Merge pull request #17088 from shanyi15/update_1.0_readme

update_1.0_readme

README.md

@@ -2,8 +2,8 @@
 
 
 [![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
-[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html)
-[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html)
+[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
+[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
 [![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
 [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
 
@@ -19,7 +19,7 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
 Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
 
 
-### Latest PaddlePaddle Release: [Fluid 0.15.0](https://github.com/PaddlePaddle/Paddle/tree/v0.15.0)
+### Latest PaddlePaddle Release: [Fluid 1.0.2](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
 ### Install Latest Stable Release:
 ```
 # Linux CPU
@@ -27,9 +27,9 @@ pip install paddlepaddle
 # Linux GPU cuda9cudnn7
 pip install paddlepaddle-gpu
 # Linux GPU cuda8cudnn7
-pip install paddlepaddle-gpu==0.15.0.post87
+pip install paddlepaddle-gpu==1.0.2.post87
 # Linux GPU cuda8cudnn5
-pip install paddlepaddle-gpu==0.15.0.post85
+pip install paddlepaddle-gpu==1.0.2.post85
 
 # For installation on other platform, refer to http://paddlepaddle.org/
 ```
@@ -76,26 +76,26 @@ pip install paddlepaddle-gpu==0.15.0.post85
 
 ## Installation
 
-It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/beginners_guide/install/install_doc.html) on our website.
+It is recommended to read [this doc](http://www.paddlepaddle.org/documentation/docs/en/1.0/build_and_install/index_en.html) on our website.
 
 ## Documentation
 
-We provide [English](http://paddlepaddle.org/documentation/docs/en/0.15.0/getstarted/index_en.html) and
-[Chinese](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/beginners_guide/index.html) documentation.
+We provide [English](http://www.paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html) and
+[Chinese](http://www.paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) documentation.
 
 - [Deep Learning 101](https://github.com/PaddlePaddle/book)
 
   You might want to start from this online interactive book that can run in a Jupyter Notebook.
 
-- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/user_guides/howto/training/cluster_howto.html)
+- [Distributed Training](http://www.paddlepaddle.org/documentation/docs/zh/1.0/user_guides/howto/training/multi_node.html)
 
   You can run distributed training jobs on MPI clusters.
 
-- [Python API](http://paddlepaddle.org/documentation/api/zh/0.15.0/fluid.html)
+- [Python API](http://www.paddlepaddle.org/documentation/api/zh/1.0/fluid.html)
 
    Our new API enables much shorter programs.
 
-- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/advanced_usage/development/contribute_to_paddle.html)
+- [How to Contribute](http://www.paddlepaddle.org/documentation/docs/en/1.0/dev/contribute_to_paddle_en.html)
 
    We appreciate your contributions!
 

