* 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

* Update stacked_dynamic_lstm.py

* Update machine_translation.py

* Update mnist.py

* Update resnet.py

* Update vgg.py