CANARI-ML
Warning
This is a highly experimental codebase with constant changes with every development release, and is not ready for production use.
Canari-ML is a machine learning library built with PyTorch Lightning for wind forecasting (zonal wind at 700hPa) across the North Atlantic.
Canari-ML provides tools and models for processing environmental data and making wind forecast predictions. It is designed to be used in conjunction with the environmental-forecasting initiative which is used for data download and for majority of the pre-processing steps to prepare the source data for training and prediction.
- Models: Currently, a reference UNet model is implemented for wind forecasting.
- Preprocessing: Utilities for loading, reprojecting, preparing and caching ERA5 datasets for ML training.
- Integrated Experiment Tracking: Track experiments using either Tensorboard, or WandB integration.
- Prediction: Functions to train and predict on new data.
- Visualisation: Tools for visualising the results of predictions and model training.
To begin using Canari-ML:
- Installation:
pip install git+https://github.com/CANARI-ML/canari-ml@main- Usage: Run the following command to see available entry points:
canari_ml --helpCANARI-ML is licensed under the MIT license. See LICENSE for more information.
The latest documentation can be found on Read the Docs.
Contributions are welcome!
Please follow the Conventional Commits standard for commit messages. Any that do not follow this standard will not be merged into the main branch and may be rejected.
Please see CONTRIBUTING for more information on how to contribute.
CANARI-ML is a work in progress and will be updated as development progresses.