Merge pull request #1957 from wangkuiyi/polish-release-note

Polish release notes

.pre-commit-config.yaml

 -   repo: https://github.com/Lucas-C/pre-commit-hooks.git
-    sha: c25201a00e6b0514370501050cf2a8538ac12270
+    sha: v1.0.1
     hooks:
     -   id: remove-crlf
         files: (?!.*third_party)^.*$ | (?!.*book)^.*$
 -   repo: https://github.com/reyoung/mirrors-yapf.git
     sha: v0.13.2
     hooks:
-    - id: yapf
-      files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
+    -   id: yapf
+        files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    sha: 7539d8bd1a00a3c1bfd34cdb606d3a6372e83469
+    sha: 5bf6c09bfa1297d3692cadd621ef95f1284e33c0
     hooks:
     -   id: check-added-large-files
     -   id: check-merge-conflict

RELEASE.md

 # 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+- Our old Python API is kind of out of date.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/).
+* 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.
+* 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.
+* Add the CPU implementation of cmrnorm projection.
 
 ## Improvements
 
-* Support python virtualenv for `paddle_trainer` process.
+* Support Python virtualenv for `paddle_trainer`.
 * Add pre-commit hooks, used for automatically format our code.
-* Use Protobuf 3.X as the default Paddle Protobuf version.
-* Add an option to check data type in python data provider.
+* 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.
-* Reorganize the catalog of doc/ and refine several docs.
-* Add Travis-CI for checking dead links.
-* Add a example for explaining sparse_vector.
-* Add Relu in layer_math.py
-* Simplify data processing flow for quick start.
+* 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 for v2 API.
+* 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 ReLU in `layer_math.py`.
 * Add packages for automatically downloading public datasets.
-* Rename Argument::sumCost to Argument::sum since Argument does not have to have any relationship with cost.
-  * Expose Argument::sum to Python
+* 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 optimize the calculation of multiple expressions.
-* Add `Function` to reconstruct the computation function.
-  * PadFunc and PadGradFunc.
-  * ContextProjectionForwardFunc and ContextProjectionBackwardFunc.
-  * CosSimBackward and CosSimBackwardFunc.
-  * CrossMapNormalFunc and CrossMapNormalGradFunc.
-  * MulFunc.
-* Add `AutoCompare` and `FunctionCompare`, which make it easier to write unittest for comparing gpu and cpu version of a function.
-* Add `libpaddle_test_main.a` and remove the main function inside the test file.
+* 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 & paste codes before.
-  * Extract RowBuffer class for SparseRowMatrix.
-  * Clean GradientMachine's interface.
-  * Try use `override` keyword in layer.
-  * Simplify Evaluator::create, use `ClassRegister` to create Evaluator.
-* Add md5 check when downloading demo's dataset.
+* 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
 
-* Add layer check for recurrent_group.
-* Clang-format off on some cuda .cc files.
-* Fix LogActivation which is not defined.
-* Fix bug when run test_layerHelpers multiple times.
-* Fix protobuf size limit on seq2seq demo.
-* Fix bug for dataprovider converter in GPU mode.
-* Fix bug in GatedRecurrentLayer which only occurs in predicting or `job=test` mode.
-* Fix bug for BatchNorm when testing more than models in test mode.
-* Fix unit test of paramRelu.
-* Fix some warning about CpuSparseMatrix.
-* Fix MultiGradientMachine error if trainer_count > batch_size.
-* Fix when async load data in PyDataProvider2.
+* 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