Install

Recursively clone repo

We attach our fork of the Depthcharge package as a submodule. Therefore, this repository needs to be recursively cloned:

git clone --recursive git@github.com:statisticalbiotechnology/pairwise.git

cd pairwise

Install environment (Conda)

1. Create environment:

   conda env create -f environment.yml

   conda activate pairwise_env

2. Install local Depthcharge

   conda activate pairwise_env

   cd depthcharge

   python -m pip install .

Framework/structure

We utilize Pytorch Lightning. We define the models in src/models/, and wrap them with Lightning modules that contain the training code. The training wrappers are found in src/pl_wrappers.py.

A number of callbacks such as for annealing the LR and temperature are found in src/pl_callbacks.py. They can be toggeled by providing the corresponding argument, i.e. --anneal_lr=True.

Logging

We use WandB for detailed logging.

Either sign in to your account wandb login and set the arguments

python src/main.py --config=<your-config> --wandb_project=<your-project> --wandb_entity=<your-entity>

or run the main script src/main.py directly and select:

• option "(1) Private W&B dashboard, no account required" (requires internet connection) or
• option "(4) Don’t visualize my results" (offline)

python src/main.py --config=<your-config>

Train

Example command:

python src/main.py --config=configs/example.yaml

Example sbatch script for multi-node/multi-gpu training

#!/bin/bash
#SBATCH --gpus 64
#SBATCH -t 3-00:00:00
#SBATCH --tasks-per-node=8
#SBATCH --cpus-per-task=16

module load Anaconda/2021.05-nsc1
conda activate pairwise_env
export OMP_NUM_THREADS=16

srun python src/main.py --config=configs/example.yaml \
--num_devices=8 --num_nodes=8 --matmul_precision="medium"

Submit job

sbatch job.sbatch

Arguments, configs and priority

The full list of arguments and their descriptions can be found in src/parse_args.py. These arguments are the same as the arguments in the config files. You can adjust the configs files or provide the arguments from the command line.

Priority: Provided command line args > config values > argparse defaults

Datasets

TODO

Reproducing 9 species cross validation results

1. Download checkpoint for specific species and 9 species version (1 or 2)
2. Checkout to branch "9_species_cross_validation"
3. Run script roc_curve.py with following command: python roc_curve.py path_to_checkpoint species_name output_name_extension
   • Output is a roc curve in a two column csv file