Skip to main content

Library for training deep learning models with environmental focus

Project description

#

Python library to train neural networks with a strong focus on hydrological applications.

This package has been used extensively in research over the last years and was used in various academic publications. The core idea of this package is modularity in all places to allow easy integration of new datasets, new model architectures or any training-related aspects (e.g. loss functions, optimizer, regularization). One of the core concepts of this code base are configuration files, which let anyone train neural networks without touching the code itself. The NeuralHydrology package is built on top of the deep learning framework PyTorch, since it has proven to be the most flexible and useful for research purposes.

We (the AI for Earth Science group at the Institute for Machine Learning, Johannes Kepler University, Linz, Austria) are using this code in our day-to-day research and will continue to integrate our new research findings into this public repository.

Cite NeuralHydrology

In case you use NeuralHydrology in your research or work, it would be highly appreciated if you include a reference to our JOSS paper in any kind of publication.

@article{kratzert2022joss,
  title = {NeuralHydrology --- A Python library for Deep Learning research in hydrology},
  author = {Frederik Kratzert and Martin Gauch and Grey Nearing and Daniel Klotz},
  journal = {Journal of Open Source Software},
  publisher = {The Open Journal},
  year = {2022},
  volume = {7},
  number = {71},
  pages = {4050},
  doi = {10.21105/joss.04050},
  url = {https://doi.org/10.21105/joss.04050},
}

Contact

For questions or comments regarding the usage of this repository, please use the discussion section on Github. For bug reports and feature requests, please open an issue on GitHub. In special cases, you can also reach out to us by email: neuralhydrology(at)googlegroups.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neuralhydrology-1.13.0.tar.gz (146.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

neuralhydrology-1.13.0-py3-none-any.whl (194.6 kB view details)

Uploaded Python 3

File details

Details for the file neuralhydrology-1.13.0.tar.gz.

File metadata

  • Download URL: neuralhydrology-1.13.0.tar.gz
  • Upload date:
  • Size: 146.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for neuralhydrology-1.13.0.tar.gz
Algorithm Hash digest
SHA256 f5e6d9c5062f68546d90cea63384dcb34632a09afa86b5c06d5557d9bc8c6b44
MD5 fcd4e1e41d3a17fbc6a570f33c0b1c6d
BLAKE2b-256 1022189e477c57fba841e5c1d260cac38e05ce2bc2c5faa6144012e4d69642f6

See more details on using hashes here.

File details

Details for the file neuralhydrology-1.13.0-py3-none-any.whl.

File metadata

File hashes

Hashes for neuralhydrology-1.13.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ada0b80d26691d21e273b8f66b49ae2d865ae83d0ebd77af2ee9a145b91655e6
MD5 a68cc0a58dc246412d27cbb46a118073
BLAKE2b-256 7bfbc587fc001ab03dc3e8accc19486671a90329b0cc22c44a2d59309457d184

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page