Sockeye

Added
- LHUC is now supported in transformer models

Added
- \[Experimental\] Introducing the image captioning module. Type of models supported: ConvNet encoder - Sockeye NMT decoders. This includes also a feature extraction script,
an image-text iterator that loads features, training and inference pipelines and a visualization script that loads images and captions.
See [this tutorial](tutorials/image_captioning) for its usage. This module is experimental therefore its maintenance is not fully guaranteed.

Changed
- Updated to MXNet 1.2
- Use of the new LayerNormalization operator to save GPU memory.

Fixed
- Removed summation of gradient arrays when logging gradients.
This clogged the memory on the primary GPU device over time when many checkpoints were done.
Gradient histograms are now logged to Tensorboard separated by device.

Added
- Added decoding with target-side lexical constraints (documentation in `tutorials/constraints`).

Added
- Introduced Sockeye Autopilot for single-command end-to-end system building.
See the [Autopilot documentation]((https://github.com/awslabs/sockeye/tree/master/contrib/autopilot)) and run with: `sockeye-autopilot`.
Autopilot is a `contrib` module with its own tests that are run periodically.
It is not included in the comprehensive tests run for every commit.