346d1fd9e2
Update internal master to public master - these changes have been approved and reviewed or are minor bugfixes Changes: * Add NCCL to source tree as module, add custom target to use NCCL build * Add Zlib to source tree * Recognize CPU intrinsics on Linux * Removed unsused code for SVD with unclear license * Fixed parameter swapping on single GPU * Compile with CUDA static libs * Add back aligned malloc for CPU tensors Related work items: #96098 |
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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:
- Fast multi-gpu training and translation
- Compatible with Nematus and DL4MT
- Efficient pure C++ implementation
- Permissive open source license (MIT)
- more details...
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The handwritten decoder for RNN models compatible with Marian and Nematus has been superseded by the Marian decoder.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), 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.