In mid-2018 at least, PyTorch is one of the widely used programming frameworks for Deep Learning (with corporate backing by facebook); in and of itself PyTorch is not NLP-specific, but it is a critical dependency for several of the NLPL strands of activity.
Usage on Abel
The module nlpl-pytorch provides a PyTorch installation in a Python 3.5 virtual environment. Besides PyTorch and its dependencies (e.g. NumPy), the virtual environment includes a selection of popular add-on packages, e.g. SciKit-Learn, the Python Data Analysis Library (Pandas), GenSim, and Keras. This installation should support both cpu and gpu nodes on Abel.
module purge module use -a /projects/nlpl/software/modulefiles module load nlpl-pytorch
There is a short sample program that test availability of cpu vs. gpu computing devices.
On Abel, as of September 2018, PyTorch 0.4.1 is available.
Installation on Abel
module purge module load cuda/8.0 module load python3/3.5.0
cd /projects/nlpl/software mkdir pytorch virtualenv pytorch/0.4.1
Next, we need to create a module definition, in this case /projects/nlpl/software/modulefiles/nlpl-pytorch/0.4.1.
module load nlpl-pytorch/0.4.1 pip install --upgrade pip pip install --upgrade numpy pillow six pip install torch torchvision
wget -O /tmp/requirements.txt \ https://raw.githubusercontent.com/OpenNMT/OpenNMT-py/master/requirements.txt pip install -r /tmp/requirements.txt