Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into aws-benchmark

.gitignore

@@ -25,12 +25,3 @@ third_party/
 
 # clion workspace.
 cmake-build-*
-
-# generated while compiling
-paddle/pybind/pybind.h
-CMakeFiles
-cmake_install.cmake
-paddle/.timestamp
-python/paddlepaddle.egg-info/
-paddle/fluid/pybind/pybind.h
-python/paddle/version.py

cmake/external/mklml.cmake

@@ -28,7 +28,7 @@ INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
 SET(MKLML_VER           "mklml_lnx_2018.0.1.20171007")
-SET(MKLML_URL           "https://github.com/01org/mkl-dnn/releases/download/v0.11/${MKLML_VER}.tgz")
+SET(MKLML_URL           "http://paddlepaddledeps.bj.bcebos.com/${MKLML_VER}.tgz")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")

cmake/external/snappystream.cmake

@@ -54,5 +54,7 @@ add_library(snappystream STATIC IMPORTED GLOBAL)
 set_property(TARGET snappystream PROPERTY IMPORTED_LOCATION
         "${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
 
-include_directories(${SNAPPYSTREAM_INCLUDE_DIR})
+include_directories(${SNAPPYSTREAM_INCLUDE_DIR}) # For snappysteam to include its own headers.
+include_directories(${THIRD_PARTY_PATH}/install) # For Paddle to include snappy stream headers.
+
 add_dependencies(snappystream extern_snappystream)

cmake/external/warpctc.cmake

@@ -62,7 +62,8 @@ ExternalProject_Add(
 )
 
 MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
-INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR})
+INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.
+INCLUDE_DIRECTORIES(${THIRD_PARTY_PATH}/install) # For Paddle code to include warpctc headers.
 
 ADD_LIBRARY(warpctc SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET warpctc PROPERTY IMPORTED_LOCATION ${WARPCTC_LIBRARIES})

cmake/external/zlib.cmake

@@ -25,7 +25,8 @@ ELSE(WIN32)
   SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
 ENDIF(WIN32)
 
-INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR})
+INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) # For zlib code to include its own headers.
+INCLUDE_DIRECTORIES(${THIRD_PARTY_PATH}/install) # For Paddle code to include zlib.h.
 
 ExternalProject_Add(
     extern_zlib

cmake/generic.cmake

@@ -244,14 +244,14 @@ function(cc_test TARGET_NAME)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_executable(${TARGET_NAME} ${cc_test_SRCS})
     # Support linking flags: --whole-archive (Linux) / -force_load (MacOS)
-    target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
+    target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main memory gtest gflags glog)
     if("${cc_test_DEPS}" MATCHES "ARCHIVE_START")
       list(REMOVE_ITEM cc_test_DEPS ARCHIVE_START ARCHIVE_END)
     endif()
-    add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
+    add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main memory gtest gflags glog)
     add_test(NAME ${TARGET_NAME}
              COMMAND ${TARGET_NAME} ${cc_test_ARGS}
-             WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+             WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
   endif()
 endfunction(cc_test)
 
@@ -311,8 +311,8 @@ function(nv_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS)
     cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS})
-    target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
-    add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
+    target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
+    add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
     add_test(${TARGET_NAME} ${TARGET_NAME})
   endif()
 endfunction(nv_test)
@@ -387,8 +387,8 @@ function(hip_test TARGET_NAME)
     endif()
     add_executable(${TARGET_NAME} ${_cmake_options} ${_generated_files} ${_sources})
     set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
-    target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
-    add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main memory gtest gflags)
+    add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main memory gtest gflags)
     add_test(${TARGET_NAME} ${TARGET_NAME})
   endif()
 endfunction(hip_test)
@@ -561,9 +561,9 @@ function(py_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS ARGS ENVS)
     cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
-             COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python ${py_test_ENVS}
+             COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
              ${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
-             WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+             WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
   endif()
 endfunction()
 

