v1.8.0

Introducing MeshTensorFlow - this enables training really big models O(Billions) of parameters.

Models/Layers:

• Layers Added: NAC and NALU from https://arxiv.org/abs/1808.00508 Thanks @lukaszkaiser !
• Added a sparse graph neural net message passing layer to tensor2tensor.
• Targeted dropout added to ResNet. Thanks @aidangomez !
• Added VQA models in models/research/vqa_*
• Added Weight Normalization layer from https://arxiv.org/abs/1602.07868.

Datasets/Problems:

• MSCoCo paraphrase problem added by @tlatkowski - many thanks!
• VideoBairRobotPushingWithActions by @mbz !

Usability:

• Code cleaup in autoencoder, works both on image and text. Thanks @lukaszkaiser
• Set the default value of Text2TextProblem.max_subtoken_length to 200, this prevents very long vocabulary generation times. Thanks @afrozenator
• Add examples to distributed_training.md, update support for async training, and simplify run_std_server codepath. Thanks @rsepassi !
• Store variable scopes in T2TModel; add T2TModel.initialize_from_ckpt. Thanks @rsepassi !
• Undeprecate exporting the model from the trainer Thanks @gcampax !
• Doc fixes, thanks to @stefan-it :)
• Added t2t_prune: simple magnitude-based pruning script for T2T Thanks @aidangomez !
• Added task sampling support for more than two tasks. Thanks @urvashik !

Bug Fixes:

• Override serving_input_fn for video problems.
• StackWrapper eliminates problem with repeating actions. Thanks @blazejosinski !
• Calculated lengths of sequences using _raw in lstm.py
• Update universal_transformer_util.py to fix TypeError Thanks @zxqchat !

Testing:

• Serving tests re-enabled on Travis using Docker. Thanks @rsepassi !

Many more fixes, tests and work on RL, Glow, SAVP, Video and other models and problems.