Multimodal learning using the JUMP dataset

Description

This repository contains code used to launch Deep Learning experiments trained on the JUMP dataset.

🛠️ Installation

This project uses conda to create a virtual environment with some main dependencies (Python, CUDA, Poetry), then uses Poetry to install the rest of the dependencies via pip.

# clone project
git clone https://github.com/gwatkinson/jump_models
cd jump_models

# create conda environment and install dependencies
conda create -n jump_models -f conda-linux-64.lock          # for linux
# conda create -n jump_models -f conda-windows-64.lock      # for windows

# activate conda environment
conda activate jump_models

# install other dependencies and current project with poetry
poetry install

# install pre-commit hooks if you want
pre-commit install
# pre-commit run -a # run all hooks on all files

How to run

Train model with default configuration

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name.yaml

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 data.batch_size=64

TODO

Look at DMPNNFeaturizer.