doc/design/file_manager/README.md

-# FileManager设计文档
-## 目标
-在本文档中，我们设计说明了名为FileManager系统，方便用户上传自己的训练数据以进行分布式训练
-
-主要功能包括：
-
-- 提供常用的命令行管理命令管理文件和目录
-- 支持大文件的断点上传、下载  
-
-## 名词解释
-- PFS：是`Paddlepaddle cloud File System`的缩写，是对用户文件存储空间的抽象，与之相对的是local filesystem。目前我们用CephFS来搭建。
-- [CephFS](http://docs.ceph.com/docs/master/cephfs/)：一个POSIX兼容的文件系统。
-- Chunk：逻辑划上文件分块的单位。
-
-## 模块
-### 架构图
-<image src=./src/filemanager.png width=900>
-
-### PFSClient
-- 功能： 详细设计[link](./pfs/pfsclient.md)
-	- 提供用户管理文件的命令
-	- 需要可以跨平台执行
-
-- 双向验证   
-	PFSClient需要和Ingress之间做双向验证<sup>[tls](#tls)</sup>，所以用户需要首先在`cloud.paddlepaddle.org`上注册一下，申请用户空间，并且把系统生成的CA(certificate authority)、Key、CRT(CA signed certificate)下载到本地，然后才能使用PFSClient。
-		
-### [Ingress](https://kubernetes.io/docs/concepts/services-networking/ingress/)
-- 功能：  
-	提供七层协议的反向代理、基于粘性会话的负载均衡功能。
-	
-- 透传用户身份的办法  
-	Ingress需要把PFSClient的身份信息传给PFSServer，配置的方法参考[link](http://www.integralist.co.uk/posts/clientcertauth.html#3)
-
-### PFSServer
-PFSServer提供RESTful API接口，接收处理PFSClient端的文件管理请求，并且把结果返回PFSClient端。
-
-RESTful API
-
-- /api/v1/files
-	- `GET /api/v1/files`: Get metadata of files or directories.
-	- `POST /api/v1/files`: Create files or directories.
-	- `PATCH /api/v1/files`: Update files or directories.
-	- `DELETE /api/v1/files`: Delete files or directories.
-
-- /api/v1/file/chunks
-	- `GET /api/v1/storage/file/chunks`: Get chunks's metadata of a file.
-
-- /api/v1/storage/files
-	- `GET /api/v1/storage/files`: Download files or directories.
-	- `POST /api/v1/storage/files`: Upload files or directories.
-
-- /api/v1/storage/file/chunks
-	- `GET /api/v1/storage/file/chunks`: Download chunks's data.
-	- `POST /api/v1/storage/file/chunks`: Upload chunks's data.
-
-## 文件传输优化
-
-### 分块文件传输
-用户文件可能是比较大的，上传到Cloud或者下载到本地的时间可能比较长，而且在传输的过程中也可能出现网络不稳定的情况。为了应对以上的问题，我们提出了Chunk的概念，一个Chunk由所在的文件偏移、数据、数据长度及校验值组成。文件的上传和下载都是通过对Chunk的操作来实现的。由于Chunk比较小（默认256K），完成一个传输动作完成的时间也比较短，不容易出错。PFSClient需要在传输完毕最后一个Chunk的时候检查destination文件的MD5值是否和source文件一致。
-
-一个典型的Chunk如下所示：
-
-```
-type Chunk struct {
-	fileOffset int64
-	checksum uint32
-	len     uint32
-	data    []byte
-}
-```  
-
-### 生成sparse文件
-当destination文件不存在或者大小和source文件不一致时，可以用[Fallocate](https://Go.org/pkg/syscall/#Fallocate)生成sparse文件，然后就可以并发写入多个Chunk。
-
-### 覆盖不一致的部分
-文件传输的的关键在于需要PFSClient端对比source和destination的文件Chunks的checksum是否保持一致，不一致的由PFSClient下载或者传输Chunk完成。这样已经传输成功的部分就不用重新传输了。
-
-## 用户使用流程
-参考[link](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/data_dispatch.md)
-
-## 框架生成
-用[swagger](https://github.com/swagger-api/swagger-codegen)生成PFSClient和PFSServer的框架部分，以便我们可以把更多的精力放到逻辑本身上。
-
-## 参考文档
-- <a name=tls></a>[TLS complete guide](https://github.com/k8sp/tls/blob/master/tls.md)
-- [aws.s3](http://docs.aws.amazon.com/cli/latest/reference/s3/)
-- [linux man document](https://linux.die.net/man/)

doc/design/file_manager/pfs/pfsclient.md

-# PFSClient
-
-## Description
-The `pfs` command is a Command Line Interface to manage your files on PaddlePaddle Cloud
-
-## Synopsis
-```
-paddle [options] pfs <subcommand> [parameters]
-```
-
-## Options
-```
---profile (string)
-	Use a specific profile from your credential file.
-
---help (string)
-	Display more information about command
-
---version
-	Output version information and exit
-
---debug
-	Show detailed debugging log	
-	
---only-show-errors (boolean) 
-	Only errors and warnings are displayed. All other output is suppressed.
-```
-
-## Path Arguments
-When using a command, we need to specify path arguments. There are two path argument type: `localpath` and `pfspath`.  
-
-A `pfspath` begin with `/pfs`, eg: `/pfs/$DATACENTER/home/$USER/folder`.
-
-[Here](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/data_dispatch.md#上传训练文件) is how to config datacenters.
-
-## order of Path Arguments
-Commonly, if there are two path arguments, the first is the source, and the second is the destination.
-
-## Subcommonds
-- rm - remove files or directories
-
-```
-Synopsis:
-	rm [-r] [-v] <PFSPath> ...
-
-Options:
-	-r 
-		Remove directories and their contents recursively 
-	-v      
-		Cause rm to be verbose, showing files after they are removed.
-	
-Examples:
-	paddle pfs rm /pfs/$DATACENTER/home/$USER/file
-	paddle pfs rm -r /pfs/$DATACENTER/home/$USER/folder
-```
-- mv - move (rename) files
-
-```
-Synopsis:
-	mv [-f | -n] [-v] <LocalPath> <PFSPath>
-	mv [-f | -n] [-v] <LocalPath> ... <PFSPath>
-	mv [-f | -n] [-v] <PFSPath> <LocalPath> 
-	mv [-f | -n] [-v] <PFSPath> ... <LocalPath> 
-	mv [-f | -n] [-v] <PFSPath> <PFSPath> 
-	mv [-f | -n] [-v] <PFSPath> ... <PFSPath> 
-	
-Options:
-	-f      
-		Do not prompt for confirmation before overwriting the destination path.  (The -f option overrides previous -n options.)
-	-n      
-		Do not overwrite an existing file.  (The -n option overrides previous -f options.)
-	-v      
-		Cause mv to be verbose, showing files after they are moved.
-		
-Examples:
-	paddle pfs mv ./text1.txt /pfs/$DATACENTER/home/$USER/text1.txt
-```
-- cp - copy files or directories
-
-```
-Synopsis:
-	cp [-r] [-f | -n] [-v] [--preserve--links] <LocalPath> <PFSPath>
-	cp [-r] [-f | -n] [-v] [--preserve--links] <LocalPath> ... <PFSPath>
-	cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> <LocalPath> 
-	cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> ... <LocalPath>
-	cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> <PFSPath> 
-	cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> ... <PFSPath>
-
-Options:
-	-r
-   		Copy directories recursively
-   	-f      
-		Do not prompt for confirmation before overwriting the destination path.  (The -f option overrides previous -n options.)
-	-n      
-		Do not overwrite an existing file.  (The -n option overrides previous -f options.)
-	-v      
-		Cause cp to be verbose, showing files after they are copied.
-	--preserve--links
-	   Reserve links when copy links
-	   
-Examples:
-	paddle pfs cp ./file /pfs/$DATACENTER/home/$USER/file
-	paddle pfs cp /pfs/$DATACENTER/home/$USER/file ./file
-```
-- ls- list files
-
-```
-Synopsis:
-	ls [-r] <PFSPath> ...
-	
-Options:
-	-R
-   		List directory(ies) recursively
-
-Examples:
-	paddle pfs ls  /pfs/$DATACENTER/home/$USER/file
-	paddle pfs ls  /pfs/$DATACENTER/home/$USER/folder
-```
-
-- mkdir - mkdir directory(ies)
-Create intermediate directory(ies) as required.
-
-```
-Synopsis:
-	mkdir <PFSPath> ...
-
-Examples:
-	paddle pfs mkdir  /pfs/$DATACENTER/home/$USER/folder
-```

doc/fluid/CMakeLists.txt

@@ -27,7 +27,7 @@ sphinx_add_target(paddle_fluid_docs
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_EN})
 
