b29cc07a95
* Add MMAP as an option * Use io::isBin * Allow getYamlFromModel from an Item vector * ScorerWrapper can now load on to a graph from Item vector The interface IEncoderDecoder can now call graph loads directly from an Item Vector. * Translator loads model before creating scorers Scorers are created from an Item vector * Replace model-config try-catch with check using IsNull * Prefer empty vs size * load by items should be pure virtual * Stepwise forward load to encdec * nematus can load from items * amun can load from items * loadItems in TranslateService * Remove logging * Remove by filename scorer functions * Replace by filename createScorer * Explicitly provide default value for get model-mmap * CLI option for model-mmap only for translation and CPU compile * Ensure model-mmap option is CPU only * Remove move on temporary object * Reinstate log messages for model loading in Amun / Nematus * Add log messages for model loading in scorers Co-authored-by: Roman Grundkiewicz <rgrundkiewicz@gmail.com> |
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Marian
Marian is an efficient Neural Machine Translation framework written in pure C++ with minimal dependencies.
Named in honour of Marian Rejewski, a Polish mathematician and cryptologist.
Main features:
- Efficient pure C++ implementation
- Fast multi-GPU training and GPU/CPU translation
- State-of-the-art NMT architectures: deep RNN and transformer
- Permissive open source license (MIT)
- more detail...
If you use this, please cite:
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch (2018). Marian: Fast Neural Machine Translation in C++ (http://www.aclweb.org/anthology/P18-4020)
@InProceedings{mariannmt,
title = {Marian: Fast Neural Machine Translation in {C++}},
author = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
Neckermann, Tom and Seide, Frank and Germann, Ulrich and
Fikri Aji, Alham and Bogoychev, Nikolay and
Martins, Andr\'{e} F. T. and Birch, Alexandra},
booktitle = {Proceedings of ACL 2018, System Demonstrations},
pages = {116--121},
publisher = {Association for Computational Linguistics},
year = {2018},
month = {July},
address = {Melbourne, Australia},
url = {http://www.aclweb.org/anthology/P18-4020}
}
Amun
The handwritten decoder for RNN models compatible with Marian and Nematus has been superseded by the Marian decoder. The code is available in a separate repository: https://github.com/marian-nmt/amun
Website
More information on https://marian-nmt.github.io
Acknowledgements
The development of Marian received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreements 688139 (SUMMA; 2016-2019), 645487 (Modern MT; 2015-2017), 644333 (TraMOOC; 2015-2017), 644402 (HiML; 2015-2017), 825303 (Bergamot; 2019-2021), the Amazon Academic Research Awards program, the World Intellectual Property Organization, and is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract #FA8650-17-C-9117.
This software contains source code provided by NVIDIA Corporation.