Get Started#
Installation#
Clone the library source code:
git clone https://github.com/teocala/pihnn.git
and move to the library main folder.
Note
Before the installation, it is recommended to create an Anaconda environment:
conda env create -f environment.yml # the file 'environment.yml' already provides all dependencies
conda activate pihnn-env # 'pihnn-env' is the name of the newly created environment
Finally, run
pip install .
Warning
Make sure the name and version of the library are correctly detected during installation. There are currently some issues with some versions of pip and linux (e.g., here)
Run a test#
The examples
folder contains some tests that can be easily run from command line.
For example,
python3 examples/simply_connected/laplace.py
will run the test on the Laplace equation and provide plots in the results
folder.
Using CUDA#
The library supports CUDA processing. If your system is CUDA-capable, the code will automatically run on the GPU.
Citation#
If you use PIHNNs for your research, don’t forget to cite our works in Publications.