-add_dependencies(paddle_fluid_docs gen_proto_py)
+add_dependencies(paddle_fluid_docs gen_proto_py paddle_python)
 
 # configured documentation tools and intermediate build results
 set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
@@ -50,6 +50,6 @@ sphinx_add_target(paddle_fluid_docs_cn
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_CN})
 
-add_dependencies(paddle_fluid_docs_cn gen_proto_py)
+add_dependencies(paddle_fluid_docs_cn gen_proto_py paddle_python)
 
 add_subdirectory(api)

doc/fluid/api/CMakeLists.txt

@@ -19,4 +19,4 @@ sphinx_add_target(paddle_fluid_apis
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_EN})
 
-add_dependencies(paddle_fluid_apis  gen_proto_py framework_py_proto copy_paddle_pybind)
+add_dependencies(paddle_fluid_apis  gen_proto_py framework_py_proto copy_paddle_pybind paddle_python)

doc/fluid/design/algorithm/parameter_average.md

@@ -5,9 +5,11 @@ In a large scale machine learning setup where the size of the training data is h
 
 Polyak and Juditsky (1992) showed that the test performance of simple average of parameters obtained by Stochastic Gradient Descent (SGD) is as good as that of parameter values that are obtained by training the model over and over again, over the training dataset.
 
-Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="./images/theta_star.gif"/><br/> . The averaging is done as follows:
+Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/theta_star.gif"/><br/> . The averaging is done as follows:
 
-![](./images/asgd.gif)
+<p align="center">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/asgd.gif"><br />
+</p>
 
 We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above.
 

doc/fluid/design/concurrent/channel.md

@@ -114,13 +114,13 @@ current thread under two conditions:
 #### Channel Send
 
 <p align="center">
-<img src="./images/channel_send.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_send.png"/><br/>
 </p>
 
 #### Channel Receive
 
 <p align="center">
-<img src="./images/channel_recv.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_recv.png"/><br/>
 </p>
 
 ## Limitations and Considerations

doc/fluid/design/concurrent/concurrent_programming.md

@@ -23,21 +23,25 @@ The following table compares concepts in Fluid and Go
 <td>user-defined functions </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid">layers</a></td>
+<td></td>
 </tr>
 <tr>
 <td>control-flow and built-in functions </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">intrinsics/operators</a></td>
+<td></td>
 </tr>
 <tr>
 <td>goroutines, channels </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h">class ThreadPool</a></td>
+<td></td>
 </tr>
 <tr>
 <td>runtime </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h">class Executor</a></td>
+<td></td>
 </tr>
 </tbody>
 </table>

doc/fluid/design/concurrent/select_op.md

@@ -254,7 +254,7 @@ only one case will be executed.
 ### select_op flow
 
 <p align="center">
-<img src="./images/select_op_workflow.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/select_op_workflow.png"/><br/>
 </p>
 
 The select algorithm is inspired by golang's select routine.  Please refer to

doc/fluid/design/dist_train/distributed_architecture.md

@@ -40,11 +40,11 @@ computation is only specified in Python code which sits outside of PaddlePaddle,
 
 Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows:
 
-<img src="src/compiler.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/compiler.png"/>
 
 PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component:
 
-<img src="src/paddle-compile.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/paddle-compile.png"/>
 
 The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation.
 
@@ -60,7 +60,7 @@ For a detailed explanation, refer to this document -
 
 The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:
 
-<img src="src/distributed_architecture.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/distributed_architecture.png"/>
 
 The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*.
 
@@ -152,7 +152,7 @@ for data in train_reader():
 `JobDesc` object describe the distributed job resource specification to run on
 Cluster environment.
 
-<img src="src/remote_executor.png" width="500" align="center" />
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/remote_executor.png" width="500" align="center" />
 
 `RemoteExecutor.run` sends the `ProgramDesc` and
 [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/unreleased-tpr/doc/autoscale/README.md#training-job-resource)
@@ -171,7 +171,7 @@ In the future, a more general placement algorithm should be implemented, which m
 
 The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:
 
-<img src="src/local_architecture.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local_architecture.png"/>
 
 
 ### Training Data

doc/fluid/design/dist_train/multi_cpu.md

@@ -8,11 +8,11 @@ Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
 
 ## Transpiler
 
-<img src="src/multi-threads/single-thread@3x.png" width="300">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/single-thread@3x.png" width="300">
 
 After converted:
 
-<img src="src/multi-threads/multi-threads@3x.png" width="1000">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/multi-threads@3x.png" width="1000">
 
 ## Implement
 

doc/fluid/design/dist_train/parameter_server.md

@@ -41,11 +41,11 @@ We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
 Below is an example of converting the user defined graph to the
 subgraphs for the trainer and the parameter server:
 
-<img src="src/local-graph.png" width="300"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local-graph.png" width="300"/>
 
 After converting:
 
-<img src="src/dist-graph.png" width="700"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dist-graph.png" width="700"/>
 
 1. The parameter variable W and its optimizer program are placed on the parameter server.
 1. Operators are added to the program.
@@ -69,8 +69,7 @@ In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list o
 non-zero gradient data. So when we do parameter optimization both locally and remotely,
 we only need to send those non-zero rows to the optimizer operators:
 
-<img src="src/sparse_update.png" width="700" />
-
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/sparse_update.png" width="700" />
 ### Benefits
 
 - Model parallelism becomes easier to implement: it is an extension to

doc/fluid/design/dynamic_rnn/rnn.md

@@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i
 ## RNN Algorithm Implementation
 
 <p align="center">
-<img src="./rnn.jpg"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/rnn.jpg"/>
 </p>
 
 The above diagram shows an RNN unrolled into a full network.
@@ -22,7 +22,7 @@ There are several important concepts here:
 There could be local variables defined in each step-net.  PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.
 
