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131 lines
4.8 KiB
Markdown
131 lines
4.8 KiB
Markdown
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# AmuNMT
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[](https://gitter.im/amunmt/amunmt?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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[](http://vali.inf.ed.ac.uk/jenkins/job/amunmt_compilation_cuda/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/amunmt_compilation_cpu/)
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A C++ inference engine for Neural Machine Translation (NMT) models trained with Theano-based scripts from
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Nematus (https://github.com/rsennrich/nematus) or DL4MT (https://github.com/nyu-dl/dl4mt-tutorial)
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If you use this, please cite:
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Marcin Junczys-Dowmunt, Tomasz Dwojak, Hieu Hoang (2016). Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions (https://arxiv.org/abs/1610.01108)
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## Recommended for GPU version:
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Tested on Ubuntu 14.04 LTS
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* CMake 3.5.1 (due to CUDA related bugs in earlier versions)
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* GCC/G++ 4.9
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* Boost 1.54
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* CUDA 7.5
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Tested on Ubuntu 16.04 LTS
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* CMake 3.5.1 (due to CUDA related bugs in earlier versions)
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* GCC/G++ 5.4
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* Boost 1.61
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* CUDA 8.0
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Also compiles the CPU version.
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## Recommended for CPU version:
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The CPU-only version will automatically be compiled if CUDA cannot be detected by CMAKE. Tested on different machines and distributions:
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* CMake 3.5.1
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* The CPU version should be a lot more forgiving concerning GCC/G++ or Boost versions.
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## Compilation
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The project is a standard Cmake out-of-source build:
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mkdir build
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cd build
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cmake ..
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make -j
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If you want to compile only CPU version on a machine with CUDA, add `-DCUDA=OFF` flag:
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cmake -DCUDA=OFF ..
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## Vocabulary files
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Vocabulary files (and all other config files) in AmuNMT are by default YAML files. AmuNMT also reads gzipped yml.gz files.
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* Vocabulary files from models trained with Nematus can be used directly as JSON is a proper subset of YAML.
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* Vocabularies for models trained with DL4MT (\*.pkl extension) need to be converted to JSON/YAML with either of the two scripts below:
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```
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python scripts/pkl2json.py vocab.en.pkl > vocab.json
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python scripts/pkl2yaml.py vocab.en.pkl > vocab.yml
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```
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## Running AmuNMT
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./bin/amun -c config.yml <<< "This is a test ."
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## Configuration files
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An example configuration:
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# Paths are relative to config file location
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relative-paths: yes
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# performance settings
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beam-size: 12
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devices: [0]
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normalize: yes
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gpu-threads: 1
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# scorer configuration
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scorers:
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F0:
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path: model.en-de.npz
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type: Nematus
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# scorer weights
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weights:
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F0: 1.0
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# vocabularies
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source-vocab: vocab.en.yml.gz
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target-vocab: vocab.de.yml.gz
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## BPE Support
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AmuNMT has integrated support for [BPE encoding](https://github.com/rsennrich/subword-nmt). There are two option `bpe` and `debpe`. The `bpe` option receives a path to a file with BPE codes (here `bpe.codes`). To turn on desegmentation on the ouput, set `debpe` to `true`, e.g.
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bpe: bpe.codes
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debpe: true
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## Python Bindings
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Python bindings allow to run AmuNMT decoder in python scripts. The compilation of the bindings requires `python-dev` package. To compile the bindings run:
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```
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make python
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```
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The Python bindings consist of 2 function: `init` and `translate`:
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```python
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import libamunmt
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libamunmt.init('-c config.yml')
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print libamunmt.translate(['this is a little test .'])
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```
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The `init` function init the decoder and the syntax is the same as in command line. The `translate`
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function takes a list of sentences to translate. For real-world example, see the `scripts/amunmt_erver.py`
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script, which uses python bindings to run REST server.
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## Using GPU/CPU threads
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AmuNMT can use GPUs, CPUs, or both, to distribute translation of different sentences. **However, it is unlikely that CPUs used together with GPUs yield any performance improvement. It is probably better to only use the GPU if one or more are available.**
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cpu-threads: 8
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gpu-threads: 2
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devices: [0, 1]
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The setting above uses 8 CPU threads and 4 GPU threads (2 GPUs x 2 threads). The `gpu-threads` and `devices` options are only available when AmuNMT has been compiled with CUDA support. Multiple GPU threads can be used to increase GPU saturation, but will likely not result in a large performance boost. By default, `gpu-threads` is set to `1` and `cpu-threads` to `0` if CUDA is available. Otherwise `cpu-threads` is set to `1`. To disable the GPU set `gpu-threads` to `0`. Setting both `gpu-threads` and `cpu-threads` to `0` will result in an exception.
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## Example usage
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* [Data and systems for our winning system in the WMT 2016 Shared Task on Automatic Post-Editing](https://github.com/emjotde/amunmt/wiki/AmuNMT-for-Automatic-Post-Editing)
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