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TensorFlow is one of the most widely used Deep Learning frameworks in NLP (in mid-2018, at least), with corporate support from Google.

Usage on Abel

The module nlpl-tensorflow provides a TensorFlow installation in a Python 3.5 virtual environment. Besides TensorFlow 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-tensorflow

Installation on Abel

module purge
module load gcc/4.9.2 cuda/9.0
module load python3/3.5.0
cd /projects/nlpl/software
mkdir tensorflow
virtualenv tensorflow/1.11

First things first: Enable use of our custom (more modern) GNU C Library installation, by wrapping the basic python binary:

mv /projects/nlpl/software/tensorflow/1.11/bin/{,.}python3.5
sed 's@pytorch/0.4.1@tensorflow/1.11@' \
  /projects/nlpl/software/pytorch/0.4.1/bin/python3.5 \
  > /projects/nlpl/software/tensorflow/1.11/bin/python3.5
chmod 755 /projects/nlpl/software/tensorflow/1.11/bin/python3.5

Next, create a module definition, in this case /projects/nlpl/software/modulefiles/nlpl-tensorflow/1.11.

module load nlpl-tensorflow/1.11
pip install --upgrade pip
pip install --upgrade $(pip list | tail -n +3 | awk '{print $1}')
pip install --upgrade -r /projects/nlpl/software/tensorflow/1.11/modules.txt
qlogin --account=nn9106k --time=1:00:00 --partition=accel --gres=gpu:1
cp -av /usr/lib64/* /usr/lib64/libnvidia* \
module purge
module use -a /projects/nlpl/software/modulefiles
module load nlpl-tensorflow
pip install --upgrade tensorflow-gpu
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