RELEASE.cn.md

-# 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/)。
-
-- 旧的Python API由于难以学习和使用已经过时了。使用旧版本的API至少需要两份python文件，分别是定义数据生成器和定义网络拓扑结构的文件。用户通过运行`paddle_trainer`的C++程序来启动PaddlePaddle任务，该程序调用Python解释器来运行定义网络拓扑结构的文件，然后通过迭代加载数据生成器提供的小批量数据启动训练循环。这与Python的现代编辑方式不符，比如Jupyter Notebook。
-
-- 新版的API被称为 *V2 API*，允许我们在单个.py文件中，通过编辑更短的Python程序来定义网络结构和数据。此外，该Python程序也可以在Jupyter Notebook中运行，因为PaddlePaddle可以作为共享库来被Python程序加载和使用。
-
-基于新的API，我们提供了一个在线的学习文档 [Deep Learning 101](http://book.paddlepaddle.org/index.en.html) 及其[中文版本](http://book.paddlepaddle.org/)。
-
-我们还致力于迭代更新新版API的在线文档，并将新版API引入分布式集群（包括MPI和Kubernetes）训练中。我们将在下一个版本中发布更多的内容。
-
-## 新特点
-
-* 发布新版[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。
-* 发布深度学习系列课程 [Deep Learning 101](http://book.paddlepaddle.org/index.en.html) 及其[中文版本](http://book.paddlepaddle.org/)。
-* 支持矩形输入的CNN。
-* 为seqlastin和seqfirstin提供stride pooling。
-* 在`trainer_config_helpers`中暴露`seq_concat_layer/seq_reshape_layer`。
-* 添加公共数据集包：CIFAR，MNIST，IMDB，WMT14，CONLL05，movielens，imikolov。
-* 针对Single Shot Multibox Detection增加 Prior box layer。
-* 增加光滑的L1损失。
-* 在V2 API中增加 data reader 创建器和修饰器。
-* 增加cmrnorm投影的CPU实现。
-
-
-## 改进
-
-* 提供`paddle_trainer`的Python virtualenv支持。
-* 增加代码自动格式化的pre-commit hooks。
-* 升级protobuf到3.x版本。
-* 在Python数据生成器中提供一个检测数据类型的选项。
-* 加速GPU中average层的后向反馈计算。
-* 细化文档。
-* 使用Travis-CI检查文档中的死链接。
-* 增加解释`sparse_vector`的示例。
-* 在layer_math.py中添加ReLU。
-* 简化Quick Start示例中的数据处理流程。
-* 支持CUDNN Deconv。
-* 在v2 API中增加数据feeder。
-* 在情感分析示例的演示中增加对标准输入流中样本的预测。
-* 提供图像预处理的多进程接口。
-* 增加V1 API的基准文档。
-* 在`layer_math.py`中增加ReLU。
-* 提供公共数据集的自动下载包。
-* 将`Argument::sumCost`重新命名为`Argument::sum`，并暴露给python。
-* 为矩阵相关的表达式评估增加一个新的`TensorExpression`实现。
-* 增加延迟分配来优化批处理多表达式计算。
-* 增加抽象的类函数及其实现：
-  * `PadFunc` 和 `PadGradFunc`。
-  * `ContextProjectionForwardFunc` 和 `ContextProjectionBackwardFunc`。
-  * `CosSimBackward` 和 `CosSimBackwardFunc`。
-  * `CrossMapNormalFunc` 和 `CrossMapNormalGradFunc`。
-  * `MulFunc`。
-* 增加`AutoCompare`和`FunctionCompare`类，使得编写比较gpu和cpu版本函数的单元测试更容易。
-* 生成`libpaddle_test_main.a`并删除测试文件内的主函数。
-* 支持PyDataProvider2中numpy的稠密向量。
-* 清理代码库，删除一些复制粘贴的代码片段：
-  * 增加`SparseRowMatrix`的抽样类`RowBuffer`。
-  * 清理`GradientMachine`的接口。
-  * 在layer中增加`override`关键字。
-  * 简化`Evaluator::create`，使用`ClassRegister`来创建`Evaluator`。
-* 下载演示的数据集时检查MD5校验。
-* 添加`paddle::Error`，用于替代Paddle中的`LOG(FATAL)`。
-
-
-## 错误修复
-
-* 检查`recurrent_group`的layer输入类型。
-* 不要用.cu源文件运行`clang-format`。
-* 修复`LogActivation`的使用错误。
-* 修复运行`test_layerHelpers`多次的错误。
-* 修复seq2seq示例超出消息大小限制的错误。
-* 修复在GPU模式下dataprovider转换的错误。
-* 修复`GatedRecurrentLayer`中的错误。
-* 修复在测试多个模型时`BatchNorm`的错误。
-* 修复paramRelu在单元测试时崩溃的错误。
-* 修复`CpuSparseMatrix`编译时相关的警告。
-* 修复`MultiGradientMachine`在`trainer_count > batch_size`时的错误。
-* 修复`PyDataProvider2`阻止异步加载数据的错误。

