MultimodalAI'25 Hackathon Base Source Code

Overview

This repository provides the base source code for the MultimodalAI'25 workshop Hackathon. It is designed to help participants get started quickly with a pre-configured Python environment and essential dependencies for development and testing.

Requirements

• Python 3.10, 3.11, or 3.12
• Git

Installation

The steps below are linear and work with venv, conda, or uv. Pick one method and follow it end‑to‑end.

1) Clone and create an environment

git clone https://github.com/pykale/mmai-hackathon.git
cd mmai-hackathon

# conda (recommended)
conda create -n mmai-hackathon python=3.11 -y
conda activate mmai-hackathon


# venv (alternative)
# python3 -m venv .venv && source .venv/bin/activate

# uv (alternative)
# uv venv .venv && source .venv/bin/activate

2) Install dependencies (with tests)

# Recommended for development and testing (includes pytest, coverage, linters)
pip install -e .[dev]

# If you only need runtime dependencies (not recommended for contributors):
# pip install -e .

By default, the dependencies declared in pyproject.toml are sufficient (they include torch and torch-geometric).

Only if you enable a data‑loading extension that requires extra PyG ops (e.g., torch-scatter, torch-sparse, torch-cluster, torch-spline-conv), do you need to install those extras. The snippet below detects your Torch/CUDA and prints the correct wheel index to use for these optional packages:

# Inspect Torch / CUDA (optional)
python - <<'PYINFO'
import torch
print('Torch:', torch.__version__)
print('CUDA version:', torch.version.cuda)
print('CUDA available:', torch.cuda.is_available())
PYINFO

# Compute the PyG wheel index matching your Torch/CUDA
PYG_INDEX=$(python - <<'PYG'
import torch
torch_ver = torch.__version__.split('+')[0]
cuda = torch.version.cuda
if cuda:
    cu_tag = f"cu{cuda.replace('.', '')}"
else:
    cu_tag = 'cpu'
print(f"https://data.pyg.org/whl/torch-{torch_ver}+{cu_tag}.html")
PYG
)
echo "Using PyG wheel index: $PYG_INDEX"

# Install only the extras you need, for example:
# pip install torch-scatter -f "$PYG_INDEX"
# pip install torch-sparse  -f "$PYG_INDEX"
# pip install torch-cluster -f "$PYG_INDEX"
# pip install torch-spline-conv -f "$PYG_INDEX"

More details: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html

3) (Optional) Pre‑commit hooks

pre-commit install

4) Run tests

pytest

Example data

Example data can be downloaded from this Dropbox folder. No Dropbox account is required to access the files.

Notes

• The project restricts Python versions to 3.10–3.12 as specified in .python-version and pyproject.toml.
• For more information about the dependencies, see pyproject.toml.

Tip: Integration tests optionally use real data. In CI, datasets are downloaded with python -m tests.dropbox_download "/MMAI25Hackathon" "MMAI25Hackathon" --unzip when a Dropbox token is configured.

Authors

• Shuo Zhou ([email protected])
• Xianyuan Liu ([email protected])
• Wenrui Fan ([email protected])
• Mohammod N. I. Suvon ([email protected])
• L. M. Riza Rizky ([email protected])