Multi-Source Time Series Domain Adaptation

Steps:

• Convert to .tfrecord files for TensorFlow (./generate_tfrecords.sh)
• Train models (main.py or kamiak_train.srun)
• Evaluate models (main_eval.py or kamiak_eval.srun)
• Analyze results (analysis.py)

Installation

This requires the following packages (module load for Kamiak):

module load cuda/10.1.105 cudnn/7.6.4.38_cuda10.1 python3/3.7.4
pip install --user --upgrade pip
export PATH="$HOME/.local/bin:$PATH"
pip3 install --user --upgrade pip
pip3 install --user --upgrade numpy cython
pip3 install --user --upgrade tensorflow-gpu pillow lxml jupyter matplotlib pandas scikit-learn scipy tensorboard rarfile tqdm pyyaml grpcio absl-py

For the CPU-only jobs like kamiak_train_simple.srun:

module load python3/3.7.4
export PATH="$HOME/.local/bin:$PATH"
pip3 install --user --upgrade virtualenvwrapper
export VIRTUALENVWRAPPER_PYTHON="$(which python3)"
mkdir -p ~/Envs
export WORKON_HOME=~/Envs
source ~/.local/bin/virtualenvwrapper.sh
mkvirtualenv -p python3 tensorflow_cpu

which pip # check it's ~/Envs/tensorflow_cpu/bin/pip
which python3 # check it's ~/Envs/tensorflow_cpu/bin/python3

# Note: we don't use --user for virtual environments
pip install --upgrade numpy cython
# Note: here it's "tensorflow" not "tensorflow-gpu" -- the rest is the same.
pip install --upgrade tensorflow pillow lxml jupyter matplotlib pandas scikit-learn scipy tensorboard rarfile tqdm pyyaml grpcio absl-py

Training

sbatch -J train kamiak_train.srun adapt

Evaluating

sbatch -J eval kamiak_eval.srun adapt

Then look at the resulting results/results_*.txt file or analyze with analysis.py.