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Sockeye

Sockeye has entered maintenance mode and is no longer adding new features. We are grateful to everyone who has contributed to Sockeye throughout its development with pull requests, issue reports, and more.

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Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. Recent developments and changes are tracked in our CHANGELOG.

For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial.

For questions and issue reports, please file an issue on GitHub.

Version 3.1.x: PyTorch only

With version 3.1.x, we remove support for MXNet 2.x. Models trained with PyTorch and Sockeye 3.0.x remain compatible with Sockeye 3.1.x. Models trained with 2.3.x (using MXNet) and converted to PyTorch with Sockeye 3.0.x's conversion tool can NOT be used with Sockeye 3.1.x.

Version 3.0.0: Concurrent PyTorch and MXNet support

Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet.

All models trained with 2.3.x (using MXNet) can be converted to models running with PyTorch using the converter CLI (sockeye.mx_to_pt). This will create a PyTorch parameter file (<model>/params.best) and backup the existing MXNet parameter file to <model>/params.best.mx. Note that this only applies to fully-trained models that are to be used for inference. Continued training of an MXNet model with PyTorch is not supported (because we do not convert training and optimizer states). sockeye.mx_to_pt requires MXNet to be installed into the environment.

All CLIs of Version 3.0.0 now use PyTorch by default, e.g. sockeye-{train,translate,score}. MXNet-based CLIs/modules are still operational and accessible via sockeye-{train,translate,score}-mx.

Sockeye 3 can be installed and run without MXNet, but if installed, an extended test suite is executed to ensure equivalence between PyTorch and MXNet models. Note that running Sockeye 3.0.0 with MXNet requires MXNet 2.x to be installed (pip install --pre -f https://dist.mxnet.io/python 'mxnet>=2.0.0b2021')

Installation

Download the current version of Sockeye:

git clone https://github.com/awslabs/sockeye.git

Install the sockeye module and its dependencies:

cd sockeye && pip3 install --editable .

For faster GPU training, install NVIDIA Apex. NVIDIA also provides PyTorch Docker containers that include Apex.

Documentation

Older versions

Citation

For more information about Sockeye, see our papers (BibTeX).

Sockeye 3.x

Felix Hieber, Michael Denkowski, Tobias Domhan, Barbara Darques Barros, Celina Dong Ye, Xing Niu, Cuong Hoang, Ke Tran, Benjamin Hsu, Maria Nadejde, Surafel Lakew, Prashant Mathur, Anna Currey, Marcello Federico. Sockeye 3: Fast Neural Machine Translation with PyTorch. ArXiv e-prints.

Sockeye 2.x

Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, Kenneth Heafield. The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (AMTA'20).

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar. Sockeye 2: A Toolkit for Neural Machine Translation. Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, Project Track (EAMT'20).

Sockeye 1.x

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post. The Sockeye Neural Machine Translation Toolkit at AMTA 2018. Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (AMTA'18).

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post. 2017. Sockeye: A Toolkit for Neural Machine Translation. ArXiv e-prints.

Research with Sockeye

Sockeye has been used for both academic and industrial research. A list of known publications that use Sockeye is shown below. If you know more, please let us know or submit a pull request (last updated: May 2022).

2023

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2017