Installation

Relational Transformers requires Python 3.10 or newer.

Install with uv

uv add relational-transformers

Add deployment extras with uv add 'relational-transformers[onnx]' or uv add 'relational-transformers[triton]'.

Install with pip

pip install -U relational-transformers

PyTorch is the default backend and supports CPU, Apple MPS, and CUDA. Install deployment extras only where they are used:

pip install -U 'relational-transformers[triton]'
pip install -U 'relational-transformers[onnx]'
pip install -U 'relational-transformers[dev]'

Cell encoders are intentionally application-owned. The quickstart uses Sentence Transformers to reproduce the released RT-J embedding space, so its example environment also installs it explicitly:

pip install -U sentence-transformers

Install with Conda

Create an isolated environment with Conda, then install the package from PyPI:

conda create -n relational-transformers python=3.12
conda activate relational-transformers
python -m pip install -U relational-transformers

Install from Source

git clone https://github.com/RelativeDB/relational-transformers
cd relational-transformers
python -m pip install .

Editable Install

For development, install the checkout with test and documentation dependencies:

git clone https://github.com/RelativeDB/relational-transformers
cd relational-transformers
python -m pip install -e '.[dev]'
pytest

Install PyTorch with CUDA support

Install the PyTorch build matching the CUDA runtime on the deployment host, then install relational-transformers[triton]. Follow the current command from PyTorch’s installation selector rather than pinning a CUDA wheel URL in application code.

Model weights download from Hugging Face on first use and remain in its normal local cache. A local directory with config.json and model.safetensors uses the same constructor and never accesses the network.