v0.13.0

Major Changes:

• Per [add] modelExporters by @amkrajewski, 3 model exporter classes have been added to pySIPFENN in the modelExporters module:
  • ONNXExporter allowing (1) exporting back to ONNX after models were adjusted to new datasets or new properties (transfer learning), (2) automated simplification of model architecture for improved performance, and (3) adjusting model internal precision to FP16 to reduce its size by half with only minor performance impact.
  • TorchExporter to export models to PyTorch which are used internally by pySIPFENN.
  • CoreMLExporter to export models to Apple's CoreML format developed for use in their devices, where it provides the most seamless integration with existing apps and can harvest very efficient Neural Engine hardware acceleration. At the same time, it can be used on other platforms as well, such as Linux or Windows. Note that by default, models will be converted to FP16 precision, similar to one of the ONNXExporter options.

The above changes are a step in our effort to make advanced use of pySIPFENN easier for end-users. Next items on the roadmap include (1) automation of transfer learning on small datasets and (2) OPTIMADE integrations, which will make pySIPFENN API a one-stop solution for model fine-tuning.

Minor Changes:

• Small improvements in the Calculator object printout with the verbose option (true by default) added to its initialization.
• Matching tests were added to the CI pipelines.
• dev extra dependencies have been established, alongside appropriate documentation, for all future dependencies that are not required for core pySIPFENN functionalities to keep it as light as possible while not limiting advanced users. One of the future directions will be to make core dependencies lighter.
• Model exports and motivations were added to the documentation under Exporting pySIPFENN Models page.

Full Changelog: v0.12.2...v0.13.0