* It seems some other change breaks the async load. It will cause data
  error in GPU mode.

* The DataProvider should be INCREF every time.

When the define_py_data_sources2 has both train_list and test_list,
for job=test, the trainer will create both dataProvider_ and testDataProvider_.
But dataProvider_ is not used. This causes SIGSEGV at finishAsync() because asyncLoader_ is not created.

Change-Id: If579f715f80a70ebc795094792c3436bfa0f5746

* Fix SRL hang when exit.

* Error occurred when enable Async Load in TestDataProvider.
  * It because DataProvider is calling getNextBatchInternal in one thread, and destructing DataProvider in other thread.
  * Add wait routine in DataProvider destructing.
* Also fix another bug, when destructing TestDataProvider and do not read any test data.

Fix #286

* Follow comments, Use mutex is cool!

* Fix bug in yield dictionary in DataProvider.
* Also make virtualenv work in Paddle.

* fix DataProvider create function args bug

Change-Id: I9e3a1c535c805bf30204a14aea8d5143ff534784

* remove PserverForPython.h which is not used

Change-Id: I2b27f1f3c11a42766a92fc689f0f5f1f73ee1d70

* add internal document script

Change-Id: Ia0fec79456caea0b271f9903cc13e8a3d32e0774

This bug occasionally causes dead lock in test_RecurrentGradientMachine
In general, conditional_variable::notify should be used together with mutex for changing condition.

* reduce trainer count for unit test on MAC OSX

* min_pool_size would be infinite by default.
  * add unittest for min_pool_size
* Fix bug in can_over_batch_size
  * add unittest for can_over_batch_size
* Add DEFINE_PROVIDER_EX
* Add default value of should_shuffle
  * When training, the default value of should_shuffle is True.
  * When testing, the default value of should_shuffle is False.
  * User a set a provider should_shuffle or not by pass it to `@provider`
  * should_shuffle can handle a list of value, not just boolean
* Add input order mapping by using name
  * Add unittest
* Add check to check input format.
  * Default is close for speed reason.
  * User could stop train when check error, or continue train without
    this train sample.
* use deque instead of vector in generators pool, make erase
  generator faster.
* Add chinese/english documentation
* Make should shuffle = false in unittest
* Add python files to depends.

* Need PyGuard when set args for calc_batch_size

ISSUE=4592951

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1447 1ad973e4-5ce8-4261-8a94-b56d1f490c56

Also add some comments for cost layers.
ISSUE=4580653

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1410 1ad973e4-5ce8-4261-8a94-b56d1f490c56

ISSUE=4586495

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1408 1ad973e4-5ce8-4261-8a94-b56d1f490c56