remove conflict

Author: chengduoZH

RELEASE.cn.md

@@ -1,3 +1,62 @@
+# v0.11.0版本
+
+## PaddlePaddle Fluid
+
+- PaddlePaddle发布版本v0.11.0包含一个新的特性*PaddlePaddle Fluid*. Fluid 是设计用来让用户像Pytorch和Tensorflow Eager Execution一样执行程序。在这些系统中，不再有*模型*这个概念，应用也不再包含一个用于描述Operator图或者一系列层的符号描述，而是像通用程序那样描述训练或者预测的过程。而Fluid与PyTorch或Eager Execution的区别在于Fluid不依赖Python提供的控制流，例如 if-else-then或者for，而是提供了基于C++实现的控制流并暴露了对应的用with语法实现的Python接口。例如：
+
+ https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
+
+- 在v0.11.0版本中，我们提供了一个C++类`Executor`用于运行一个Fluid程序。Executor类似一个解释器。在未来的版本中，我们将提升和优化Executor成为一个调试器，就像GDB。并可能提供一些编译器，这个编译器会读取一个上文所描述的应用然后编译成一个等价的
+源代码，这个源代码可以被nvcc编译成可以使用CUDA的二进制，或者被icc编译成可以充分利用Intel CPU的二进制。
+
+
+## 新特点
+
+* 发布 `PaddlePaddle Fluid`。
+* 增加了用于模型预测的C-API。
+* 用Fluid API实现了一个简单的GAN的例子。
+* 增加了关于性能调优的文档。
+* 为`paddle.v2.dataset`下载数据集提供了重试机制.
+* C++中使用protobuf-lite替换protobuf减少了二进制的大小。
+* 发布了新特性 [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment).
+* 基于Bazel API利用cmake实现了一个的新的构建系统函数库。
+* 当使用编译选项`WITH_MKL=ON`时自动下载和编译Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) 函数库.
+* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
+ - 完成了 11个 MKL-DNN 层: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN。
+ - 完成了 3个 MKL-DNN 网络: VGG-19, ResNet-50, GoogleNet
+ - 基于Intel Skylake 6148 CPU的[性能测试](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) : 相对于MKLML有2~3倍的训练加速。
+* 增加 [softsign activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign)
+* 增加 [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod)
+* 增加 [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance)
+* 增加 [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq)
+* 增加 [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score)
+* 增加 [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice)
+* 增加 [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
+* 增加移动端友好的网页
+
+## 改进
+
+* 使用一个Python`whl`包即可安装.
+* [V2 API可以实现用户定制化评估](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标)。
+* 将 `PADDLE_ONLY_CPU` 改为 `PADDLE_WITH_GPU`, 因为我们会支持多种设备。
+* 删除了有一些bug的BarrierStat。
+* 清理和删除了paddle::Parameter中未使用的函数。
+* 删除了ProtoDataProvider。
+* Huber loss同时支持回归和分类。
+* 为sequence pooling 层增加`stride`参数。
+* v2 API自动使用cudnn batch normalization。
+* 可以使用一个固定的参数名共享BN层的参数。
+* 2D convolution operation支持variable-dimension input特性。
+* 重构cmake中关于CUDA的部分并实现自动检测GPU架构的功能。
+* 优化网页导航。
+
+## 错误修复
+
+* 修复ROI pooling的Bug. cc9a761
+* 修复当label是dense vector是AUC变成0的问题. #5274
+* 修复WarpCTC 层的Bug.
+
+
 # v0.10.0版本
 
 我们非常高兴发布了PaddlePaddle V0.10.0版，并开发了新的[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。

