Recommended Reading

We use a lot of external libraries and tools in Undertale - if you’re not already pretty familiar with the following, it’s worth reading through their documentation and possibly completing their tutorial(s) before contributing.

dask

The parallel computing library that supports distributed computing via SLURM (among others). We use dask for all of our data processing pipelines.

Pandera

The DataFrame schema validation library. We use Pandera to validate datasets and enforce common schema across processing stages.

PyTorch

The deep learning library. If you’re not already deeply familiar with PyTorch, the textbook Deep Learning with Pytorch is an excellent resource.

PyTorch Lightning

All of our models are written in PyTorch and wrapped in Lightning modules. We largely let Lightning handle the complexities of multi-node, multi-GPU training, validation, and integration with tensorboard for monitoring training.

Binary Ninja

Binary Ninja is our disassembler/decompiler of choice. It’s fast and has a robust Python API. Most of our dataset pipelines require an active Binary Ninja license.

Tensorboard

The visualization tool we use for tracking training runs.

Sphinx

A software documentation library. All of our documentation is written in reStructuredText and built with Sphinx. We also use autodoc with Google-style Python docstrings for automatically generated reference documentation.

pyinstrument

The statistical profiler for Python. We use this occasionally for performance testing.