Release XLearning 1.3

Major Features And Improvements

Support the lightLDA, see examples/lightLDA for use
Support the xflow, see examples/xflow for use
By submitting the configuration parameter to support the user-defined environment variable settings
Setting the last worker as estimator role of the distribute TensorFlow application if the user set the tf-evaluator as true, see examples/tfEstimators for use
Define the single worker index to save the output by set the output-index
Port reservation mechanism optimization
Local data container allocation priority mechanism
Display resource application and usage information
ps role function expansion: more convenient metrics use information rendering and output output upload

Container waits for the remaining machine port addresses to be stuck in the process due to the failure of the Container in distributed mode
After the worker applies, the number of redundant applications is released, and the remove request operation is added
Application failed due to excessive environment variables too long of the input in PLACEHOLDER mode
Job execution judgment failure condition control
The status code returns incorrectly when the Container successfully exits