In this tutorial you will be guided through the installation of PyCA, a small library that streamlines the implementation of interactive artificial life models.
If you are comfortable with git , you can simply clone the repository with git clone <https://github.com/frotaur/pyca> .
If not, navigate to the PyCA github repo and download the full code of the project, and unzip it :
Open the project in your favorite IDE. At this point, it is recommended to install uv not to have to deal with python virtual environments. To do so, please follow info at this link .
Once uv is install, simply open a terminal in the PyCA repository folder, and run uv sync . You should get a bunch of things getting installed, and when it ends, pyca is ready to be used!
ONLY FOR WINDOWS PC WITH GPUS: If you are running windows, and have a NVIDIA GPU, run the following command before proceeding : uv pip install torch torchvision --index-url <https://download.pytorch.org/whl/cu128> --upgrade. If you get cuda-related crashes afterwards, visit https://pytorch.org/ and try installing another version of torch , using the uv pip install command.
NOTE : it is technically possible to run torch with AMD gpus, but the code was untested in this case, so you are on your own !
To test that everything works correct, run uv run simulate.py -d cpu . It could take some time to launch the first time, but a window should open, looking similar to this (the screenshots are from an older version):
If you have an Nvidia GPU, you can test with uv run simulate.py -d cuda . This should open the same window if everything works correctly.
Note : with Macs, you can try uv run simulate.py -d mps to use mac’s gpu acceleration. Everything should work, but some automata might behave differently, an you might experience some crashes. If this happens, use -d cpu instead.