 <p align="center">
-<img src="./rnn.png"/><br/>
+<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/rnn.png"/><br/>
 Figure 2 illustrates the RNN's data flow
 </p>
 
@@ -93,7 +93,7 @@ For example, we could have a 2-level RNN, where the top level corresponds to par
 The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.
 
 <p align="center">
-<img src="./2_level_rnn.png"/>
+<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/2_level_rnn.png"/>
 </p>
 
 ```python
@@ -149,5 +149,5 @@ If the `output_all_steps` is set to False, it will only output the final time st
 
 
 <p align="center">
-<img src="./rnn_2level_data.png"/>
+<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/rnn_2level_data.png"/>
 </p>

doc/fluid/design/modules/batch_norm_op.md

@@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu
 
 The following graph showes the training computational process of `batch_norm_op`:
 
-<img src="../images/batch_norm_op_kernel.png" width="800"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_op_kernel.png" width="800"/>
 
 cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel.
 
@@ -124,7 +124,7 @@ for pass_id in range(PASS_NUM):
 `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
 
 <div align=center>
-<img src="../images/batch_norm_fork.png" width="500"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_fork.png" width="500"/>
 </div>
 
 Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate.

doc/fluid/design/modules/regularization.md

@@ -6,17 +6,17 @@ A central problem in machine learning is how to design an algorithm that will pe
 ### Parameter Norm Penalties
 Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows:
 
-<img src="./images/loss_equation.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/loss_equation.png" align="center"/><br/>
 
 The parameter `alpha` is a hyperparameter that weights the relative contribution of the norm penalty term, `omega`, relative to the standard objective function `J`.
 
 The most commonly used norm penalties are the L2 norm penalty and the L1 norm penalty. These are given as follows:
 
 ##### L2 Regularization:
-<img src="./images/l2_regularization.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l2_regularization.png" align="center"/><br/>
 
 ##### L1 Regularization
-<img src="./images/l1_regularization.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l1_regularization.png" align="center"/><br/>
 
 A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html).
 
@@ -40,11 +40,11 @@ The idea of building ops for regularization is in sync with the refactored Paddl
 
 Below is an example of a really simple feed forward neural network.
 
-<img src="./images/feed_forward.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward.png" align="center"/><br/>
 
 The Python API will modify this computation graph to add regularization operators. The modified computation graph will look as follows:
 
-<img src="./images/feed_forward_regularized.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward_regularized.png" align="center"/><br/>
    
 ### Python API implementation for Regularization
 
@@ -64,9 +64,3 @@ Since we want to create the regularization ops in a lazy manner, the regularizat
 #### High-level API
 
 In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we also need to provide regularization functionality in layer functions. The design of these APIs can be postponed for later right now. A good reference for these APIs can be found in [Keras](https://keras.io/regularizers/) and also by looking at Tensorflow in [`tf.contrib.layers`](https://www.tensorflow.org/api_guides/python/contrib.layers).
-
-
-
-
-
-    

doc/fluid/design/network/deep_speech_2.md

@@ -116,7 +116,7 @@ The classical DS2 network contains 15 layers (from bottom to top):
 - **One** CTC-loss layer
 
 <div align="center">
-<img src="images/ds2_network.png" width=350><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ds2_network.png" width=350><br/>
 Figure 1. Archetecture of Deep Speech 2 Network.
 </div>
 
@@ -208,7 +208,7 @@ TODO by Assignees
 ### Beam Search with CTC and LM
 
 <div align="center">
-<img src="images/beam_search.png" width=600><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/beam_search.png" width=600><br/>
 Figure 2. Algorithm for CTC Beam Search Decoder.
 </div>
 

doc/fluid/design/network/sequence_decoder.md

@@ -199,7 +199,7 @@ Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail i
 
 ## LoD and shape changes during decoding
 <p align="center">
-  <img src="./images/LOD-and-shape-changes-during-decoding.jpg"/>
+  <img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/LOD-and-shape-changes-during-decoding.jpg"/>
 </p>
 
 According to the image above, the only phase that changes the LoD is beam search.

doc/fluid/design/others/gan_api.md

@@ -7,14 +7,14 @@ It applies several important concepts in machine learning system design, includi
 In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.
 
 <p align="center">
-<img src="./test.dot.png" width = "35%" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/test.dot.png" width = "35%" align="center"/><br/>
 Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.
 </p>
 
 The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.
 
 <p align="center">
-<img src="./dcgan.png" width = "90%" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dcgan.png" width = "90%" align="center"/><br/>
