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@JasonKChow JasonKChow released this 15 Nov 02:00
· 14 commits to release since this release
v0.5.0
874f219

New feature release:

  • GPU support for GPClassificationModel and GPRegressionModel alongside GPU support for generating points with OptimizeAcqfGenerator with any acquisition function.
    • Models that are subclasses of GPClassificationModel and GPRegressionModel should also have GPU support.
    • This should allow the use of the better acquisition functions while maintaining practical live active learning trial generation speeds.
    • GPU support will also speed up post-hoc analysis when fitting on a lot of data. Models have a model.device attribute like tensors in PyTorch do and can be smoothly moved between devices using the same API (e.g., model.cuda() or model.cpu() as tensors.
    • We wrote a document on speeding up AEPsych, especially for live experiments with active learning: https://aepsych.org/docs/speed.
    • More models and generators will gain GPU support soon.
  • New parameter configuration format and parameter transformations
    • The settings for parameters should now be set in parameter-specific blocks, old configs will still work but will not support new parameter features going forward.
    • We added a log scale transformation and the ability to disable the normalize scale transformation, these can be set at a parameter-specific level.
    • Take a look at our documentation about the new parameter options: https://aepsych.org/docs/parameters
    • More parameter transforms to come!

Please raise an issue if you find any bugs with the new features or if you have any feature requests that would help you run your next experiment using AEPsych.

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