Torch utilities for training neural networks in gravitational wave physics applications.
Please visit our documentation page to see descriptions and examples of the functions and modules available in ml4gw.
We also have an interactive Jupyter notebook that demonstrates much of the core functionality available in the examples directory.
You can install ml4gw with pip:
pip install ml4gwTo build with a specific version of PyTorch/CUDA, please see the PyTorch installation instructions here to see how to specify the desired torch version and --extra-index-url flag. For example, to install with torch 2.5.1 and CUDA 11.8 support, you would run
pip install ml4gw torch==2.5.1--extra-index-url=https://download.pytorch.org/whl/cu118ml4gw is also fully compatible with use in Poetry, with your pyproject.toml set up like
[tool.poetry.dependencies]
python = "^3.9" # python versions 3.9-3.12 are supported
ml4gw = "^0.6"To build against a specific PyTorch/CUDA combination, consult the PyTorch installation documentation above and specify the extra-index-url via the tool.poetry.source table in your pyproject.toml. For example, to build against CUDA 11.6, you would do something like:
[tool.poetry.dependencies]
python = "^3.9"
ml4gw = "^0.6"
torch = {version = "^2.0", source = "torch"}
[[tool.poetry.source]]
name = "torch"
url = "https://download.pytorch.org/whl/cu118"
priority = "explicit"If you come across errors in the code, have difficulties using this software, or simply find that the current version doesn't cover your use case, please file an issue on our GitHub page, and we'll be happy to offer support. We encourage users who encounter these difficulties to file issues on GitHub, and we'll be happy to offer support to extend our coverage to new or improved functionality. We also strongly encourage ML users in the GW physics space to try their hand at working on these issues and joining on as collaborators! For more information about how to get involved, feel free to reach out to [email protected]. By bringing in new users with new use cases, we hope to develop this library into a truly general-purpose tool that makes deep learning more accessible for gravitational wave physicists everywhere.
We are grateful for the support of the U.S. National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institute for Accelerating AI Algorithms for Data Driven Discovery (A3D3) under Cooperative Agreement No. PHY-2117997.