 Figure 2. Photo borrowed from the original DC-GAN paper.
 </p>
 

doc/fluid/dev/index_cn.rst

@@ -9,5 +9,5 @@
   use_eigen_cn.md
   name_convention.md
   support_new_device.md
-  releasing_process.md
+  releasing_process_cn.md
   op_markdown_format.md

doc/fluid/dev/index_en.rst

@@ -9,5 +9,5 @@ Development
   use_eigen_en.md
   name_convention.md
   support_new_device.md
-  releasing_process.md
+  releasing_process_en.md
   op_markdown_format.md

doc/fluid/dev/releasing_process.md → doc/fluid/dev/releasing_process_cn.md

@@ -10,19 +10,10 @@ PaddlePaddle每次发新的版本，遵循以下流程:
   * 使用Regression Test List作为检查列表，测试本次release的正确性。
 	  * 如果失败，记录下所有失败的例子，在这个`release/版本号`分支中，修复所有bug后，Patch号加一，到第二步
 	* 修改`python/setup.py.in`中的版本信息,并将`istaged`字段设为`True`。
-	* 编译这个版本的python wheel包，并发布到pypi。
-		* 由于pypi.python.org目前遵循[严格的命名规范PEP 513](https://www.python.org/dev/peps/pep-0513)，在使用twine上传之前，需要重命名wheel包中platform相关的后缀，比如将`linux_x86_64`修改成`manylinux1_x86_64`。
-		* pypi上的package名称为paddlepaddle和paddlepaddle_gpu，如果要上传GPU版本的包，需要修改build/python/setup.py中，name: "paddlepaddle_gpu"并重新打包wheel包：`python setup.py bdist_wheel`。
-		* 上传方法：
-			```
-			cd build/python
-			pip install twine
-			twine upload dist/[package to upload]
-			```
-		* 编译这个版本的Docker发行镜像，发布到dockerhub。如果失败，修复Docker编译镜像问题，Patch号加一，返回第二步
-1. 第三步完成后，将`release/版本号`分支合入master分支，并删除`release/版本号`分支。将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。最后删除`release/版本号`分支。
-1. 协同完成Release Note的书写
-
+	* 将这个版本的python wheel包发布到pypi。
+	* 更新Docker镜像（参考后面的操作细节）。
+1. 第三步完成后，将`release/版本号`分支合入master分支，将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。
+1. 协同完成Release Note的书写。
 
 需要注意的是:
 
@@ -31,13 +22,18 @@ PaddlePaddle每次发新的版本，遵循以下流程:
 
 ## 发布wheel包到pypi
 
-使用[PaddlePaddle CI](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
+1. 使用[PaddlePaddle CI](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
 完成自动化二进制编译，参考下图，选择需要发布的版本（通常包含一个CPU版本和一个GPU版本），点击"run"右侧的"..."按钮，可以
-弹出下面的选择框，在第二个tab (Changes)里选择需要发布的分支，这里选择0.11.0，然后点击"Run Build"按钮。等待编译完成后
-可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。然后按照上述的方法
-使用`twine`工具上传即可。
-
-<img src="ci_build_whl.png">
+弹出下面的选择框，在第二个tab (Changes)里选择需要发布的分支，这里选择0.11.0，然后点击"Run Build"按钮。
+	<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ci_build_whl.png">
+1. 等待编译完成后可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。
+1. 由于pypi.python.org目前遵循[严格的命名规范PEP 513](https://www.python.org/dev/peps/pep-0513)，在使用twine上传之前，需要重命名wheel包中platform相关的后缀，比如将`linux_x86_64`修改成`manylinux1_x86_64`。
+1. 上传：
+```
+cd build/python
+pip install twine
+twine upload dist/[package to upload]
+```
 
 * 注：CI环境使用 https://github.com/PaddlePaddle/buildtools 这里的DockerImage作为编译环境以支持更多的Linux
   发型版，如果需要手动编译，也可以使用这些镜像。这些镜像也可以从 https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/ 下载得到。
@@ -48,10 +44,20 @@ PaddlePaddle每次发新的版本，遵循以下流程:
 上述PaddlePaddle CI编译wheel完成后会自动将Docker镜像push到DockerHub，所以，发布Docker镜像只需要对自动push的镜像打上
 版本号对应的tag即可：
 
-1. 进入 https://hub.docker.com/r/paddlepaddle/paddle/tags/ 查看latest tag的更新时间是否在上述编译wheel包完成后是否最新。
-1. 执行 `docker pull paddlepaddle/paddle:[latest tag]`，latest tag可以是latest或latest-gpu等。
-1. 执行 `docker tag paddlepaddle/paddle:[latest tag] paddlepaddle/paddle:[version]`
-1. 执行 `docker push paddlepaddle/paddle:[version]`
+```
+docker pull [镜像]:latest
+docker tag [镜像]:latest [镜像]:[version]
+docker push [镜像]:[version]
+```
+
+需要更新的镜像tag包括：
+
+* `[version]`: CPU版本
+* `[version]-openblas`: openblas版本
+* `[version]-gpu`: GPU版本（CUDA 8.0 cudnn 5）
+* `[version]-gpu-[cudaver]-[cudnnver]`: 不同cuda, cudnn版本的镜像
+
+之后可进入 https://hub.docker.com/r/paddlepaddle/paddle/tags/ 查看是否发布成功。
 
 ## PaddlePaddle 分支规范
 
@@ -76,7 +82,7 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-
 
 ### PaddlePaddle Book中所有章节
 
-PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
+PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练（V2和Fluid）模型正确性。
 
 <table>
 <thead>

doc/fluid/dev/releasing_process_en.md

+# PaddlePaddle Releasing Process
+
+PaddlePaddle manages its branches using "git-flow branching model", and [Semantic Versioning](http://semver.org/) as it's version number semantics.
+
+Each time we release a new PaddlePaddle version, we should follow the below steps:
+
+1. Fork a new branch from `develop` named `release/[version]`, e.g. `release/0.10.0`.
+1. Push a new tag on the release branch, the tag name should be like `[version]rc.patch`. The
+   first tag should be `0.10.0rc1`, and the second should be `0.10.0.rc2` and so on.
+1. After that, we should do:
+  * Run all regression test on the Regression Test List (see PaddlePaddle TeamCity CI), to confirm
+      that this release has no major bugs.
+        * If regression test fails, we must fix those bugs and create a new `release/[version]`
+          branch from previous release branch.
+    * Modify `python/setup.py.in`, change the version number and change `ISTAGED` to `True`.
+    * Publish PaddlePaddle release wheel packages to pypi (see below instructions for detail).
+    * Update the Docker images (see below instructions for detail).
+1. After above step, merge `release/[version]` branch to master and push a tag on the master commit,
+   then merge `master` to `develop`.
+1. Update the Release Note.          
+
+***NOTE:***
+
+* Do ***NOT*** merge commits from develop branch to release branches to keep the release branch contain