RELEASE.md

@@ -1,3 +1,75 @@
+# Release v0.11.0
+
+## PaddlePaddle Fluid
+
+- Release 0.11.0 includes a new feature *PaddlePaddle Fluid*. Fluid is
+ designed to allow users to program like PyTorch and TensorFlow Eager Execution.
+ In these systems, there is no longer the concept *model* and applications
+ do not include a symbolic description of a graph of operators nor a sequence
+ of layers. Instead, applications look exactly like a usual program that
+ describes a process of training or inference. The difference between
+ Fluid and PyTorch or Eager Execution is that Fluid doesn't rely on Python's
+ control-flow, `if-then-else` nor `for`. Instead, Fluid provides its
+ C++ implementations and their Python binding using the `with` statement. For an example
+
+ https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
+
+- In 0.11.0, we provides a C++ class `Executor` to run a Fluid program.
+Executor works like an interpreter. In future version, we will improve
+`Executor` into a debugger like GDB, and we might provide some compilers,
+which, for example, takes an application like the above one, and outputs
+an equivalent C++ source program, which can be compiled using
+[`nvcc`](http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)
+to generate binaries that use CUDA, or using
+[`icc`](https://software.intel.com/en-us/c-compilers) to generate binaries
+that make full use of Intel CPUs.
+
+## New Features
+
+* Release `PaddlePaddle Fluid`.
+* Add C-API for model inference
+* Use fluid API to create a simple GAN demo.
+* Add develop guide about performance tunning.
+* Add retry when download `paddle.v2.dataset`.
+* Linking protobuf-lite not protobuf in C++. Reduce the binary size.
+* Feature [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment) released.
+* A new style cmake functions for Paddle. It is based on Bazel API.
+* Automatically download and compile with Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) library as CBLAS when build `WITH_MKL=ON`.
+* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
+ - Complete 11 MKL-DNN layers: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN.
+ - Complete 3 MKL-DNN networks: VGG-19, ResNet-50, GoogleNet
+ - [Benchmark](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) on Intel Skylake 6148 CPU: 2~3x training speedup compared with MKLML.
+* Add the [`softsign` activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign).
+* Add the [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod).
+* Add the [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance).
+* Add the [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq).
+* Add the [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score).
+* Add the [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice).
+* Add the [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
+* Add mobile friendly webpages.
+
+## Improvements
+
+* Build and install using a single `whl` package.
+* [Custom evaluating in V2 API](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标).
+* Change `PADDLE_ONLY_CPU` to `PADDLE_WITH_GPU`, since we will support many kinds of devices.
+* Remove buggy BarrierStat.
+* Clean and remove unused functions in paddle::Parameter.
+* Remove ProtoDataProvider.
+* Huber loss supports both regression and classification.
+* Add the `stride` parameter for sequence pooling layers.
+* Enable v2 API use cudnn batch normalization automatically.
+* The BN layer's parameter can be shared by a fixed the parameter name.
+* Support variable-dimension input feature for 2D convolution operation.
+* Refine cmake about CUDA to automatically detect GPU architecture.
+* Improved website navigation.
+
+## Bug Fixes
+
+* Fix bug in ROI pooling. cc9a761
+* Fix AUC is zero when label is dense vector. #5274
+* Fix bug in WarpCTC layer.
+
 # Release v0.10.0
 
 We are glad to release version 0.10.0. In this version, we are happy to release the new 

benchmark/IntelOptimizedPaddle.md

@@ -19,6 +19,8 @@ On each machine, we will test and compare the performance of training on single
 ## Benchmark Model
 
 ### Server
+
+#### Training
 Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
 
 Input image size - 3 * 224 * 224, Time: images/second
@@ -53,5 +55,33 @@ Input image size - 3 * 224 * 224, Time: images/second
 
