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feat(data): improve offline data prep with validation, tutorials, image util tests - #1875
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…ge util tests Contributors can now prepare and check datasets on CPU before arranging GPU resources, and the image helpers gain CPU-only regression coverage. Key changes: - Add python -m areal.dataset.validate with text-rl and tokenized-sft profiles, --split/--require-field/--max-length/--max-errors options, deterministic JSON reports, and 0/1/2 exit codes - Extend EN/ZH dataset customization guides with a runnable offline custom-dataset tutorial wired to the new validator, incl. DatasetDict split selection and unavailable-split errors - Add tests/test_image_utils.py covering base64 encoding and max-size padding behavior with synthetic Pillow images - Document existing image2base64/pad_images_batch_to_max_size behavior via docstrings without changing signatures - Add offline unit tests and subprocess CLI integration tests for the validator (skipped when the full import chain is unavailable) Refs: areal-project#1695
DoubleCore
requested review from
Le8r0nJames,
fishcrap,
garrett4wade and
sitabulaixizawaluduo
as code owners
November 3, 2026 12:56
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|
Closing this PR myself: it duplicates wesssI's #1852, which implements the same offline data validation CLI and custom-dataset walkthrough. To avoid wasting maintainer review time on duplicate work, I'm closing this one. |
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Implements #1695.