+  features only for current release, so that we can test on that version.
+* If we want to fix bugs on release branches, we must merge the fix to master, develop and release branch.
+
+## Publish Wheel Packages to pypi
+
+1. Use our [CI tool](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
+   to build all wheel packages needed to publish. As shown in the following picture, choose a build
+     version, click "..." button on the right side of "Run" button, and switch to the second tab in the
+pop-up box, choose the current release branch and click "Run Build" button. You may repeat this
+     step to start different versions of builds.
+    <img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ci_build_whl.png">
+1. After the build succeeds, download the outputs under "Artifacts" including capi, `cp27m` and `cp27mu`.
+1. Since pypi.python.org follows [PEP 513](https://www.python.org/dev/peps/pep-0513), before we
+     upload the package using `twine`, we need to rename the package from `linux_x86_64` to
+     `manylinux1_x86_64`.
+1. Start the upload:
+     ```
+     cd build/python
+     pip install twine
+     twine upload dist/[package to upload]
+     ```
+
+* NOTE: We use a special Docker image to build our releases to support more Linux distributions, you can
+  download it from https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/, or build it using
+    scripts under `tools/manylinux1`.
+* pypi does not allow overwrite the already uploaded version of wheel package, even if you delete the
+  old version. you must change the version number before upload a new one.
+
+## Publish Docker Images
+
+Our CI tool will push latest images to DockerHub, so we only need to push a version tag like:
+
+```
+docker pull [image]:latest
+docker tag [image]:latest [image]:[version]
+docker push [image]:[version]
+```
+
+Tags that need to be updated are:
+* `[version]`: CPU only version image
+* `[version]-openblas`: openblas version image
+* `[version]-gpu`: GPU version（using CUDA 8.0 cudnn 5）
+* `[version]-gpu-[cudaver]-[cudnnver]`: tag for different cuda, cudnn versions
+
+You can then checkout the latest pushed tags at https://hub.docker.com/r/paddlepaddle/paddle/tags/.
+
+## Branching Model
+
+We use [git-flow](http://nvie.com/posts/a-successful-git-branching-model/) as our branching model,
+with some modifications:
+
+* `master` branch is the stable branch. Each version on the master branch is tested and guaranteed.
+* `develop` branch is for development. Each commit on develop branch has passed CI unit test, but no
+  regression tests are run.
+* `release/[version]` branch is used to publish each release. Latest release version branches have
+  bugfix only for that version, but no feature updates.
+* Developer forks are not required to follow
+  [git-flow](http://nvie.com/posts/a-successful-git-branching-model/)
+  branching model, all forks is like a feature branch.
+    * Advise: developer fork's develop branch is used to sync up with main repo's develop branch.
+    * Advise: developer use it's fork's develop branch to for new branch to start developing.
+  * Use that branch on developer's fork to create pull requests and start reviews.
+      * developer can push new commits to that branch when the pull request is open.
+* Bug fixes are also started from developers forked repo. And, bug fixes branch can merge to
+  `master`, `develop` and `releases`.
+
+## PaddlePaddle Regression Test List
+
+### All Chapters of PaddlePaddle Book
+
+We need to guarantee that all the chapters of PaddlePaddle Book can run correctly. Including
+V1 (`paddle_trainer` training) and V2 training and Fluid training.
+
+<table>
+<thead>
+<tr>
+<th></th>
+<th>Linear Regression</th>
+<th>Recognize Digits</th>
+<th>Image Classification</th>
+<th>Word2Vec</th>
+<th>Personalized Recommendation</th>
+<th>Sentiment Analysis</th>
+<th>Semantic Role Labeling</th>
+<th>Machine Translation</th>
+</tr>
+</thead>
+
+<tbody>
+<tr>
+<td>API.V2 + Docker + GPU </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td> API.V2 + Docker + CPU </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td>`paddle_trainer` + Docker + GPU </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td>`paddle_trainer` + Docker + CPU </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td> API.V2 + Ubuntu + GPU</td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td>API.V2 + Ubuntu + CPU </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td> `paddle_trainer` + Ubuntu + GPU</td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+
+<tr>
+<td> `paddle_trainer` + Ubuntu + CPU</td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+<td>  </td>
+<td> </td>
+</tr>
+</tbody>
+</table>

doc/fluid/howto/performance/profiler.md

@@ -23,7 +23,7 @@ But how to record the time for the mixed C++ and CUDA program?  There many C++ A
 
 The overall flow is shown as the following figure.
 
-<img src="./images/profiler.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/profiler.png" align="center"/><br/>
 
 ### Event
 

doc/fluid/images/2_level_rnn.dot

+digraph G {
+
+  rnn [label="1st level RNN" shape=box]
+
+  subgraph cluster0 {
+    label = "time step 0"
+
+    sent0 [label="sentence"]
+    sent1 [label="sentence"]
+
+    rnn1 [label="2nd level RNN" shape=box]
+
+    sent0 -> rnn1
+    sent1 -> rnn1
+  }
