* memory init

* add env

* refine anounce

* Add check for Nan

* Debug

* Add env for cc_test

* Add env for py_test and nv_test

* Remove py_test env

* Add env for py_test

* serial test_recognize_digits

* Test FLAGS_init_allocated_mem function for unit test

* Init allocated mem for op unit test

* Add env for all unit test

* "fix deterministic"

* "fix ci"

* "fix init"

2. remvoe comment line

* add lod_tensor utility python module

* add lod_tensor test code

* add more lod tensor tests

* modify word2vec example code using new api

* add comment

* fix trainer import error on ce

* fix setup.py.in

- The trainer and inferencer will load params from disk if param_path
  argument is not None in their constructor.

- Remove params.py, we will expose core.Scope to the user if needed
  (e.g., for GAN). Currently we will not expose it, unless we clearly
  know doing so can support GAN.

- Add `save_params` to Trainer (a TODO item).

- rename "network" to "program"

* "add metrics"

* "add fluid metrics"

* "add import guards"

* "show warnings"

* "add demo"

* "fix ci"

* "add some details"

* "fix cci"

* "add demo Python"

* "add metrics"

* Fix old documentation for channel_recv

* Initial design of CSP select

* Redesign channel implementation for Select Op

* Remove unecessary header

* Initial checkin of select op, currently will read all the conditional_op in the cases block and also pull out all channels involved in the select.

* Init python select op API

* Python select bug fix when checking op creates block

* Add case_to_execute as (a) input to select, (b) into the passed inputs into the select op

* Add in addition code for select op

* Init fibonacci test from python

* implement fibonnaci sequence test

* update fib unit test

* Improve select test cases

* Shorten non-pep-8-ed lines

* Add methods on channel needed by select op

* Fix compile issues, finish implementation, still need to debug code

* Fix issue with fibonncci test, it works now!

* Change QueueMessage callback to take in an ChannelAction enum, fix select unit test

* Fix case attributes

* Fix issue with select control flow

* Make cases - previously on each selectcase conditional_block - attributes to select

* Use class constants for type of channel

* Change select op to take in "cases" attribute

* return boolean from select callback function to tell Channel if this RECV or SEND should be executed

* Improve attributes and inputs comments on select op

* Fix issues with python unit test

* Assert fibonacci final output

* Fix issue when channel name / channel var is null for "default" case in select op

* Assert base select test output

* Make QueueMessage use shared pointer and modify the order of the callback

* Fixing the order in which the callback is called

* Move channel utility methods to paddle/fluid/operators/concurrency/channel_util

* Create channel_util and move channel util methods

* Fix crash when calling select_op

* Fix deadlock

* Fix issue of channel destructor deadlock

* Fix precommit issues

* Accidentally checked in changes to beam_search_op, reverting change.

* Fix dependency issue in concurrency cmake

* add device_context dependency for concurrency target

* add memopt level

* add opt level for image classification demo

* clean code

* add delete op

* clean code

* test machine translation demo

* clean code

* clean code

* skip fill constant with force cpu

* clean code

* clean code

* refine code

* clean code

* fix bug

* change learning_rate_decay to learning_rate_scheduler
* optimize code
* change nn.cast to tensor.cast

* Add reset and guard to unique_name

* Adding Python boilerplate code for Go op

* Add very basic test case

* Adding the python logic for go routine

* Fix syntax

* Changing test to notest

* Rename Routine to Go

* Combining GoGuard and Go in one class

* Modify test

* Adding fluid close channel

* Fixing __init__.py for calling fluid.go()

* Adding stubs for channel methods and updating test case

* Removing import *

* Adding imports from concurrency

* add basic interface for learning rate decay
* add exponential_decay
* add natural_exp_decay
* add inverse_time_decay