  We present PyTorch Frame, a PyTorch-based framework for deep learning over
multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by
providing a PyTorch-based data structure to handle complex tabular data,
introducing a model abstraction to enable modular implementation of tabular
models, and allowing external foundation models to be incorporated to handle
complex columns (e.g., LLMs for text columns). We demonstrate the usefulness of
PyTorch Frame by implementing diverse tabular models in a modular way,
successfully applying these models to complex multi-modal tabular data, and
integrating our framework with PyTorch Geometric, a PyTorch library for Graph
Neural Networks (GNNs), to perform end-to-end learning over relational
databases.
