Infrastructure/software/catalogue
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 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-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 | September 2018 | Stephan Oepen |
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 | 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 |