tsl (Torch Spatiotemporal) is a library built to accelerate research on neural spatiotemporal data processing methods, with a focus on Graph Neural Networks.
tsl
is built on several libraries of the Python scientific computing ecosystem, with the final objective of providing a straightforward process that goes from data preprocessing to model prototyping.
In particular, tsl
offers a wide range of utilities to develop neural networks in PyTorch for processing spatiotemporal data signals.
tsl
is compatible with Python>=3.7. We recommend installation from source to be up-to-date with the latest version:
git clone https://github.com/TorchSpatiotemporal/tsl.git
cd tsl
python setup.py install # Or 'pip install .'
To solve all dependencies, we recommend using Anaconda and the provided environment configuration by running the command:
conda env create -f tsl_env.yml
Alternatively, you can install the library from pip:
pip install torch-spatiotemporal
Please refer to PyG installation guidelines for installation of PyG ecosystem without conda.
The best way to start using tsl
is by following the tutorial notebook in examples/notebooks/a_gentle_introduction_to_tsl.ipynb
.
The documentation is hosted on readthedocs. For local access, you can build it from the docs
directory.