RELEASE.md

-# Release v0.11.0
+# Release Note
 
-## 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 
-[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/).
-
-- Our old Python API is kind of out of date.  It's hard to learn and hard to
-  use.  To write a PaddlePaddle program using the old API, we'd have to write
-  at least two Python files: one `data provider` and another one that defines
-  the network topology.  Users start a PaddlePaddle job by running the
-  `paddle_trainer` C++ program, which calls Python interpreter to run the
-  network topology configuration script and then start the training loop,
-  which iteratively calls the data provider function to load minibatches.
-  This prevents us from writing a Python program in a modern way, e.g., in the
-  Jupyter Notebook.
-  
-- The new API, which we often refer to as the *v2 API*, allows us to write
-  much shorter Python programs to define the network and the data in a single
-  .py file.  Also, this program can run in Jupyter Notebook, since the entry
-  point is in Python program and PaddlePaddle runs as a shared library loaded
-  and invoked by this Python program.
-  
-Basing on the new API, we delivered an online interative
-book, [Deep Learning 101](http://book.paddlepaddle.org/index.en.html)
-and [its Chinese version](http://book.paddlepaddle.org/).
-
-We also worked on updating our online documentation to describe the new API.
-But this is an ongoing work.  We will release more documentation improvements
-in the next version.
-
-We also worked on bring the new API to distributed model training (via MPI and
-Kubernetes).  This work is ongoing. We will release more about it in the next
-version.
-
-## New Features
-
-* We release [new Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/).
-* Deep Learning 101 book in [English](http://book.paddlepaddle.org/index.en.html) and [Chinese](http://book.paddlepaddle.org/).
-* Support rectangle input for CNN.
-* Support stride pooling for seqlastin and seqfirstin.
-* Expose `seq_concat_layer/seq_reshape_layer` in `trainer_config_helpers`.
-* Add dataset package: CIFAR, MNIST, IMDB, WMT14, CONLL05, movielens, imikolov.
-* Add Priorbox layer for Single Shot Multibox Detection. 
-* Add smooth L1 cost.
-* Add data reader creator and data reader decorator for v2 API.
-* Add the CPU implementation of cmrnorm projection.
-
-## Improvements
-
-* Support Python virtualenv for `paddle_trainer`.
-* Add pre-commit hooks, used for automatically format our code.
-* Upgrade protobuf to version 3.x.
-* Add an option to check data type in Python data provider.
-* Speedup the backward of average layer on GPU.
-* Documentation refinement.
-* Check dead links in documents using Travis-CI.
-* Add a example for explaining `sparse_vector`.
-* Add ReLU in layer_math.py
-* Simplify data processing flow for Quick Start.
-* Support CUDNN Deconv.
-* Add data feeder in v2 API.
-* Support predicting the samples from sys.stdin for sentiment demo.
-* Provide multi-proccess interface for image preprocessing. 
-* Add benchmark document for v1 API.
-* Add ReLU in `layer_math.py`.
-* Add packages for automatically downloading public datasets.
-* Rename `Argument::sumCost` to `Argument::sum` since class `Argument` is nothing with cost.
-* Expose Argument::sum to Python
-* Add a new `TensorExpression` implementation for matrix-related expression evaluations.
-* Add lazy assignment for optimizing the calculation of a batch of multiple expressions.
-* Add abstract calss `Function` and its implementation:
-  * `PadFunc` and `PadGradFunc`.
-  * `ContextProjectionForwardFunc` and `ContextProjectionBackwardFunc`.
-  * `CosSimBackward` and `CosSimBackwardFunc`.
-  * `CrossMapNormalFunc` and `CrossMapNormalGradFunc`.
-  * `MulFunc`.
-* Add class `AutoCompare` and `FunctionCompare`, which make it easier to write unit tests for comparing gpu and cpu version of a function.
-* Generate `libpaddle_test_main.a` and remove the main function inside the test file.
-* Support dense numpy vector in PyDataProvider2.
-* Clean code base, remove some copy-n-pasted code snippets:
-  * Extract `RowBuffer` class for `SparseRowMatrix`.
-  * Clean the  interface of `GradientMachine`.
-  * Use `override` keyword in layer.
-  * Simplify `Evaluator::create`, use `ClassRegister` to create `Evaluator`s.
-* Check MD5 checksum when downloading demo's dataset.
-* Add `paddle::Error` which intentially replace `LOG(FATAL)` in Paddle.
-
-## Bug Fixes
-
-* Check layer input types for `recurrent_group`.
-* Don't run `clang-format` with .cu source files.
-* Fix bugs with `LogActivation`.
-* Fix the bug that runs `test_layerHelpers` multiple times.
-* Fix the bug that the seq2seq demo exceeds protobuf message size limit.
-* Fix the bug in dataprovider converter in GPU mode.
-* Fix a bug in `GatedRecurrentLayer`.
-* Fix bug for `BatchNorm` when testing more than one models.
-* Fix broken unit test of paramRelu.
-* Fix some compile-time warnings about `CpuSparseMatrix`.
-* Fix `MultiGradientMachine` error when `trainer_count > batch_size`.
-* Fix bugs that prevents from asynchronous data loading in `PyDataProvider2`.
-
-# Release v0.9.0
-
-## New Features:
-
-* New Layers
-  * bilinear interpolation layer.
-  * spatial pyramid-pool layer.
-  * de-convolution layer.
-  * maxout layer.
-* Support rectangle padding, stride, window and input for Pooling Operation.
-* Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON.
-* Expose cost_weight/nce_layer in `trainer_config_helpers`
-* Add FAQ, concepts, h-rnn docs.
-* Add Bidi-LSTM and DB-LSTM to quick start demo @alvations
-* Add usage track scripts.
-
-## Improvements
-
-* Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed.
-* Add code coverage tools.
-* Refine convolution layer to speedup and reduce GPU memory.
-* Speed up PyDataProvider2
-* Add ubuntu deb package build scripts.
-* Make Paddle use git-flow branching model.
-* PServer support no parameter blocks.
-
-## Bug Fixes
-
-* add zlib link to py_paddle
-* add input sparse data check for sparse layer at runtime
-* Bug fix for sparse matrix multiplication
-* Fix floating-point overflow problem of tanh
-* Fix some nvcc compile options
-* Fix a bug in yield dictionary in DataProvider
-* Fix SRL hang when exit.
-
-# Release v0.8.0beta.1
-New features:
-
-* Mac OSX is supported by source code. #138
-   * Both GPU and CPU versions of PaddlePaddle are supported.
-
-* Support CUDA 8.0
-
-* Enhance `PyDataProvider2`
-   * Add dictionary yield format. `PyDataProvider2` can yield a dictionary with key is data_layer's name, value is features.
-   * Add `min_pool_size` to control memory pool in provider.
-
-* Add `deb` install package & docker image for no_avx machines.
-   * Especially for cloud computing and virtual machines
-
-* Automatically disable `avx` instructions in cmake when machine's CPU don't support `avx` instructions.
-
-* Add Parallel NN api in trainer_config_helpers.
-
-* Add `travis ci` for Github
-
-Bug fixes:
-
-* Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers
-* Check if PaddlePaddle is installed when unittest.
-* Fix bugs in GTX series GPU
-* Fix bug in MultinomialSampler
-
-Also more documentation was written since last release.
-
-# Release v0.8.0beta.0
-
-PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version.
+Please turn to [here](https://github.com/PaddlePaddle/Paddle/releases) for release note.