+
+  subgraph cluster1 {
+    label = "time step 1"
+
+    sent2 [label="sentence"]
+    sent3 [label="sentence"]
+
+    rnn2 [label="2nd level RNN" shape=box]
+
+    sent2 -> rnn2
+    sent3 -> rnn2
+  }
+
+  subgraph cluster2 {
+    label = "time step 2"
+
+    sent4 [label="sentence"]
+    sent5 [label="sentence"]
+
+    rnn3 [label="2nd level RNN" shape=box]
+
+    sent4 -> rnn3
+    sent5 -> rnn3
+  }
+
+
+  para0 [label="paragraph info 0"]
+  para1 [label="paragraph info 1"]
+  para2 [label="paragraph info 2"]
+
+  rnn1 -> para0
+  rnn2 -> para1
+  rnn3 -> para2
+
+  para0 -> rnn
+  para1 -> rnn
+  para2 -> rnn
+
+  chapter [label="chapter info"]
+  rnn -> chapter
+}

doc/fluid/images/batch_norm_fork.dot

+digraph ImageBatchNormForkGragh {
+  subgraph cluster_before {
+    Prev [label="...", shape=plaintext];
+    Rnn [label="rnn_op", shape=box];
+    BatchNorm [label="batch_norm_op", shape=box];
+    Fc [label="fc_op", shape=box];
+    After [label="...", shape=plaintext];
+    Prev -> Rnn -> BatchNorm -> Fc -> After;
+    label="original";
+  }
+
+  subgraph cluster_after {
+    Prev2 [label="...", shape=plaintext];
+    Rnn2 [label="rnn_op", shape=box];
+    BatchNorm2_1 [label="train_batch_norm_op", shape=box];
+    BatchNorm2_2 [label="infer_batch_norm_op", shape=box];
+    Fc2_1 [label="fc_op", shape=box];
+    Fc2_2 [label="fc_op", shape=box];
+    After2_1 [label="...", shape=plaintext];
+    After2_2 [label="...", shape=plaintext];
+    Prev2 -> Rnn2 -> BatchNorm2_1 -> Fc2_1 -> After2_1;
+    Rnn2 -> BatchNorm2_2 ->Fc2_2 ->After2_2
+    label="forked";
+  }
+}

doc/fluid/images/graph_construction_example.bash

+cat ./graph_construction_example.dot | \
+    sed 's/color=red/color=red, style=invis/g' | \
+    sed 's/color=green/color=green, style=invis/g' | \
+    dot -Tpng > graph_construction_example_forward_only.png
+
+cat ./graph_construction_example.dot | \
+    sed 's/color=green/color=green, style=invis/g' | \
+    dot -Tpng > graph_construction_example_forward_backward.png
+
+cat ./graph_construction_example.dot | \
+    dot -Tpng > graph_construction_example_all.png

doc/fluid/images/graph_construction_example.dot

+digraph ImageClassificationGraph {
+        ///////// The forward part /////////
+        FeedX [label="Feed", color=blue, shape=box];
+        FeedY [label="Feed", color=blue, shape=box];
+        InitW [label="Init", color=blue, shape=diamond];
+        Initb [label="Init", color=blue, shape=diamond];
+        FC [label="FC", color=blue, shape=box];
+        MSE [label="MSE", color=blue, shape=box];
+
+        x [label="x", color=blue, shape=oval];
+        l [label="l", color=blue, shape=oval];
+        y [label="y", color=blue, shape=oval];
+        W [label="W", color=blue, shape=doublecircle];
+        b [label="b", color=blue, shape=doublecircle];
+        cost [label="cost", color=blue, shape=oval];
+
+        FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
+        FeedY -> l [color=blue];
+        InitW -> W [color=blue];
+        Initb -> b [color=blue];
+        W -> FC [color=blue];
+        b -> FC [color=blue];
+        l -> MSE [color=blue];
+
+        ////////// The backward part /////////
+        MSE_Grad [label="MSE_grad", color=red, shape=box];
+        FC_Grad [label="FC_grad", color=red, shape=box];
+
+        d_cost [label="d cost", color=red, shape=oval];
+        d_y [label="d y", color=red, shape=oval];
+        d_b [label="d b", color=red, shape=oval];
+        d_W [label="d W", color=red, shape=oval];
+
+        cost -> MSE_Grad [color=red];
+        d_cost -> MSE_Grad [color=red];
+        l -> MSE_Grad [color=red];
+        y -> MSE_Grad -> d_y [color=red];
+
+        x -> FC_Grad [color=red];
+        y -> FC_Grad [color=red];
+        d_y -> FC_Grad [color=red];
+        W -> FC_Grad -> d_W [color=red];
+        b -> FC_Grad -> d_b [color=red];
+
+        ////////// The optimizaiton part //////////
+
+        OPT_W [label="SGD", color=green, shape=box];
+        OPT_b [label="SGD", color=green, shape=box];
+
+        W -> OPT_W [color=green];
+        b -> OPT_b [color=green];
+        d_W -> OPT_W -> W [color=green];
+        d_b -> OPT_b -> b [color=green];
+
+        ////////// Groupings //////////
+
+        subgraph clusterMSE {
+                style=invis;
+                MSE;
+                MSE_Grad;
+        }
+
+        subgraph clusterFC {
+                style=invis;
+                FC;
+                FC_Grad;
+        }
+}

doc/fluid/images/rnn.dot

+digraph G {
+  label = "simple RNN implementation" 
+
+  ranksep=2;
+
+  //graph [nodesep=1, ranksep=1];
+
+  node[nodesep=1]
+
+  subgraph cluster0 {
+    label = "global scope"
+    rankdir = TB
+    W
+    boot_memory
+    input
+    output
+  }
+
+  subgraph cluster1 {
+    label = "step-scope 0"
+    rankdir = TB
+    memory0[label="memory"]
+    prememory0[label="pre-memory"]
+    step_input0[label="step input"]
+    step_output0[label="step output"]
+  }
+
+  subgraph cluster2 {
+    label = "step-scope 1"
+    rankdir = TB
+    memory1[label="memory"]
+    prememory1[label="pre-memory"]
+    step_input1[label="step input"]
+    step_output1[label="step output"]
+  }
+
+  subgraph cluster3 {
+    label = "step-scope 2"
+    rankdir = TB
+    memory2[label="memory"]
+    prememory2[label="pre-memory"]
+    step_input2[label="step input"]
