Infrastructure/software/catalogue

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 the NLPL directory of module configurations, on top of pre-configured, system-wide modules, one needs to:

• Abel:

module use -a /projects/nlpl/software/modulefiles/

• Taito:

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

We will add 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

Module Name/Version	Description	System	Install Date	Maintainer
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-tensorflow/1.11	TensorFlow Deep Learning Framework (CPU and GPU)	Abel, Taito	September 2018	Stephan Oepen

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 with regard to common Deep Learning frameworks are discussed on a separate page.

Activity B: Statistical and Neural Machine Translation

Module Name/Version	Description	System	Install Date	Maintainer
moses/mmt-mvp-v0.12.1-2739-gdc42bcb	Moses SMT system, including GIZA++, MGIZA, fast_align	Taito	November 2017
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
efmaral/0.1_2017_07_20	efmaral and eflomal word alignment tools	Taito	July 2017
efmaral/0.1_2017_11_24	efmaral and eflomal word alignment tools	Taito, Abel	November 2017
nlpl-opennmt-py/0.2.1	OpenNMT Python Library	Abel, Taito	September 2018	Stephan Oepen

Activity C: Data-Driven Parsing

Module Name/Version	Description	System	Install Date	Maintainer
nlpl-uuparser/2.1	Uppsala Parser	Abel	December 2017
nlpl-udpipe/1.2.1-devel	UDPipe 1.2 with Pre-Trained Models	Taito, Abel	November 2017

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