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Moved from mlkit, as agreed for DEV-161: extensions of third-party
frameworks for digital pathology belong in ratiopath (like train_test_split
for scikit-learn). The callback adds on_{stage}_dataloader_start/end hooks
for validation, test and predict. Lightning is an optional extra
(ratiopath[lightning]); the logic is unchanged.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Documentation PreviewPreview URL: https://RationAI.github.io/ratiopath/claude-multiloader-lifecycle/ |
matejpekar
approved these changes
Oct 15, 2026
Adames4
approved these changes
Oct 15, 2026
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Moves
MultiloaderLifecyclefrom mlkit into ratiopath, as agreed at the 8 Oct repository meeting (DEV-161): extensions of third-party frameworks for digital pathology belong in ratiopath, the waytrain_test_splitextends scikit-learn. Here it is PyTorch Lightning.Changes
ratiopath/lightning/callbacks/multiloader_lifecycle.py: the callback addson_{stage}_dataloader_start/_endhooks forvalidation,testandpredict. The logic is copied fromrationai.mlkit.lightning.callbacksunchanged (diffed). The empty hook stubs got docstrings, and the class got a Google-style docstring with an example.pip install ratiopath[lightning](lightning>=2.4.0). The base install doesn't change. Lightning is also in thetestsgroup, so the new tests run in CI.tests/test_multiloader_lifecycle.py: hook order across dataloader changes for all three stages, state reset between epochs, and an epoch with no batches.Checks (local)
pytest: 381 passed (5 new).mypy .: clean.ruff check/ruff format --check(0.17.0): clean.mkdocs build --strict: passes.uv.lockonly adds lightning and its dependencies (torchmetrics, pytorch-lightning, aiohttp, …), nothing upgraded.Follow-up and questions
carcinoma-binary-classification-methodsandstain-normalizationuse it), and then drops the copy.ratiopath.lightning.callbacksthe namespace you want, or would you rather have something underratiopath.torch?🤖 Generated with Claude Code