Ensemble Learning Models (elm) is a set of tools for creating multiple unsupervised and supervised machine learning models and training them in parallel on datasets too large to fit into the RAM of a single machine, with a focus on applications in climate science, GIS, and satellite imagery.
Some reasons for using elm over scikit-learn alone:
- Parallelize ML pipelines across the cores of a single machine or compute cluster
- Use out-of-core ML algorithms to process large datasets which are too large to fit into RAM
- Analyze multidimensional climate data, extending beyond the limitations of two-dimensional arrays and matrices
- Read data from file formats popular to climate science and GIS, such as netCDF, HDF4, HDF5, Shapefiles, GeoJSON, and GeoTIFF
More use-cases can be found here.
elm is a Work in Progress¶
elm is immature and largely for experimental use.
The developers do not promise backwards compatibility with future versions.