Travis doc build (#232)

* Add travis-ci job to deploy docs to visualdl.paddlepaddle.org.  Update readme's to correctly show all images.

* Add SSH known hosts

* Add in additional restrictions for documentation deploy:
* Only deploy on merge to 'develop' branch

.travis.yml

@@ -16,6 +16,7 @@ os:
 env:
   - JOB=check_style
   - JOB=test
+  - JOB=build_doc
 
 addons:
   apt:
@@ -31,6 +32,7 @@ addons:
       - ccache
       - npm
       - nodejs
+  ssh_known_hosts: 52.76.173.135
 
 before_install:
   - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; sudo pip install pre-commit flake8; fi
@@ -41,6 +43,14 @@ before_install:
 script:
   - if [[ "$JOB" == "check_style" ]]; then ./travis/check_style.sh; fi
   - if [[ "$JOB" == "test" ]]; then /bin/bash ./tests.sh all; fi
+  - |
+    if [[ "$JOB" != "build_doc" ]]; then exit 0; fi;
+    if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit 0; fi;
+    if [[ "$TRAVIS_BRANCH" != "develop" ]]; then exit 0; fi;
+    export DEPLOY_DOCS_SH=https://raw.githubusercontent.com/PaddlePaddle/PaddlePaddle.org/master/scripts/deploy/deploy_docs.sh
+    export DOCS_DIR=`pwd`
+    cd ..
+    curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH $DOCS_DIR
 
 notifications:
   email:

README.cn.md

@@ -4,9 +4,10 @@
 [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
 
 <p align="center">
-  <img src="./docs/images/vs-logo.png" width="60%" />
+  <img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/images/vs-logo.png" width="60%" />
 </p>
 
+#
 ## 介绍
 VisualDL是一个面向深度学习任务设计的可视化工具，包含了scalar、参数分布、模型结构、图像可视化等功能，项目正处于高速迭代中，新的组件会不断加入。
 
@@ -28,21 +29,21 @@ 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="https://github.com/daming-lu/large_files/blob/master/graph_demo.gif" width="60%" />
+  <img src="https://raw.githubusercontent.com/daming-lu/large_files/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="https://github.com/daming-lu/large_files/blob/master/loss_scalar.gif" width="60%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/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="https://github.com/daming-lu/large_files/blob/master/loss_image.gif" width="60%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_image.gif" width="60%"/>
 </p>
 
 ### histogram
@@ -50,7 +51,7 @@ 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="https://github.com/daming-lu/large_files/blob/master/histogram.gif" width="60%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/histogram.gif" width="60%"/>
 </p>
 
 ## 快速尝试

README.md

@@ -4,9 +4,10 @@
 [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
 
 <p align="center">
-  <img src="./docs/images/vs-logo.png" width="60%" />
+  <img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/images/vs-logo.png" width="60%" />
 </p>
 
+#
 ## 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.
@@ -35,7 +36,7 @@ Cooperated with Python SDK, VisualDL can be compatible with most major DNN frame
 PaddlePaddle, PyTorch and MXNet.
 
 <p align="center">
-  <img src="https://github.com/daming-lu/large_files/blob/master/graph_demo.gif" width="60%" />
+  <img src="https://raw.githubusercontent.com/daming-lu/large_files/master/graph_demo.gif" width="60%" />
 </p>
 
 ### Scalar
@@ -43,21 +44,21 @@ 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%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/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%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/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%"/>
+<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/histogram.gif" width="60%"/>
 </p>
 
 ## Quick Start

docs/README.cn.md

+../README.cn.md
\ No newline at end of file

docs/README.md

-# 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="./introduction/graph.png" width="60%"/>
-</p>
-
-### scalar
-Show the error trend throughout the training.
-
-<p align="center">
-<img src="./introduction/scalar.png" width="60%"/>
-</p>
-
-### image
-To visualize any tensor, or model generated images
-
-<p align="center">
-<img src="./introduction/image-gan.png" width="60%"/>
-</p>
-
-### histogram
-To visualize the distribution of elements in any tensor
-
-<p align="center">
-<img src="./introduction/histogram.png" width="60%"/>
-</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;
-namespace cp = visualdl::components;
-
-int main() {
-  const std::string dir = "./tmp";
-  vs::LogWriter logger(dir, 10);
-
-  logger.SetMode("train");
-  auto tablet = logger.AddTablet("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

docs/README.md

+../README.md
\ No newline at end of file

docs/index_cn.rst

@@ -4,6 +4,7 @@ VisualDL
 .. toctree::
     :maxdepth: 1
 
+    首页<README.cn.md>
     quick_start_cn.md
     contribute_code_cn.md
     api/visualdl.rst
\ No newline at end of file

docs/quick_start_cn.md

@@ -57,7 +57,7 @@ visualDL --logdir ./random_log --port 8080
 之后用浏览器打开地址 `http://0.0.0.0:8080`，就可以看到scalar下的可视化结果
 
 <p align="center">
-<img src="./images/scratch_scalar.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/images/scratch_scalar.png"/>
 </p>
 
 ## scalar的C++ 示例
@@ -86,4 +86,6 @@ visualDL --logdir somedir --model_pb <path_to_model>
 
 比如mnist，会得到如下graph
 
-IMG
+<p align=center>
+    <img width="70%" src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/demo/mxnet/mxnet_graph.gif" />
+</p>

docs/quick_start_en.md

@@ -64,7 +64,7 @@ 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"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/images/scratch_scalar.png"/>
 </p>
 
 ## Scalar Demo in C++
@@ -95,5 +95,5 @@ 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" />
+<img width="70%" src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/demo/mxnet/mxnet_graph.gif" />
 </p>

requirements.txt

@@ -4,4 +4,5 @@ scipy==1.0.0
 Sphinx==1.6.6
 sphinx-rtd-theme==0.2.4
 flake8==3.5.0
+Pillow==5.0.0
 pre-commit==1.5.1