
 </p>
 
 ### histogram
@@ -43,7 +47,7 @@ VisualDL 目前支持4种组件：
 用于可视化任何tensor中元素分布的变化趋势
 
 <p align="center">
-<img src="./images/histogram_demo.png" width="600"/>
+<img src="https://github.com/daming-lu/large_files/blob/master/histogram.gif" width="60%"/>
 </p>
 
 ## SDK
@@ -110,3 +114,9 @@ board 还支持一下参数来实现远程的访问：
 - `--host` 设定IP
 - `--port` 设定端口
 - `--model_pb` 指定 ONNX 格式的模型文件
+
+
+### 贡献
+
+VisualDL 是由 [PaddlePaddle](http://www.paddlepaddle.org/) 和 
+[ECharts](http://echarts.baidu.com/) 合作推出的开源项目。我们欢迎所有人使用，提意见以及贡献代码。 

README.md

-# VisualDL
+<p align="center">
+  <img src="https://github.com/daming-lu/large_files/blob/master/visualdl-readme.png" width="60%" />
+</p>
+
+# 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="https://github.com/daming-lu/large_files/blob/master/graph_demo.gif" width="60%" />
+</p>
+
+### Scalar
+Scalar can be used to show the trends of error during training.
+
+
+<p align="center">
+<img src="https://github.com/daming-lu/large_files/blob/master/loss_scalar.gif" width="60%"/>
+</p>
+
+### Image
+Image can be used to visualize any tensor or intermediate generated image.
+
+<p align="center">
+<img src="https://github.com/daming-lu/large_files/blob/master/loss_image.gif" width="60%"/>
+</p>
+
+### Histogram
+Histogram can be used to visualize parameter distribution and trends for any tensor.
+
+<p align="center">
+<img src="https://github.com/daming-lu/large_files/blob/master/histogram.gif" width="60%"/>
+</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
 
 
 ### How to install
@@ -13,3 +140,10 @@ vdl_scratch.py
 visualDL --logdir=scratch_log
 ```
 that will start a server locally.
+
+
+### Contribute
+
+VisualDL is initially created by [PaddlePaddle](http://www.paddlepaddle.org/) and 
+[ECharts](http://echarts.baidu.com/). 
+We welcome everyone to use, comment and contribute to Visual DL :)

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