 <img src="figs/googlenet-cpu-train.png" width="500">
 
+#### Inference
+Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+- VGG-19
+
+| BatchSize | 1 | 2 | 4 | 8 | 16 |
+|-----------|-------|-------|-------|-------|-------|
+| OpenBLAS | 1.07 | 1.08 | 1.06 | 0.88 | 0.65 |
+| MKLML | 5.58 | 9.80 | 15.15 | 21.21 | 28.67 |
+| MKL-DNN | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
+
+- ResNet-50
+
+| BatchSize | 1 | 2 | 4 | 8 | 16 |
+|-----------|-------|--------|--------|--------|--------|
+| OpenBLAS | 3.35 | 3.19 | 3.09 | 2.55 | 1.96 |
+| MKLML | 6.33 | 12.02 | 22.88 | 40.53 | 63.09 |
+| MKL-DNN | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
+
+
+- GoogLeNet
+
+| BatchSize | 1 | 2 | 4 | 8 | 16 |
+|-----------|--------|--------|--------|--------|--------|
+| OpenBLAS | 12.04 | 11.31 | 10.00 | 9.07 | 4.34 |
+| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
+| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
+
+
 ### Laptop
 TBD

cmake/cblas.cmake

@@ -17,7 +17,7 @@ if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
  set(CBLAS_INC_DIR ${MKLML_INC_DIR})
  set(CBLAS_LIBRARIES ${MKLML_LIB})
 
- add_definitions(-DPADDLE_USE_MKLML)
+ add_definitions(-DPADDLE_WITH_MKLML)
  add_definitions(-DLAPACK_FOUND)
 
  message(STATUS "Found cblas and lapack in MKLML "

cmake/external/mkldnn.cmake

@@ -67,5 +67,5 @@ ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
 MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
-add_definitions(-DPADDLE_USE_MKLDNN)
+add_definitions(-DPADDLE_WITH_MKLDNN)
 LIST(APPEND external_project_dependencies mkldnn)

doc/howto/dev/contribute_to_paddle_cn.md

@@ -76,18 +76,18 @@ no changes added to commit (use "git add" and/or "git commit -a")
 
 ## 构建和测试
 
-编译 PaddlePaddle 的源码以及生成文档需要多种开发工具。为了方便大家，我们的标准开发流程是把这些工具都装进一个Docker image，称为*开发镜像*，通常名字是 `paddle:dev`。然后所有用 `cmake && make` 的地方（比如IDE配置里）都用 `docker run paddle:dev`来代替。
+编译 PaddlePaddle 的源码以及生成文档需要多种开发工具。为了方便大家，我们的标准开发流程是把这些工具都装进一个Docker image，称为*开发镜像*，通常名字是 `paddle:latest-dev` 或者 `paddle:[version tag]-dev` 如 `paddle:0.11.0-dev`。然后所有用 `cmake && make` 的地方（比如IDE配置里）都用 `docker run paddle:latest-dev`来代替。
 
 如要build这个开发镜像，在源码目录树的根目录中运行：
 
 ```bash
-➜ docker build -t paddle:dev .
+➜ docker build -t paddle:latest-dev .
 ```
 
 随后可以用这个开发镜像开始build PaddlePaddle的源码。比如如果要build一个不依赖GPU，但是支持AVX指令集，并且包括unit tests的PaddlePaddle，可以：
 
 ```bash
-➜ docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TEST=ON" paddle:dev
+➜ docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TESTING=ON" paddle:latest-dev
 ```
 
 这个过程除了编译PaddlePaddle为 `./build/libpaddle.so`，并且输出一个 `./build/paddle.deb`文件之外，还会输出一个 `build/Dockerfile`。我们只需要运行下面命令把编译好的PaddlePaddle打包成一个*生产镜像*（`paddle:prod`）：
@@ -99,7 +99,7 @@ no changes added to commit (use "git add" and/or "git commit -a")
 如果要运行所有的单元测试，可以用如下命令：
 
 ```bash
-➜ docker run -it -v $(pwd):/paddle paddle:dev bash -c "cd /paddle/build && ctest"
+➜ docker run -it -v $(pwd):/paddle paddle:latest-dev bash -c "cd /paddle/build && ctest"
 ```
 
 关于构建和测试的更多信息，请参见[这篇文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。