Infrastructure/software/catalogue

From Nordic Language Processing Laboratory
(Difference between revisions)
Jump to: navigation, search
(Activity C: Data-Driven Parsing)
(Activity C: Data-Driven Parsing)
Line 92: Line 92:
 
! Module Name/Version !! Description !! System !! Install Date !! Maintainer
 
! Module Name/Version !! Description !! System !! Install Date !! Maintainer
 
|-
 
|-
| [http://wiki.nlpl.eu/index.php/Parsing/uuparser] || Uppsala Parser || Abel || December 2018 ||  
+
| [http://wiki.nlpl.eu/index.php/Parsing/uuparser nlpl-uuparser] || Uppsala Parser || Abel || December 2018 ||  
 
|-
 
|-
| http://wiki.nlpl.eu/index.php/Parsing/udpipe || UDPipe 1.2 with Pre-Trained Models      || Taito, Abel || November 2017 ||
+
| [http://wiki.nlpl.eu/index.php/Parsing/udpipe nlpl-udpipe] || UDPipe 1.2 with Pre-Trained Models      || Taito, Abel || November 2017 ||
 
|}
 
|}
  

Revision as of 18:34, 3 January 2019

Contents

Background

This page provides a high-level summary of NLPL-specific software installed on either of our two systems. As a rule of thumb, NLPL aims to build on generic software installations provided by the system maintainers (e.g. development tools and libraries that are not discipline-specific), using the modules infrastructure. For example, an environment like OpenNMT is unlikely to be used by other disciplines, and NLPL stands to gain from in-house, shared expertise that comes with maintaining a project-specific installation. On the other hand, the CUDA libraries are general extensions to the operating system that most users of deep learning frameworks on gpus will want to use; hence, CUDA is most appropriately installed by the core system maintainers. Frameworks like PyTorch and TensorFlow, arguably, present a middle ground to this rule of thumb: In principle, they are not discipline-specific, but in mid-2018 at least the demand for installations of these frameworks is strong within NLPL, and the project will likely benefit from growing its competencies in this area.

Module Catalogue

The discipline-specific modules maintained by NLPL are not activated by default. To make available the NLPL directory of module configurations, on top of the pre-configured, system-wide modules, one needs to:

module use -a /proj*/nlpl/software/modulefiles/

We will at times assume a shell variable $NLPLROOT that points to the top-level project directory, i.e. /projects/nlpl/ (on Abel) or /proj/nlpl/ (on Taito). For NLPL users, we recommend that one adds the above module use command to the shell start-up script, e.g. .bashrc in the user home directory.

Activity A: Basic Infrastructure

Interoperability of NLPL installations with each other, as well as with system-wide software that is maintained by the core operations teams for Abel and Taito, is no small challenge; neither is parallelism across the two systems, for example in available software (and versions) and techniques for ‘mixing and matching’. These challenges are discussed in some more detail with regard to the Python programming environment and with regard to common Deep Learning frameworks.

Module Name/Version Description System Install Date Maintainer
nlpl-cython/0.29.1 C Extensions for Python Abel December 2018 Stephan Oepen
nlpl-nltk/3.3 Natural Language Toolkit (NLTK) Abel, Taito September 2018 Stephan Oepen
nlpl-pytorch/0.4.1 PyTorch Deep Learning Framework (CPU and GPU) Abel, Taito September 2018 Stephan Oepen
nlpl-spacy/2.0.12 spaCy: Natural Language Processing in Python Abel, Taito October 2018 Stephan Oepen
nlpl-tensorflow/1.11 TensorFlow Deep Learning Framework (CPU and GPU) Abel, Taito September 2018 Stephan Oepen

Activity B: Statistical and Neural Machine Translation

Module Name/Version Description System Install Date Maintainer
nlpl-moses/mmt-mvp-v0.12.1-2739-gdc42bcb Moses SMT system, including GIZA++, MGIZA, fast_align Taito July 2017 Yves Scherrer
nlpl-moses/4.0-65c75ff Moses SMT System Release 4.0, including GIZA++, MGIZA, fast_align, SALM
Some minor fixes added to existing install 2/2018.
Should not break compatibility except when using tokenizer.perl for Finnish or Swedish.
Taito, Abel November 2017 Yves Scherrer
nlpl-efmaral/0.1_2017_07_20 efmaral and eflomal word alignment tools Taito July 2017 Yves Scherrer
nlpl-efmaral/0.1_2017_11_24 efmaral and eflomal word alignment tools Taito, Abel November 2017 Yves Scherrer
nlpl-efmaral/0.1_2018_12_13/17 efmaral and eflomal word alignment tools Taito, Abel December 2018 Yves Scherrer
nlpl-hnmt/1.0.1 HNMT neural machine translation system Taito March 2018 Yves Scherrer
nlpl-opennmt-py/0.2.1 OpenNMT Python Library Abel, Taito September 2018 Stephan Oepen
nlpl-marian/1.2.0 Marian neural machine translation system Taito March 2018 Yves Scherrer
marian/1.5 Marian neural machine translation system Taito June 2018 CSC staff
nlpl-mttools/2018_12_23 A collection of preprocessing and evaluation script for machine translation Taito, Abel December 2018 Yves Scherrer

Activity C: Data-Driven Parsing

Module Name/Version Description System Install Date Maintainer
nlpl-uuparser Uppsala Parser Abel December 2018
nlpl-udpipe UDPipe 1.2 with Pre-Trained Models Taito, Abel November 2017

Activity E: Pre-Trained Word Embeddings

Module Name/Version Description System Install Date Maintainer
nlpl-gensim/3.6.0 GenSim: Topic Modeling for Humans Taito, Abel October 2018 Stephan Oepen

Activity G: OPUS Parallel Corpus

Module Name/Version Description System Install Date Maintainer
nlpl-cwb/3.4.12 Corpus Work Bench (CWB) Taito, Abel November 2017
nlpl-opus/0.1 Various OPUS Tools Taito, Abel November 2017
nlpl-uplug/0.3.8dev UPlug Parallel Corpus Tools Taito, Abel November 2017
Personal tools
Namespaces

Variants
Actions
Navigation
Tools