* update cluster train v2 doc

* WIP cluster train doc

* update

* cluster train doc

* add TOC for en doc

* fix sphix build issue

* fix error links

* fix link errors

* fix image link

* polish cluster train docs

* update general distributed training document

* fix sphinx compile error

* fix doc image error

* Implement FC layer with helper

* Update LayerHelper

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Layer wrapper for FC

* Fix unittest

* Fix CI

* Add code generator

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* Complete mlp, fit_a_line

* Implementation of simple conv_2d layer

* Fix bugs

* Change ProgramDesc not a global variable

* Polish code style

* Stash

* Correct implement BlockDesc destructor

* Correct implement BlockDesc destructor

* Unify program as parameter name

* Fix bugs

* Add unittest

* Fix unit test error

* Remove unused functions

* Add clone for Python Program

* Compare OpDescBind directly

* Add glog as dependencies of ops

* Use VLOG to logging some information is helpful when we debug Paddle

* Fix Unittests

* Adding Proximal Gradient Descent

* Fixing review comments

* Implement FC layer with helper

* Update LayerHelper

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Layer wrapper for FC

* Fix unittest

* Fix CI

* Add code generator

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* Complete mlp, fit_a_line

* Implementation of simple conv_2d layer

* Fix bugs

* Correct implement BlockDesc destructor

* Fix bugs

* Fix unit test error

* Follow comments

* Add design doc of batch_norm_op

* Move batch_norm_op.png to operator/images

* Refine batch_norm_op design doc

* Change ProgramDesc not a global variable

* Polish code style

* Correct implement BlockDesc destructor

* Unify program as parameter name

* initial matmul operator

Similar to np.matmul, but also has transpose_X and transpose_Y flags,
and only supports tensors from rank 1 to 3 inclusive.

For GPU, uses cublas?gemmStridedBatched. For CPU, uses
cblas_?gemm_batch if available via MKL; otherwise a simple serial
implementation that loops over the batch dimension is employed for now.

* init parameter base class

* optimize the Comments of optimizer

* basic implimentation of optimizer

* add test_optimizer

* add no_grad_set to interface

* update optimizer.py

* python code can run

* fix some problem

* add sync_with_cpp to Python Program and Block

* sync vars and ops in block from cpp

* optimize code and add some comment

* add more check for sync

* update optimizer with return value of Backward

* rm unused code

* infer shape when create gradient vairiable

* update test_optimizer

* update test_program.py

* update backward test

* follow comment

* Make global scope not thread-safe

1. It is no need to make global scope thread-safe, since it will be
invoked in Python main thread.
2. Do not free the global scope when C++ exit. Let the OS free memories,
otherwise, we need to handle the destroy dependencies.

See
https://google.github.io/styleguide/cppguide.html#Static_and_Global_Variables

* Revert "FIX: Release CPU/GPU memory via deleter"

This reverts commit 8f80f5bc.

They are public now

* rm cpp executor_test, rewrite in python later

* remove executor_test code in CMakeList.txt

* Change dataType to data_type

Follow PEP8

* Change name_convention to fit PEP8

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* add FIXME comment