Difference between revisions of "Infrastructure/software/nltk"
(→Usage on Abel) |
(→Usage on Abel) |
||
Line 31: | Line 31: | ||
<pre> | <pre> | ||
module load nlpl-nltk nlpl-tensorflow | module load nlpl-nltk nlpl-tensorflow | ||
+ | </pre> | ||
+ | |||
+ | Because PyTorch and TensorFlow are ‘special’ in their requirements for | ||
+ | dynamic libraries and support for both cpu and gpu nodes, it is important | ||
+ | for them to be activated last, i.e. on the ‘top’ of a multi-module stack. | ||
+ | This can be validated by inspecting which <tt>python</tt> binary is active | ||
+ | according to the search order in the <tt>$PATH</tt> environment variable: | ||
+ | <pre> | ||
+ | type -all python | ||
+ | </pre> | ||
+ | |||
+ | In late September 2018, for example, the output from the above command | ||
+ | would look as follows: | ||
+ | <pre> | ||
+ | python is /projects/nlpl/software/tensorflow/1.11/bin/python | ||
+ | python is /projects/nlpl/software/nltk/3.3/bin/python | ||
+ | python is /usr/bin/python | ||
+ | python is /opt/rocks/bin/python | ||
</pre> | </pre> | ||
Revision as of 18:48, 29 September 2018
Background
The Natural Language Toolkit (NLTK) provides a large collection of core NLP utilities (e.g. sentence splitting and tokenization, part of speech tagging, various approaches to parsing, and many more) in an integrated Python environment. The NLTK distribution also bundles a broad range of common, freely available data sets, which are made accessible through a uniform API. Albeit often neither quite state of the art nor blindingly efficient, NLTK is popular as a teaching environment and go-to repository of common ‘basic’ preprocessing tasks, e.g. sentence splitting, stop word removal, or lemmatization (for English, at least).
Usage on Abel
The module nlpl-nltk provides an NLTK installation in a Python 3.5 virtual environment.
module purge module use -a /projects/nlpl/software/modulefiles module load nlpl-nltk
This installation (just as other NLPL-maintained Python virtual environments) can be combined with other Python-based modules, for example the NLPL installations of PyTorch or TensorFlow. To ‘stack’ multiple Python environments, they can simply be loaded together, e.g.
module load nlpl-nltk nlpl-tensorflow
Because PyTorch and TensorFlow are ‘special’ in their requirements for dynamic libraries and support for both cpu and gpu nodes, it is important for them to be activated last, i.e. on the ‘top’ of a multi-module stack. This can be validated by inspecting which python binary is active according to the search order in the $PATH environment variable:
type -all python
In late September 2018, for example, the output from the above command would look as follows:
python is /projects/nlpl/software/tensorflow/1.11/bin/python python is /projects/nlpl/software/nltk/3.3/bin/python python is /usr/bin/python python is /opt/rocks/bin/python
Available Versions
Installation on Abel
module purge module load python3/3.5.0
mkdir /projects/nlpl/software/nltk virtualenv /projects/nlpl/software/nltk/3.3
Next, we need to create a module definition, in this case /projects/nlpl/software/modulefiles/nlpl-nltk/3.3; make sure to establish the environment variable $NLTK_DATA, pointing to the data sub-directory of the NLTK tree, as established by the command-line data download below.
module load nlpl-nltk/3.3 pip install --upgrade pip pip install --upgrade $(pip list | tail -n +3 | gawk '{print $1}')
Finally, install the NLTK code and all data packages.
pip install nltk python -m nltk.downloader -d /projects/nlpl/software/nltk/3.3/data all