* add fleet util (fleet/utils/fleet_util.py): functions for users' convenience
* add some interface in hdfs util : hdfs is_file、hdfs cat

* add train demo for imdb text classification task

* make inference library release data_feed dataset dataset_factory data_feed_factory

* add String Data Generator

* new feature of train demo: save model params

* New feature of train demo: set training config using gflags

* change code style for CI

* add readme and dataset for imdb demo trainer

* change grad_op approval

* test=develop

* update roi doc in roi_pool and roi_align, test=develop

* fix QueueDataset queue size，set queue size = batch size * 100, to avoid too many instances in channel when training is much slower than reading data.

* fix memory overlapping of fetch var (return of executor.run), test=develop

* fix wrong usage of ParallelExecutor in op_test, test=develop

* remove useless parameter and simplify code

* avoid tensor destruct untimely, test=develop

* add testcase independent of OpTest, test=develop

As mentioned in this link, the last version of NumPy to support Python 2.7 is numpy 1.16.4.

* CI Add Reviewer Rules

* CI Add Reviewer Rules

* change git_files

* change git_files

* test=develop

* test=develop

* Add the check of lod in sequence_softmax kernel.
test=develop

* Refine the comments.
test=develop

* fix overflow by int32 mul test=develop

* fix reference nullptr

* fix codestyle test=develop

* modify to point in ContextProjectFunctor test=develop

* modify to point in ContextProjectFunctor test=develop

* modify . to -> test=develop

* add var_conv_2d op test=develop

* edit api.spec test=develop

* ignore unittest if with_mkl=off test=develop

* fix python3 division test=develop

* fix ignore unittest bug test=develop

* remove useless code test=develop

* modify api.spec test=develop

* modify default_grad.spec test=develop

* fix profiler doc error, test=develop

* update API.spec, test=develop

Fix dropout in nn.py

* fix g_param shape mismatch in WeightNormParamAttr

* add comment to show why insert reshape in startup_program
test=develop

* fix warpctc.dll not found issue, test=develop

* revert the linux platform change, test=develop

* delete warpctc_lib_path.h.in, test=develop

* add SetPySitePackagePath function

* fix warpctc.dylib not found issue on Mac, test=develop

* improve the paddle lib path setting logic, test=develop

* fix mac ci issue caused by test_warpctc_op unittest, test=develop

* tweak code, test=develop

* update exe.run

test=develop

python inference API support enable_memory_optim

Fix the third-party openblas dependency for paddle on windows

* test=develop support Tensor input for ctc_align_op

* test=develop add some comment

make listen and server as exclusive run 

* test=develop test=document_preview

* Update API.spec

* open gc by default, test=develop

* fix test_train_recognize_digits and disable gc when ngraph is enabled, test=develop

* fix conditional_block op eager deletion bug, test=develop

* add some comments to reviewers, test=develop

* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
test=develop

* add the missing files.
test=develop

* 1 add trt fp16 support
test=develop

* fix trt fp16 ce error
test=develop

* add an vlog if the user use trt4 and specify fp16.
test=develop

* support filelist size < trainer num
* pull dense when stop, to make sure local dense params are same as pserver, so save paddle model will save dense model same as pserver
*  enable QueueDataset train same filelist for serveral times

* Disable fuse optimization
test=develop

* Fix memory leak in test

test=develop

* Fix memory leak in test
test=develop

* Fix memory leak in test
test=develop

* Pull out vars of the loops
test=develop

* add fusion_seqpool_cvm_concat test=develop

* simplify pass, test=develop

* fix code style, test=develop

adjust ins weight according to nid slot , user can specify adjust_ins_weight in strategy

* fix gpu_info, test=develop

* fix reserving gpu memory calculation bug, add fraction=1 unittest, test=develop

* fix bug again for reserving size, test=develop

* test=develop
Add the op of unique_with_counts, the op is calc the unqiue input of data, and output the corresponding indices and count of data.

* test=develop
Check the input and dtype in the op of unique_with_counts

* test=develop
test=document_preview
update the API.spec for `unique_with_counts`, at the same time, optimize the python api in the op of `unique_with_count`

* test=develop
test=document_preview
Fix some python api problem in the op of `unique_with_counts`, and change the error messsage in this op.

* Fix some API problem in the op of `unique_with_counts`
test=develop
test=document_preview

* test=develop
test=document_preview
Fix the api sample of op `unique_with_counts`, and update api.spec

test=develop

- Extracted key generation from FWD and GRAD into separate function

test=develop

- Compilation fix

test=develop

- another compilation

test=develop

* fix depthwise conv gpu kernel bug, test=develop
* add more depthwise conv test, test=develop