+    step_output2[label="step output"]
+  }
+
+  stepnet [shape=box]
+  stepnet0 [shape=box, style=dashed]
+  stepnet1 [shape=box, style=dashed]
+  stepnet2 [shape=box, style=dashed]
+
+
+  edge[color=blue]
+  boot_memory -> prememory0 [label="init" color="blue"]
+  memory0 -> prememory1  [label="copy/reference" color="blue"]
+  memory1 -> prememory2 [label="copy/reference" color="blue"]
+
+  edge[color=black]
+  W -> stepnet0[constraint=false, style=dashed]
+  W -> stepnet1[constraint=false, style=dashed]
+  W -> stepnet2[constraint=false, style=dashed]
+
+  memory0 -> stepnet0[style=dashed]
+  prememory0 -> stepnet0 -> step_output0[style=dashed]
+
+  memory1 -> stepnet1[style=dashed]
+  prememory1 -> stepnet1 -> step_output1[style=dashed]
+
+  memory2 -> stepnet2[style=dashed]
+  prememory2 -> stepnet2 -> step_output2[style=dashed]
+
+  input -> step_input0
+  input -> step_input1
+  input -> step_input2
+
+  step_input0 -> stepnet0 [style=dashed]
+  step_input1 -> stepnet1[style=dashed]
+  step_input2 -> stepnet2[style=dashed]
+
+  step_output0 -> output
+  step_output1 -> output
+  step_output2 -> output
+
+  stepnet0 -> stepnet[style=dashed]
+  stepnet1 -> stepnet[style=dashed]
+  stepnet2 -> stepnet[style=dashed]
+
+}

doc/fluid/images/rnn_2level_data.dot

+digraph G {
+  chapter [label="chapter"]
+
+  subgraph cluster0 {
+    label = "paragraph 0"
+
+    top_rnn0[label="top rnn step 0" shape=box]
+
+    p0 [label="paragraph 0"]
+    p1 [label="paragraph 1"]
+  }
+
+  subgraph cluster1{
+    label = "paragraph 1"
+
+    top_rnn1[label="top rnn step 1" shape=box]
+
+    p2 [label="paragraph 0"]
+    p3 [label="paragraph 1"]
+  }
+
+  subgraph cluster_p0 {
+    label = "sentence 0"
+
+    low_rnn0 [label="low rnn step 0" shape=box]
+    s00 [label="sentence 0"]
+    s01 [label="sentence 1"]
+
+    low_rnn0 -> s00
+    low_rnn0 -> s01
+  }
+
+  subgraph cluster_p1 {
+    label = "sentence 1"
+    low_rnn1 [label="low rnn step 1" shape=box]
+    s10 [label="sentence 0"]
+    s11 [label="sentence 1"]
+    low_rnn1 -> s10
+    low_rnn1 -> s11
+  }
+
+  subgraph cluster_p2 {
+    label = "sentence 1"
+    low_rnn2 [label="low rnn step 0" shape=box]
+    s20 [label="sentence 0"]
+    s21 [label="sentence 1"]
+    low_rnn2 -> s20
+    low_rnn2 -> s21
+  }
+
+  subgraph cluster_p3 {
+    label = "sentence 1"
+    low_rnn3 [label="low rnn step 1" shape=box]
+    s30 [label="sentence 0"]
+    s31 [label="sentence 1"]
+    low_rnn3 -> s30
+    low_rnn3 -> s31
+  }
+
+
+  chapter -> top_rnn0
+  chapter -> top_rnn1
+
+  top_rnn0 -> p0
+  top_rnn0 -> p1
+  top_rnn1 -> p2
+  top_rnn1 -> p3
+
+
+  p0 -> low_rnn0
+  p1 -> low_rnn1
+  p2 -> low_rnn2
+  p3 -> low_rnn3
+
+}

doc/fluid/images/test.dot

+
+digraph Test {
+    z -> generator -> G_img;
+    G_img -> discriminator -> D_f -> d_loss_f;
+    label0 -> d_loss_f -> d_loss;
+
+    img -> discriminator -> D_t -> d_loss_t;
+    label1 -> d_loss_t -> d_loss;
+
+    d_loss -> d_loss_t[color=red, style=dashed];
+    d_loss -> d_loss_f[color=red, style=dashed];
+    d_loss_t -> D_t[color=red, style=dashed];
+    d_loss_f -> D_f[color=red, style=dashed];
+    D_t -> discriminator[color=red, style=dashed];
+    D_f -> discriminator[color=red, style=dashed];
+
+    D_f -> g_loss;
+    label2 -> g_loss;
+
+    g_loss -> D_f[color=green, style=dashed];
+    D_f -> discriminator[color=green, style=dashed];
+    discriminator -> G_img[color=green, style=dashed];
+    G_img -> generator[color=green, style=dashed];
+
+    discriminator [color=red, shape=box];
+    generator [color=green, shape=box];
+    z [shape=diamond];
+    img [shape=diamond];
+    label0 [shape=diamond];
+    label1 [shape=diamond];
+    label2 [shape=diamond];
+
+    d_loss [color=red];
+    g_loss [color=green];
+}

doc/templates/conf.py.cn.in

@@ -13,7 +13,7 @@
 # serve to show the default.
 import sys
 import os, subprocess
-sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
+sys.path.insert(0, os.path.abspath('@PADDLE_BINARY_DIR@/python'))
 import shlex
 from recommonmark import parser, transform
 import paddle

doc/templates/conf.py.en.in

@@ -13,7 +13,7 @@
 # serve to show the default.
 import sys
 import os, subprocess
-sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
+sys.path.insert(0, os.path.abspath('@PADDLE_BINARY_DIR@/python'))
 import shlex
 from recommonmark import parser, transform
 import paddle

doc/v2/CMakeLists.txt

@@ -27,7 +27,7 @@ sphinx_add_target(paddle_v2_docs
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_EN})
 
-add_dependencies(paddle_v2_docs gen_proto_py)
+add_dependencies(paddle_v2_docs gen_proto_py paddle_python)
 
 # configured documentation tools and intermediate build results
 set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
@@ -50,6 +50,6 @@ sphinx_add_target(paddle_v2_docs_cn
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_CN})
 
-add_dependencies(paddle_v2_docs_cn gen_proto_py)
+add_dependencies(paddle_v2_docs_cn gen_proto_py paddle_python)
 
 add_subdirectory(api)

doc/v2/api/CMakeLists.txt

@@ -19,4 +19,4 @@ sphinx_add_target(paddle_v2_apis
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_EN})
 
-add_dependencies(paddle_v2_apis  gen_proto_py framework_py_proto copy_paddle_pybind)
+add_dependencies(paddle_v2_apis  gen_proto_py framework_py_proto copy_paddle_pybind paddle_python)