Installation

Install from PyPI

Install the core edge2torch package from PyPI with:

pip install edge2torch

The core installation supports compiling sparsely-connected PyTorch models from edgelists, aligning named input features, customizing compiled models, and training with ordinary PyTorch.

edge2torch requires Python 3.10 or later. See the tested Python versions in the respective badge on top of the Home page.

Optional interpretation support

interpret_model() uses Captum and is installed as an optional dependency.

To install edge2torch with interpretation support:

pip install "edge2torch[interpret]"

Optional AnnData support

For optional AnnData input support:

pip install "edge2torch[anndata]"

To install both interpretation and AnnData support:

pip install "edge2torch[all]"

Development installation

To work on the package locally, clone the repository and install it in editable mode from the project root:

git clone git@github.com:Thomas-Rauter/edge2torch.git
cd edge2torch
pip install -e .

For optional interpretation support during development:

pip install -e ".[interpret]"

For optional AnnData support during development:

pip install -e ".[anndata]"

For both optional interpretation and AnnData support:

pip install -e ".[all]"

Optional dependency groups

Install development dependencies with:

pip install -e ".[dev]"

Install documentation dependencies with:

pip install -e ".[docs]"

Notebook and documentation note

Some documentation notebooks use optional visualization tools such as Graphviz. If a notebook requires Graphviz rendering, you may also need the system-level Graphviz installation in addition to the Python package.

For example, on Ubuntu or Debian:

sudo apt install graphviz

Verify the installation

A minimal core-installation smoke test is:

python -c "import edge2torch; print('edge2torch imported successfully')"

To verify interpretation support, install edge2torch[interpret] and run:

python -c "from edge2torch import interpret_model; print(interpret_model)"