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.

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

• For information on how to use Sockeye, please visit our documentation.
• Developers may be interested in our developer guidelines.

Older versions

• Sockeye 3.0, based on PyTorch & MXNet 2.x is available in the sockeye_30 branch.
• Sockeye 2.x, based on the MXNet Gluon API, is available in the sockeye_2 branch.
• Sockeye 1.x, based on the MXNet Module API, is available in the sockeye_1 branch.

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

• Zhang, Xuan, Kevin Duh, Paul McNamee. "A Hyperparameter Optimization Toolkit for Neural Machine Translation Research". Proceedings of ACL (2023).

2022

• Currey, Anna, Maria Nădejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu. "MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation". Proceedings of EMNLP (2022).
• Domhan, Tobias, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne and Felix Hieber. "The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation". Proceedings of NAACL-HLT (2022)
• Fischer, Lukas, Patricia Scheurer, Raphael Schwitter, Martin Volk. "Machine Translation of 16th Century Letters from Latin to German". Workshop on Language Technologies for Historical and Ancient Languages (2022).
• Knowles, Rebecca, Patrick Littell. "Translation Memories as Baselines for Low-Resource Machine Translation". Proceedings of LREC (2022)
• McNamee, Paul, Kevin Duh. "The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text". Proceedings of LREC (2022)
• Nadejde Maria, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, Georgiana Dinu. "CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality". Proceedings of NAACL (2022).
• Weller-Di Marco, Marion, Matthias Huck, Alexander Fraser. "Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies ". arXiv preprint arXiv:2203.13550 (2022)

2021

• Bergmanis, Toms, Mārcis Pinnis. "Facilitating Terminology Translation with Target Lemma Annotations". arXiv preprint arXiv:2101.10035 (2021)
• Briakou, Eleftheria, Marine Carpuat. "Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation". arXiv preprint arXiv:2105.15087 (2021)
• Hasler, Eva, Tobias Domhan, Sony Trenous, Ke Tran, Bill Byrne, Felix Hieber. "Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation". Proceedings of EMNLP (2021)
• Tang, Gongbo, Philipp Rönchen, Rico Sennrich, Joakim Nivre. "Revisiting Negation in Neural Machine Translation". Transactions of the Association for Computation Linguistics 9 (2021)
• Vu, Thuy, Alessandro Moschitti. "Machine Translation Customization via Automatic Training Data Selection from the Web". arXiv preprint arXiv:2102.1024 (2021)
• Xu, Weijia, Marine Carpuat. "EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints." Transactions of the Association for Computation Linguistics 9 (2021)
• Müller, Mathias, Rico Sennrich. "Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation". Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021)
• Popović, Maja, Alberto Poncelas. "On Machine Translation of User Reviews." Proceedings of RANLP (2021)
• Popović, Maja. "On nature and causes of observed MT errors." Proceedings of the 18th MT Summit (Volume 1: Research Track) (2021)
• Jain, Nishtha, Maja Popović, Declan Groves, Eva Vanmassenhove. "Generating Gender Augmented Data for NLP." Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021)
• Vilar, David, Marcello Federico. "A Statistical Extension of Byte-Pair Encoding." Proceedings of IWSLT (2021)

2020

• Dinu, Georgiana, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan. "Joint translation and unit conversion for end-to-end localization." Proceedings of IWSLT (2020)
• Exel, Miriam, Bianka Buschbeck, Lauritz Brandt, Simona Doneva. "Terminology-Constrained Neural Machine Translation at SAP". Proceedings of EAMT (2020).
• Hisamoto, Sorami, Ma