@inproceedings{
lovering2021predictinginductive,
title={Predicting Inductive Biases of Fine-tuned Models},
author={Charles Lovering and Rohan Jha and Tal Linzen and Ellie Pavlick},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=mNtmhaDkAr}
}
python -m venv predicting-venv # make a new venv
source predicting-venv/bin/activate
pip install -r requirements_gpu.txt # or requirements.txt if you don't have a gpu
python -m space download en_core_weg_lg
source setup.sh
Keeping this here only for reference, in case things break
This project was tested (and updated) to run on v100s with pytorch 1.12.1+cu102 and transformers 4.23.1 using slurm. We saved the full details of the environment in requirements_2022.txt.
# Create new env.
interact -q gpu-he -m 128GB -g 1 -t 12:00:00 -f v100
module load python/3.7.4 cuda/11.7.1 gcc/10.2
python -m venv predicting-venv
source predicting-venv/bin/activate
pip install --upgrade pip
pip install torch torchvision pytest tqdm pandas gputil spacy[cuda102] transformers pytorch_lightning inflect sklearn wandb nltk plac torchmetrics sentencepiece
pip install plac --upgrade
python -m spacy download en_core_web_lg
bash setup.sh
wget https://nlp.stanford.edu/data/glove.6B.zip
mv glove.6B.zip data/glove/glove.6B.zip
cd data/glove/
unzip glove.6B.300d.zip
cd ../..
Set wandb
subscription key in your ~/.bash_profile
.
# This is not the real key. (I set mine up in my .bashrc.)
export WANDB_API_KEY=628318530717958647692528
Generate experiments & run!
# generate datasets
./setup.sh
sbatch datasets.sh
pytest test.py
# generate jobs
bash slim_pipeline.sh # smaller set of settings
# full set of jobs (~day+ of gpu compute)
bash pipeline.sh
Install requirements.
# Create new env.
conda create --name features python=3.8
conda activate features
# Install pytorch.
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
# Install further reqs.
pip install torch torchvision pytest tqdm pandas gputil spacy[cuda102] transformers pytorch_lightning pyinflect sklearn wandb nltk plac
pip install plac --upgrade
python -m spacy download en_core_web_lg
Set wandb
subscription key in your ~/.bash_profile
.
# This is not the real key.
export WANDB_API_KEY=628318530717958647692528
Generate experiments & run!
# generate datasets
./setup.sh
# approx <30 min
sbatch datasets.sh
pytest test.py
# generate jobs
python job.py --experiment finetune
python job.py --experiment probing
# run jobs
sbatch jobs/[DATE]/jobs.sh
If you have issues with plac
(e.g. plac.opt
is not defined) reinstall it with pip install plac --upgrade
.
If you have issues with cupy
uninstall (pip uninstall cupy-cuda102
) and then re-install (pip install cupy-cuda102
).
Let us (@cjlovering, @rohjha) know if you have any questions.