* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

* 1. add weight decay feature into fluid benchmark test
2. add learning rate decay feature into fluid benchmark test
3. add L1&L2 regularization feature into fluid benchmark test
4. add error clipping feature into fluid benchmark test
5. add gradient clipping feature into fluid benchmark test

* Add some document to README.md under benchmark/fluid/ repo

* Add model_base.py

* Fix bugs in test_listen_and_serv_op

* 1. remove args out of fluid_benchmark.py
2. remove lr_decay, regularization, clipping out of fluid_benchmark.py

* add async_mode description to doc and remove the clipping description out

* for restart build

* to restart build

* remove optimization args from args.py

* 1. remove optimization from models
2. fix bug in test_listen_and_serv_op

* change the name retry_times to left_time

* change retry_times to the pserver start left time

* add fluid benchmark Dockerfile

* add_fluid_benchmark_dockerfile

* update benchmark doc

* update by comment

* wip integrate benchmark scripts

* testing nlp models

* k8s script to start dist benchmark job

* update script

* done support all models

* add README.md

* update by comment

* clean up

* follow comments

    Experiment on vgg flower, 2 trainers, 1ps.
    more trainer could have more speedup.

    After:
    Pass = 0, Iters = 327, Speed = (7.52) img/s
    Before:
    Pass = 0, Iters = 385, Speed = (6.77) img/s