Sockeye
This package contains the Sockeye project, an open-source sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet (Incubating). Sockeye powers several Machine Translation use cases, including Amazon Translate. The framework implements state-of-the-art machine translation models with Transformers (Vaswani et al, 2017). Recent developments and changes are tracked in our CHANGELOG.
If you have any questions or discover problems, please file an issue. You can also send questions to sockeye-dev-at-amazon-dot-com.
Version 2.0
With version 2.0, we have updated the usage of MXNet by moving to the Gluon API and adding support for several state-of-the-art features such as distributed training, low-precision training and decoding, as well as easier debugging of neural network architectures. In the context of this rewrite, we also trimmed down the large feature set of version 1.18.x to concentrate on the most important types of models and features, to provide a maintainable framework that is suitable for fast prototyping, research, and production. We welcome Pull Requests if you would like to help with adding back features when needed.
Installation
The easiest way to run Sockeye is with Docker or nvidia-docker.
To build a Sockeye image with all features enabled, run the build script:
python3 sockeye_contrib/docker/build.py
See the Dockerfile documentation for more information.
Documentation
For information on how to use Sockeye, please visit our documentation.
- For a quickstart guide to training a large data WMT model, see the WMT 2018 German-English tutorial.
- Developers may be interested in our developer guidelines.
