MT MODULE IN TRAMOOC PROJECT
The TraMOOC MT Server provides the following functionality:
- Docker files that facilitate the installation of Marian and all requirements, the download and configuration of the TraMOOC translation models, and the launch of a server that serves translations via HTTP.
- an API that receives and serves requests over HTTP in the XML format.
- language-specific pre- and postprocessing, including tokenization, truecasing, and subword segmentation.
- support for segment-level override patterns for text that should not be translated (such as URLs or programming language source code).
- support for a TMX translation memory.
maintainer: Roman Grundkiewicz rgrundki@staffmail.ed.ac.uk
version: 3
on Ubuntu 16.04, the server can be installed natively.
-
install required Ubuntu packages (see Dockerfile for list)
- if you don't use docker, you might install CUDA and CUDNN manually; choose version compatible with the Docker file, i.e. CUDA 8.0 and CUDNN-dev 5.1.10, which is downloadable from here
-
install required python packages with pip:
pip install -r requirements.txt --user
-
install MarianNMT:
make marian
on other Linux systems, the server can be deployed via a Docker container.
- install Docker: https://docs.docker.com/engine/installation/
- install nvidia-docker: https://github.com/NVIDIA/nvidia-docker/wiki
- execute
make build
If you want to download model, do:
make models
you can run the local server as follows (for English-German):
./docker-entrypoint.py en-de
you can run the server in a docker container as follows:
nvidia-docker run --rm -p 8080:8080 -v model:/model tramooc/mt_server en-de
a single server can also support multiple languages:
nvidia-docker run --rm -p 8080:8080 -v model:/model tramooc/mt_server en-de en-ru
you can also specify GPU devices which should be used by the server for each language pair; for example, to use GPU with ID 0 and 1 for en-de, and only GPU 1 for en-ru, you should type:
nvidia-docker run --rm -p 8080:8080 -v model:/model tramooc/mt_server en-de:0,1 en-ru:1
If you want to run more than one instance of the server, specify ports for subprocessors:
nvidia-docker run --rm -p 8080:8080 -v model:/model tramooc/mt_server en-de --subproc-port 60000
See ./docker-entrypoint.py --help for other options, which can be also passed
to nvidia-docker (at the end of the command line options).
A simple sample client is provided by sample-client.py. sample-client-2.py
allows the translation of text passed via standard input.
The code in this repository is released under the FreeBSD License.
By default, the tool downloads and uses pre-trained models for 11 language pairs (see below). These models are released for research purposes only.
- en-bg (English-Bulgarian)
- en-cs (English-Czech)
- en-de (English-German)
- en-el (English-Greek)
- en-hr (English-Croatian)
- en-it (English-Italian)
- en-nl (English-Dutch)
- en-pl (English-Polish)
- en-pt (English-Portuguese)
- en-ru (English-Russian)
- en-zh (English-Chinese)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 644333 (TraMOOC).