diff --git a/LICENSE b/LICENSE index 8b665f991..9f2ef7087 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,7 @@ MIT License -Copyright (c) 2017 AmuNMT +Copyright (c) 2017 Marcin Junczys-Dowmunt, the University of Edinburgh, Adam +Mickiewicz University, the World Intellectual Property Organization Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 339b27f55..7387180c8 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# AmuNMT Website +# Marian Website The website is build with Jekyll - a static site generator. The content is created and updated on branch `jekyll`, then the static pages @@ -48,4 +48,4 @@ message. | Tag | Description | | --- | --- | | `[Text](/permalink/)` | An active link to another subpage of the website identified by its permalink. | -| `{% github_link / %}` | An active link to a file/directory `` in the given repository, i.e. `http://amunmt.github.io/amunmt//tree/master/`. | +| `{% github_link / %}` | An active link to a file/directory `` in the given repository, i.e. `http://marian-nmt.github.io/marian//tree/master/`. | diff --git a/features.md b/features.md index fdabdf291..7a32370c6 100644 --- a/features.md +++ b/features.md @@ -57,20 +57,20 @@ the [IWSLT paper](http://workshop2016.iwslt.org/downloads/IWSLT_2016_paper_4.pdf We ran our experiments on an Intel Xeon E5-2620 2.40GHz server with four NVIDIA GeForce GTX 1080 GPUs.We present the words-per-second ratio for our NMT models -using AmuNMT and Nematus, executed on the CPU and GPU. For the CPU version we +using Marian and Nematus, executed on the CPU and GPU. For the CPU version we use 16 threads, translating one sentence per thread. We restrict the number of OpenBLAS threads to 1 per main Nematus thread. For the GPU version of Nematus we use 5 processes to maximize GPU saturation. As a baseline, the phrase-based model reaches 455 words per second using 16 threads. The CPU-bound execution of Nematus reaches 47 words per second while the -GPU-bound achieved 270 words per second. In similar settings, CPU-bound AmuNMT +GPU-bound achieved 270 words per second. In similar settings, CPU-bound Marian is three times faster than Nematus CPU, but three times slower than Moses. With vocabulary selection (systems with asteriks) we can nearly double the speed of -AmuNMT CPU. The GPU-executed version of AmuNMT is more than three times faster +Marian CPU. The GPU-executed version of Marian is more than three times faster than Nematus and nearly twice as fast as Moses, achieving 865 words per second, with vocabulary selection we reach 1,192. Even the speed of the CPU version -would already allow to replace a Moses-based SMT system with an AmuNMT-based +would already allow to replace a Moses-based SMT system with an Marian-based NMT system in a production environment without severely affecting translation throughput. @@ -88,8 +88,8 @@ graph. ### Training speed in words per second -We also compare training speed between a number of popular toolkits and AmuNMT. -As AmuNMT is still early work, we expect speed to improve with future optimizations. +We also compare training speed between a number of popular toolkits and Marian. +As Marian is still early work, we expect speed to improve with future optimizations.
Training speed #1 @@ -105,7 +105,7 @@ on German-English WMT data. ### Multi-GPU training -AmuNMT's training framework provides multi-GPU training via asynchronous SGD and +Marian's training framework provides multi-GPU training via asynchronous SGD and data parallelism (copies of the full model on each GPU). We benchmarked the [Romanian-English example](/examples/training/) on a machine with 8 NVIDIA GTX 1080 GPUs. Training speed increases with each GPU instance, but currently diff --git a/index.md b/index.md index 8bcd2ab04..616290d31 100644 --- a/index.md +++ b/index.md @@ -30,7 +30,7 @@ permalink: /

- + Download from GitHub diff --git a/publications.md b/publications.md index 346b70008..c6aebbd65 100644 --- a/publications.md +++ b/publications.md @@ -9,7 +9,7 @@ menu: 6 ## Citation Please cite the following [IWSLT paper](http://workshop2016.iwslt.org/downloads/IWSLT_2016_paper_4.pdf) -if you use AmuNMT or Marian in your research: +if you use Marian (formerly AmuNMT) in your research: ```tex @InProceedings{junczys2016neural, @@ -24,7 +24,7 @@ if you use AmuNMT or Marian in your research: } ``` -## Work using AmuNMT +## Work using Marian/AmuNMT {% bibliography %} diff --git a/quickstart.md b/quickstart.md index 69964a8e6..4412a8de2 100644 --- a/quickstart.md +++ b/quickstart.md @@ -43,28 +43,28 @@ Tested on different machines and distributions: Clone a fresh copy from github: - git clone https://github.com/amunmt/amunmt + git clone https://github.com/marian-nmt/marian The project is a standard CMake out-of-source build: - cd amunmt + cd marian mkdir build cd build cmake .. make -j If run for the first time, this will also download Marian -- the training -framework for AmuNMT. +framework for Marian. -## Running AmuNMT +## Running Marian ### Training -Marian is the training framework of AmuNMT. Assuming `corpus.en` and `corpus.ro` are +Marian is the training framework of Marian. Assuming `corpus.en` and `corpus.ro` are corresponding and preprocessed files of a English-Romanian parallel corpus, the following command will create a Nematus-compatible neural machine translation model. - ./amunmt/build/marian \ + ./marian/build/marian \ --train-sets corpus.en corpus.ro \ --vocabs vocab.en vocab.ro \ --model model.npz @@ -77,7 +77,7 @@ a WMT-grade model. If a trained model is available, run: - ./amunmt/build/amun -m model.npz -s vocab.en -t vocab.ro <<< "This is a test ." + ./marian/build/amun -m model.npz -s vocab.en -t vocab.ro <<< "This is a test ." See the [documentation](/docs/#amun) for a full list of command line options or the [examples](/examples/translating) for a full example of how to use