Separate shared runtime and benchmark responsibilities - #32
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RDBLearn keeps the v2 and v2.5 recipes it sweeps, TabPFN-Rel the v3 and hosted recipes, and both call the shared fit_tfm with a spec instead of a name wrapper. core.tfm imports torch only inside default_device. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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| from relarena.core.tfm import TFMSpec |
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Example imports removed default_device
Medium Severity
Removing the local fit_tfm wrapper also dropped the module-level default_device binding from relarena.models.tabpfn_rel.tfm. The documented caching example still imports default_device from that module, so it now fails immediately with ImportError before any cache comparison runs.
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Separate shared contracts, task sources, tuning, and estimator helpers under
relarena.core. Keep benchmark loading and final refit/evaluation in the host, and backend definitions with their models.Validation: Python 3.11/3.12 tests, pinned pre-commit checks, and wheel/sdist builds.
RDBLearn keeps only its v2 and v2.5 TabPFN recipes, TabPFN-Rel keeps v3 and the hosted backend, and both call the shared
fit_tfmwith a spec. Core imports torch only insidedefault_deviceand falls back to CPU without it.Stack created with GitHub Stacks CLI • Give Feedback 💬