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v0.15.1

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@w4nderlust w4nderlust released this 05 May 05:29
v0.15.1
5877a43

Bug fixes

  • Ray training 3.7x slowdown eliminated — LudwigProgressBar was calling rt.report() on every training batch when running inside Ray workers (~1.9 s/call through the Ray GCS). With hundreds of batches this completely dominated wall-clock time. Per-batch progress reporting is now suppressed; training metrics continue to be reported correctly at eval/checkpoint time. Ray overhead is now ~1.7x vs local (fixed TorchTrainer setup cost), down from 3.7x. (#4144)

  • GPU underutilization in Ray backend fixed — RayDatasetBatcher was running to_tensors via map_batches (spawning a Ray remote task per dataset block with scheduling overhead). It now runs locally in the producer thread. Also: datasets are now materialized before training to avoid re-reading Parquet from disk on every epoch. (#4144)

  • Python 3.14 compatibility — LudwigBaseConfig subclasses crashed with PydanticUserError: Field requires a type annotation on Python 3.14 because annotations are now stored lazily via __annotate_func__. Fixed in _LudwigModelMeta.__new__. (#4144)

  • ModernBERT tokenizer — Models containing "bert" in their name (e.g. answerdotai/ModernBERT-base) were incorrectly routed to BertTokenizer (WordPiece), causing Missing [UNK] token errors. ModernBERT now correctly uses HFTokenizer (AutoTokenizer / BPE). (#4144)

  • Dask meta= parameter — Multiple feature types (binary, category, sequence, timeseries, text, vector) called .map() without a meta= argument, causing ValueError: Metadata inference failed in map when using the Dask engine (backend: {type: ray, processor: {type: dask}}). All bare .map() calls are now fixed. (#4144)

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