Getting Started#
ml4xcube is a comprehensive Python-based toolkit designed for researchers and developers in the field of machine learning with an emphasis on xarray data cubes. This toolkit is engineered to provide specialized and robust support for data cube management and analysis, operating with the state-of-the-art machine learning libraries (1) scikit-learn, (2) PyTorch and (3) TensorFlow.
Installation#
Get started with ml4xcube effortlessly by using the newest stable xcube kernel in DeepESDL (e.g. xcube-1.11.0).
When working with custom team environment,
add ml4xcube to the list of dependencies.
Features#
- Data preprocessing and postprocessing functions
- Filling masked data and gap filling features
- Dataset creation and train-/ test splitting techniques
- Trainer classes for
sklearn,TensorFlowandPyTorch - Distributed training framework compatible with
PyTorch - chunk utilities for working with data cubes
Requirements#
| Package | Versions |
|---|---|
| dask | ≥2023.2.0 |
| numpy | ≥1.24 |
| pandas | ≥2.2 |
| scikit-learn | >1.3.1 |
| xarray | >2023.8.0 |
| zarr | >2.11 |
| rechunker | ≥0.5.1 |
Make sure you have Python version 3.8 or higher.
If you're planning to use ml4xcube with TensorFlow or PyTorch, set up these frameworks properly in your conda environment.